Technical Collection of Concept Notes on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction

10 June 2016

The United Nations Office for Disaster Risk Reduction

1. Purpose The purpose of this document is to support discussion by Member States on the selection of indicators to monitor achievement of the global targets of the Sendai Framework for Disaster Risk Reduction 2015-2030. This document has been produced by the UN Office of Disaster Risk Reduction and responds to the request for additional information in respect of the indicators for Targets A to E and G by Members of the Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Relating to Disaster Risk Reduction (OEIWG) in its Second Session in Geneva on the 10 and 11 February, 2016, The document provides technical suggestions and considerations of the Secretariat in the form of updated Concept Notes by Target, which detail the technical requirements for the indicators, list the proposed indicators under deliberation, describe applicable definitions and terminology, provide commentary, technical considerations and recommendations of the Secretariat, present possible computation methodologies and discuss critical issues.

2. Background The document draws principally from the Working Text on Indicators, and employs the terminology described in the current Working Text on Terminology, both of which were based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016 and reissued with factual corrections on 24 March 2016. Applicable definitions and terminology in this document will be updated in line with sessional and inter-sessional deliberations of the OEIWG on terminology. This document builds on, and provides updates to previous technical submissions by the Secretariat1, including but not restricted to: ▫ Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction ▫ Concept note on Methodology to Estimate Direct Economic Losses from Hazardous Events to Measure the Achievement of Target C ▫ Concept Note on Methodology to Estimate progress of National and Local DRR Strategy to Measure the Achievement of Target E ▫ Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction ▫ Background Paper - Indicators to Monitor Global Targets of the Sendai Framework for Disaster Risk Reduction 2015-2030: A Technical Review

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http://www.preventionweb.net/drr-framework/open-ended-working-group/technical-papers

1

This document introduces the approach employed by the Inter-agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs) to analyse the proposed indicators by a) the level of methodological development, and b) overall data availability. The proposed indicators are grouped into three categories: 1. Indicators for which a methodology exists, or has been proposed, and for which data are already widely available in a significant number of countries (Category I); 2. Indicators for which a methodology exists, or has been proposed, but for which data are not easily available (Category II); 3. Indicators for which a methodology has not yet been developed nor is data easily available (Category III). Member States may wish to consider Category I indicators as core requirements for global monitoring; some of which may be relevant to countries wishing to measure the degree to which national policy objectives are being met. Category II indicators could be considered as those indicators to be developed in the short to medium term – potentially to be used initially for measurement at the national level with the view to migrate to Category I over time to support global monitoring. Category III indicators could be considered those that require long term development, or present insurmountable methodological and/or data availability challenges and should not be measured. On the basis of this categorisation, and other technical considerations, the Secretariat provides recommendations as to which of the proposed indicators may be: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

This document is informed by, and in turn informs, the deliberations of the Inter-agency Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs), and the UN Statistical Commission (UNSC) on the global monitoring framework for the 2030 Agenda for Sustainable Development. At its 47th Session, the UN Statistical Commission2 expressed its support for the report of the IAEG-SDGs and Note by the Secretary-General3, and agreed with the proposed global indicator framework for the goals and targets of the 2030 Agenda for Sustainable Development as reflected in the list of indicators presented in annex IV of the report. Annex IV of this report – the Final list of proposed Sustainable Development Goal indicators – includes five indicators currently being deliberated on by the OEIWG, relating to the five Sendai Framework Targets A to E. The report makes explicit reference to the establishment of the OEIWG by the General Assembly, and identifies that the five SDGs indicators will eventually reflect the recommendations of the working group.

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E/2016/24- E/CN.3/2016/34 E/CN.3/2016/2/Rev.1*

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Contents Target A: Concept note on Methodology to Estimate Global Disaster Mortality to Measure the Achievement of Target A of the Sendai Framework for Disaster Risk Reduction: A Technical Review 4 Target B: Concept note on Methodology to Estimate the Number of Affected People to Measure the Achievement of Target B of the Sendai Framework for Disaster Risk Reduction: A Technical Review 16 Target C: Concept note on Methodology to Estimate Direct Economic Losses from Hazardous Events to Measure the Achievement of Target C of the Sendai Framework for Disaster Risk Reduction: A Technical Review 36 Target D: Concept note on Methodology to Estimate Damages to Infrastructure and Interruptions to Basic Services to Measure the Achievement of Target D of the Sendai Framework for Disaster Risk Reduction: A Technical Review 106 Target E: Concept note on Methodology to Estimate the Progress of National and Local DRR Strategy to Measure the Achievement of Target E of the Sendai Framework for Disaster Risk Reduction: A Technical Review 131 Target G: Concept note on Methodology to Estimate the Availability of and Access to MultiHazard Early Warning Systems and Disaster Risk Information and Assessments to Measure the Achievement of Target G of the Sendai Framework for Disaster Risk Reduction: A Technical Guidance 159

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Concept note on Methodology to Estimate Global Disaster Mortality to Measure the Achievement of Target A of the Sendai Framework for Disaster Risk Reduction: A Technical Review

10 June 2016

The United Nations Office for Disaster Risk Reduction

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1. Overview This document outlines a methodology to estimate global disaster mortality associated with hazardous events. The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OEIWG) requested the UNISDR to propose a methodology at the first and the second sessions, held in Geneva on 29-30 September 2015 and 10-11 February 2016. The purpose of this document is to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of the global target A of the Sendai Framework for Disaster Risk Reduction 2015-2030. Target A: Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared to 2005-2015 The methodology described here is based on previous experience of a number of governments, academic and research institutions, private organizations and work of the United Nations in more than 89 countries supporting the building of Disaster Loss Databases. This methodology proposes the collection and use of simple and uniform physical indicators of mortality (number of people) as the point of departure for computation. The methodology proposed by the Secretariat is based on previous work made by a number of groups that have engaged in the collection of similar data. In particular, it is based on the work of: ▫ the UNISDR, UNDP and other organisations supporting the development of national loss databases (which uses the DesInventar tool in the majority of cases), ▫ other independent national disaster database constructors which cover additional countries, such as databases in the USA (SHELDUS), Australia, Canada, Spain, France and other European countries. ▫ the EMDAT, MunichRe and SwissRe international disaster datasets ▫ the recommendations to EU member states on loss data collection issued by the Joint Research Centre of the European Commission, JRC, and ▫ the conclusions of IRDR DATA group.

2. Summary In this paper the Secretariat addresses important aspects of data collection which Member States should consider if a tightly defined methodology to measure mortality is to be determined. Analysis of studies and the experiences of the large number of data providers has shown that disaster mortality has been assessed and reported by different actors using slightly different but still very similar approaches. Unlike other loss indicators, such as economic loss, the degree of coherence and consistency of the figures provided by all sources are high. Variations in the uniformity of approach manifest in relatively minor inconsistencies in global disaster mortality data that are currently reported by both national and international data providers. However, where these estimates exist, it is possible to identify which elements of mortality were taken into account. 5

The elements and reporting thresholds used by Member States can differ, but in general the methodology and definitions employed remain consistent over time. The Global Assessment Report on Disaster Risk Reduction (GAR) 2015 showed that differences in reported mortality were, in the worst cases, less than 15%, and that the majority of variations in mortality were usually a function of differences in the reporting thresholds of some databases. EMDAT database, for example, by definition does not record data for disasters failing to meet certain conditions (for instance, a minimum of 10 deaths, 100 affected or declaration of an international emergency). Another source of variation also occurs due to the fact that some disaster loss databases do not take into account Missing / Presumed dead, and only count certified deaths (as is the case of EMDAT). The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

No.

B-1

Indicator

Recommended - for measurement at the global level

Methodology

Data

Y

Y

Y

Y

Y Y

Y Y

A-3

Number of deaths and missing persons / presumed dead due to hazardous events per 100,000. Number of deaths due to hazardous events. Number of missing persons / presumed dead due to hazardous events.

B-1

Recommended - for measurement at the national level

Y

Y

B-1

Not Recommended

Y

Y

A-1 alt.

[Number of deaths, missing, injured, displaced or [evacuated] due to hazardous events per 100,000.]

Y

Y

A-1 A-2

N/A

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3. Technical Requirements for an indicator to measure “global disaster mortality” Indicators proposed to measure targets in this and other frameworks are numbers that give an indication of the size of certain phenomena4, in this case it estimates the total mortality associated with disasters. It is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure of mortality losses. It would be impossible to remove a certain level of uncertainty or inaccuracy from mortality loss estimations, for which the sourcing of data is subject to the legal procedures and timeframe criteria of a specific country, as well as the exhaustiveness of data collection. In this sense, the mortality estimated is always an approximate value (a “proxy”). However, it should be noted that the experience of many disaster loss data providers demonstrate that this indicator is in general very robust and consistent across different data sources. The indicators to measure human losses for the Sendai Framework aim to meet following important criteria: Consistent over time: The target requires the comparison of losses of two different decades, the decade of the Hyogo framework (2005-2015) and the last decade of the Sendai Framework (20202030). Monitoring the losses, nevertheless, should occur throughout the entire period of 25 years in order to obtain a continuous view of the progress of implementation and achievements, and data must be recorded and reported in a consistent way for the cycle of measurement, and so avoid introducing biases. Consistent across countries: It must be a) applicable to any country in the world, to the maximum degree possible, allowing comparison among countries or regions, and b) feasible, such that data can be obtained regardless of the level of development or income of each country. SMART: Specific, Measurable, Achievable, Relevant, Time Bound. Reliable: Results can be trusted and if possible have a measure of dispersion, and for which a particular uncertainty measure can be determined. Transparent: The methodology used is well known, with caveats declared, and for which weaknesses, limitations and strengths, including economic assessment biases, are identified. Verifiable: The estimated mortality value can be traced back to the original legal documents that support mortality claims. Feasible: Easy to collect data in a practical and realistic way, without imposing an extraordinary or even impossible burden to countries. Taking advantage of existing data: Many countries have already collected standardized data. Taking advantage of this fact is more practical than having everyone start from zero. 4

http://www.oxforddictionaries.com/es/definicion/learner/indicator

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Useful: Results can be used not only for measuring the achievement of targets but also for DRR strategy planning, awareness raising, risk assessments and the development of DRR and related policies.

4. List of Proposed Indicators to Measure Target A: The following indicators are taken from the Working Text on Indicators, based on negotiations during the Second Session of the Open-ended Inter-Governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. The following are the indicators needed to measure global mortality in this guideline: [A-1 Number of [deaths / deceased] and [missing [persons] / presumed dead] due to hazardous events per 100,000.] (This indicator should be computed based on indicators A-2 and A-3, with population figures) [A-2 - Number of [deaths / deceased] due to hazardous events.] [A-3 - Number of [missing [persons] / presumed dead] due to hazardous events.]

A new indicator was proposed in the Second Session of the OEIWG: [A-1 alt. Number of deaths, missing persons, injured, displaced or [evacuated] due to hazardous events per 100,000.] NOTE: The Secretariat recognizes that this indicator measures elements beyond Mortality, being a combination of the proposed indicators required for Sendai Framework Targets (a) and (b). The Secretariat also recognizes that this Indicator approximates to the indicator proposed by the Inter-Agency and Expert Group on SDGs Indicators (IAEG-SDGs) and the UN Statistical Commission for use in measuring Targets 1.5, 11.5 and 13.1 of the SDGs. The Secretariat does not recommend using this indicator to measure Target A of the Sendai Framework, rather it recommends the use of originally proposed indicators for Target A and B which when combined will allow the measurement of SDGs Target 1.5, 11.5 and 13.1. As a result, a methodology for A-1 alt. is not proposed in this note.

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5. Applicable Definitions and Terminology Target A of the Sendai Framework specifically requires “global disaster mortality” to be estimated. For the purposes of this methodology, unless stated otherwise key terms are those defined in the “Working Text on Terminology” based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016 Death: The number of people who died during the disaster, or directly after, as a direct result of the hazardous event Missing: The number of people whose whereabouts is unknown since the hazardous event. It includes people who are presumed dead although there is no physical evidence. Hazard: A potentially damaging physical event, phenomenon or human activity that may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation. Hazardous event: The occurrence of a natural or human-induced phenomenon in a particular place during a particular period of time due to the existence of a hazard. Hazardous event: The occurrence of a natural, technological and biological phenomenon in a particular place during a particular period of time due to the existence of a hazard. Presumed dead: The number of people believed to be dead, for whom there is no physical evidence such as a body, and for which an official/legal report has been filed with competent authorities. Note from the Secretariat: The data on number of deaths and number of missing/presumed dead are mutually exclusive. Note from the Secretariat: In both definitions of "Missing" and "Presumed dead" the Secretariat suggests that the data for both categories is contingent upon the existence of legal reports or declarations. Such reports or declarations will ultimately result in those persons being legally declared dead ("declared death in absentia" or legal presumption of death) despite the absence of direct proof of the person's death, such as the identification of physical remains (e.g. a corpse or skeleton) attributable to that person. As a result, the indicator would use only official data, and not be dependent upon unofficial sources – such as mainstream media or humanitarian situation reports.

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6. Computation Methodology: In the case of Target A, the method of computation is a simple summation of related indicators from national disaster loss databases divided by the sum of figures of global population data (from national censuses, World Bank or UN Statistics information).

𝑨𝟏 =

(𝑨𝟐 + 𝑨𝟑 ) ∗ 𝟏𝟎𝟎, 𝟎𝟎𝟎 𝑷𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏

Where: A-1: A-2: A-3: Population:

Number of [deaths / deceased] and [missing [persons] / presumed dead] due to hazardous events per 100,000 Number of [deaths / deceased] due to hazardous events Number of [missing [persons] / presumed dead] due to hazardous events global population

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7. Critical issues, sources, data collection and statistical processing: Source and data collection National disaster loss databases. For Targets A through D, time dimension should be defined to establish when data should be recorded and reported. The dynamics of disasters can force changes in the data (e.g. persons injured can pass away after a certain period of time). Defining this issue is critical, especially when recording losses associated with slow-onset disasters such as drought. Therefore, given the propensity for variation after disaster, data for the two proposed indicators should be recorded only when data are stable, after the end of sudden-onset disasters, such as earthquakes or floods - 42 days is suggested (see below). It is recommended that a similar, welldefined threshold is determined to establish the ‘end date’ for the recording of data for slowonset disasters also. There are numerous mortality term thresholds that could inform Members’ deliberations. The fields of medicine and epidemiology often use terms between 28 and 42 days as benchmarks, among them: 42 days for maternal mortality after birth, new born deaths - which are considered in the first four weeks (28 days) of life, and referred to as the neonatal period (WHO, UNICEF, other sources); mortality after traffic accidents for which the WHO employs a period of 30 days. The Secretariat recommends the number of 42 days as it is consistent with medical research in maternal mortality at birth5, and while recognizing that any proposed threshold is arbitrary, it could be used as standard for the Framework. 42 days would be the period within which all people for which a legal determination of being missing or presumed dead is recorded after an event – this is expected to capture the majority of the reports. The experience gained in the aftermath of disasters suggest that this period may be sufficient to allow authorities to establish stable and appropriately representative figures. The UNISDR / DesInventar methodology suggests practical methods for the establishment of a start and end date of slow-onset disasters such as droughts. It is suggested that that the date of appearance of first reports of damage can mark the beginning of the disaster (not the actual phenomena, e.g. drought or similar slow onset event) and the date of the last report of physical damage associated with the event can be taken as the end date of the event. The same methodology also recommends annual reporting as a minimum for slow-onset disasters that span for more than one year, thereby facilitating the reporting of multi-annual events – most of which are associated with climatic phenomena and climate change induced processes.

Statistical processing Disaster loss data on mortality is significantly influenced by large-scale catastrophic events, which represent important outliers in terms of mortality, as they can imply considerable numbers of people killed (as was the case in the Haiti earthquake in 2010, the Great East Japan Earthquake in 5

Maternal Mortality Death (see http://www.maternalmortalitydata.org/Definitions.html) 42 days after birth or termination of pregnancy

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2011, and in several countries after the Indian Ocean Tsunami in 2004). UNISDR recommends countries report the data by event, so that complementary analysis can be undertaken to obtain trends and patterns in which such catastrophic events (that can represent outliers in terms of mortality) can be included or excluded. Disaggregation Further to the recommendations of both the OEIWG and the IAEG-SDGs, the Secretariat recommends disaggregating data: 

By country, by event, by hazard type (e.g. using the IRDR classification, natural hazards can be disaggregated as climatological, hydrological, meteorological, geophysical, biological and extra-terrestrial)



By deaths / missing



Additionally, the OEIWG proposed disaggregation by age, sex, location of residence and other characteristics (e.g. disability) as relevant and possible, in order to align with SDG's requirements. The Secretariat encourages the adoption of these recommendations.



Aggregation of “location of residence”: ideally by sub-national administrative unit, similar to municipality. Comments and limitations Not every country has a comparable national disaster loss database that is consistent with these guidelines (although current coverage exceeds 89 countries). Therefore, by 2020, it is expected that all countries will build/adjust national disaster loss databases according to the recommendations and guidelines of the OEIWG. As stated by Member States in the First and Second Sessions of the OEIWG, data of "Missing/Presumed dead" is not consistently collected. For many countries, the separation of data on "Missing/Presumed dead" from "Deaths/Deceased", or the collection of data on "Missing/Presumed dead" will be required to be able to report against the two separate indicators.

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ANNEX I: Summary table of indicators and sub-indicators by Category Target A Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

Indicator

Methodology

Data

A-1

[Number of [deaths / deceased] and [missing [persons] / presumed dead] due to hazardous events per 100,000.]

Y

Y

A-2

Number of [deaths / deceased] due to hazardous events.

Y

Y

A-3

Number of [missing [persons] / presumed dead] due to hazardous events.

Y

Y

A-1 alt.

[Number of deaths, missing, injured, displaced or [evacuated] due to hazardous events per 100,000.]

Target B

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REFERENCES Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. Note by the Secretary-General (E/CN.3/2016/2/Rev.1*). United Nations Economic and Social Council. Presented at the Forty-seventh session of the UN Statistical Commission. 8-11 March 2016. JRC, Tom De Groeve, Karmen Poljansek, Daniele Ehrlich. " Recording Disaster Losses: Recommendations for a European approach", European Commission, 2013. EUR 26111 EN – Joint Research Centre – Institute for the Protection and the Security of the Citizen. IRDR. "Guidelines on measuring losses from disasters. Human and Economic Impact Indicators". Integrated Research on Disaster Risk (IRDR), Data Project Report No. 2. 2015. EM-DAT - The OFDA/CRED international disaster database—www.emdat.net. Université Catholique de Louvain, Brussels, Belgium. http://www.emdat.be Visited the 2nd of February 2012. OSSO Desinventar.org—DesInventar Project. Corporación OSSO, Cali, Colombia. http://www.desinventar.org/ United Nations Development Programme (UNDP). 2013. A comparative review of country-level and regional disaster loss and damage databases. Bureau for Crisis Prevention and Recovery. New York. United Nations Office for Disaster Risk Reduction (UNISDR). "Loss Data and Extensive Risk Analysis". United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2015/en/garpdf/Annex2-Loss_Data_and_Extensive_Risk_Analysis.pdf United Nations Office for Disaster Risk Reduction (UNISDR). 2009. GAR 2009: Global Assessment Report on disaster risk reduction: risk and poverty in a changing climate. United Nations International Strategy for Disaster Reduction, Geneva. United Nations Office for Disaster Risk Reduction (UNISDR). 2011a. GAR 2011: Global Assessment Report on disaster risk reduction: revealing risk, redefining development. United Nations International Strategy for Disaster Reduction, Geneva. United Nations Office for Disaster Risk Reduction (UNISDR). 2011b. Desinventar.net database global disaster inventory. United Nations International Strategy for Disaster Reduction, Geneva. United Nations Office for Disaster Risk Reduction (UNISDR). 2013a. GAR 2013: Global Assessment Report on disaster risk reduction: from shared risk to shared value; the business case for disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ United Nations Office for Disaster Risk Reduction (UNISDR). Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. 14

United Nations Office for Disaster Risk Reduction (UNISDR). Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. WHO, ed. (2015). Global Status Report on Road Safety 2015 (PDF) (official report). Geneva, Switzerland. Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016

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Concept note on Methodology to Estimate the Number of Affected People to Measure the Achievement of Target B of the Sendai Framework for Disaster Risk Reduction: A Technical Review

10 June 2016

The United Nations Office for Disaster Risk Reduction

16

1. Overview This document outlines a methodology for the global estimation of the number of people affected by hazardous events. The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OEIWG) requested the UNISDR to propose a methodology at the First and Second Sessions, held in Geneva on 29-30 September 2015 and 1011 February 2016. The purpose of this document is to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of the global Target B of the Sendai Framework for Disaster Risk Reduction 2015-2030. Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared to 2005-2015 The methodology described here is based on previous experience of a number of governments, academic and research institutions, private organizations and work of the United Nations in more than 89 countries supporting the building of Disaster Loss Databases. This methodology proposes the collection and use of simple and uniform indicators of affected (number of people) as the point of departure for computation. The methodology proposed by the Secretariat is based on previous work undertaken by a number of groups that have engaged in the collection of similar data. In particular, it is based on the work of: ▫ the UNISDR, UNDP and other organisations supporting the development of national loss databases (which uses the DesInventar tool in the majority of cases), ▫ other independent national disaster database constructors which cover additional countries, such as databases in the USA (SHELDUS), Australia, Canada, Spain, France and other European countries. ▫ the EMDAT, MunichRe and SwissRe international disaster datasets ▫ the recommendations to EU member states on loss data collection issued by the Joint Research Centre of the European Commission, JRC, and ▫ the conclusions of IRDR DATA group.

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2. Summary The methodology outlined here aims to produce an approximate value (a “proxy”) that provides a verifiable, consistent and homogeneously calculated number of people affected. The elements of ‘affected’ are numerous and complex. People can be ‘affected’ with varying degrees of severity: from the loss or destruction of their primary residence, to the inconvenience of being unable to use household appliances as a result of an interruption in the electricity supply. The Working Text on Terminology based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016 suggests that “People can be affected directly or indirectly. Affected people may experience short-term or long-term consequences to their lives, livelihoods or health and in the economic, physical, social, cultural and environmental assets.” The following two definitions are proposed in the updated Working Text on Terminology: Directly affected: People who have suffered injury, illness or other health effects; who were evacuated, [displaced,] relocated, [became refugees]; or have suffered direct damage to their livelihoods, economic, physical, social, cultural and environmental assets. Indirectly affected: People who have suffered consequences, other than or in addition to direct effects, over time due to disruption or changes in economy, critical infrastructures, basic services, commerce, work or social, health and physiological consequences. Given the large number of variables eligible for consideration in ‘Affected’, it is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure of affected. Even estimations of directly affected can be subjective, dependent on the methodology and criteria used to define ‘affectation’, as well as the exhaustiveness of data collection. Given the difficulties of assessing the full range of all affected (directly and indirectly), UNISDR proposes the use of an indicator that would estimate “directly affected” as a proxy for the number of affected. This indicator, while not perfect, uses widely available data and could be used consistently across countries and over time to measure the achievement of the Target B. From the perspective of data availability, feasibility of collection and measurability, the Secretariat has proposed to build a compound indicator based on: ▫ ▫ ▫

Number of people Injured or ill as a direct result of a hazardous event Number of evacuated people due to hazardous events Number of relocated people due to hazardous events

and to measure the number who suffered direct damage to their livelihoods or assets ▫ ▫ ▫ ▫

People whose houses were damaged or destroyed People who received food relief aid. People who work in or own industries and commercial facilities affected (B-7) People who work on or own agricultural crops and livestock affected or lost (B-7) 18

The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

No.

Indicator

Methodology

Data

B-1

Recommended - for measurement at the global level

Y

Y

B-1 B-2 B-2. alt

Number of affected people by hazardous events 100,000. Number of injured or ill people due to hazardous events Number of people suffering from physical injuries, trauma or cases of disease requiring immediate medical assistance as a direct result of a hazardous event. Number of people who left their places of residence due to hazardous events. Number of [evacuated people / people who are saved] due to hazardous events [after the event] Number of relocated people due to hazardous events. Number of people whose houses were damaged due to hazardous events. Number of people whose houses were destroyed due to hazardous events. Number of people who received aid including food and non-food aid due to hazardous events. Number of people whose livelihoods were disrupted, destroyed or lost due to hazardous events.

Y Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

B-1

Recommended - for measurement at the national level

Y

Y

B-3a1

Y

N

Y

N

B-3 alt. B-3 altbis.

Number of evacuated people due to hazardous events before the event. Number of evacuated people due to hazardous events during or after the event. Number of people displaced due to hazardous events. Number of people evacuated, relocated and displaced due to hazardous events.

Y

N

Y

N

B-1

Not Recommended

Y

Y

B-3c B-3d B-3d B-8

[Number of people protected per 100,000.] [Refugees who left their place of residence on their own.] [Number of displaced persons who have not joined shelters.] [Number of people / percentage of population “protected” by evacuation, by improved infrastructure or by other measures that reduce the possible impact of disasters on people.]

N N N

N N N

N

N

B-3 B-3a B-3b B-4 B-5 B-6 B-7

B-3a2

19

3. Technical Requirements for an indicator to measure “number of affected people” Indicators proposed to measure targets in this and other frameworks are, as the word itself suggests, numbers that give an indication of the size of certain phenomena6, in this case it estimates the number of people affected by disasters. It is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure of the number of affected people. It would be impossible to remove a certain level of uncertainty or inaccuracy from estimations of affected people, for which the sourcing of data is subject to the legal procedures and timeframe criteria of a specific country, as well as the exhaustiveness of data collection. In this sense, the number of affected people estimated is always an approximate value (a “proxy”). However, it should be noted that the experience of many disaster loss data providers demonstrate that this indicator is in general not robust and consistent across different data sources. The indicators to measure the number of affected people for the Sendai Framework aim to meet following important criteria: Consistent over time: The target requires the comparison of losses of two different decades, the decade of the Hyogo framework (2005-2015) and the last decade of the Sendai Framework (20202030). Monitoring the losses, nevertheless, should occur throughout the entire period of 25 years in order to obtain a continuous view of the progress of implementation and achievements, and data must be recorded and reported in a consistent way for the cycle of measurement, and so avoiding introducing biases. Consistent across countries: It must be a) applicable to any country in the world, to the maximum degree possible, allowing comparison among countries or regions, and b) feasible, such that data can be obtained regardless of the level of development or income of each country. SMART: Specific, Measurable, Achievable, Relevant, Time Bound. Reliable: Results can be trusted and if possible have a measure of dispersion, and for which a particular uncertainty measure can be determined. Transparent: The methodology used is well known, with caveats declared, and for which weaknesses, limitations and strengths, including economic assessment biases, are identified. Verifiable: The estimated number of people affected value can be traced back to the original official documents that support affectation claims. Feasible: Easy to collect data in a practical and realistic way, without imposing an extraordinary or even impossible burden on countries. Taking advantage of existing data: Many countries have already collected standardized data. Taking advantage of this fact is more practical than having everyone start from zero. 6

http://www.oxforddictionaries.com/es/definicion/learner/indicator

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Useful: Results can be used not only for measuring the achievement of targets but also for DRR strategy planning, awareness raising, risk assessments and the development of DRR and related policies.

4. List of Proposed Indicators to Measure Target B: The following indicators are taken from the Working Text on Indicators, based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016. [B-1 - Number of affected people [by hazardous event / due to hazardous events] per 100,000.] (This indicator should be computed based on indicators B-2 to B-6). [B-2 - Number of injured or ill people due to hazardous events.] [B-2 alt. - Number of people suffering from physical injuries, trauma or cases of disease requiring immediate medical assistance as a direct result of a hazardous event.] [B-3 - Number of people who left their [places of residence / home][and places where they are] due to hazardous events. ] (SDG proposal - in that this indicator combines B-3a and B-3b) [B-3a - Number of [evacuated people / people who are saved] due to hazardous events [after the event]] Note: Evacuated addresses the people temporarily moved from their place of residence. This indicator can be interpreted as proxy for success indicator of early warning system and risk information accessibility in Target G. [B-3b - Number of relocated people due to hazardous events. ] Note: Relocated addresses the people permanently moved from their place of residence. This indicator excludes preventive relocation before the event. [B-3c – Number of people protected per 100,000.] [B-3d - Refugees who left their place of residence on their own.] Replace all with: [B-3 alt. – Number of people displaced due to hazardous events.] OR [B-3 alt-bis. - Number of people evacuated, relocated and displaced due to hazardous events.] [B-4 - Number of people whose [houses / dwellings or homes] were damaged due to hazardous events.] 21

[B-5 - Number of people whose [houses / dwellings or homes] were destroyed due to hazardous events.] [B-6 - Number of people who [received / required] [food relief aid / aid including food [and non-food] and medical aid] [among other things] due to hazardous events.] Note: This indicator may be restricted only for the case of droughts. The indicator is not easily comparable inter-temporarily and inter-nationally due to the influence of national and international relief policy. [B-7 - Number of people whose livelihoods were disrupted, destroyed or lost due to hazardous events.] The following are proposals received from Member States via email during the Second Session of the OEIWG that were not introduced from the floor. [B-3a1 - Number of evacuated people due to hazardous events before the event.] [B-3a2 - Number of evacuated people due to hazardous events during or after the event.] [B-3c – Number of people [protected / assisted] per 100,000.] [B-3d – Number of displaced persons who have not joined shelters.] [B-4 - Number of people whose [houses / dwellings or homes] were [damaged / partially destroyed] due to hazardous events.] [B-5 - Number of people whose [houses / dwellings or homes] were [totally] destroyed due to hazardous events.] [B-8 - Number of people / percentage of population “protected” by evacuation, by improved infrastructure or by other measures that reduce the possible impact of disasters on people.] Note: Mitigation measures could include, as appropriate, a wide range of activities by relevant actors. See definition of “Mitigation” in UNISDR terminology document.]

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5. Applicable Definitions and Terminology Target B of the Sendai Framework specifically requires “the number of affected people” to be estimated. For the purposes of this methodology, unless stated otherwise key terms are those defined in the “Working Text on Terminology” based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016 Displaced: Persons who, for different reasons and circumstances because of risk or disaster, have to leave their place of residence. Evacuated: People who, for different reasons or circumstances because of risk conditions or disaster, move temporarily to safer places before, during or after the occurrence of a hazardous event. [Alt. Evacuated: The number of people who temporarily moved from where they were (including their places of residence, work places, schools, and hospitals) to safer locations in order to ensure their safety.] Hazard: A potentially damaging physical event, phenomenon or human activity that may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation. Hazardous event: The occurrence of a natural or human-induced phenomenon in a particular place during a particular period of time due to the existence of a hazard. Hazardous event: The occurrence of a natural, technological and biological phenomenon in a particular place during a particular period of time due to the existence of a hazard. Houses damaged: Houses (housing units) with minor damage, not structural or architectural, which may continue to be habitable, although they may require some repair or cleaning. (SDG Proposal) Houses destroyed: Houses (housing units) levelled, buried, collapsed, washed away or damaged to the extent that they are no longer habitable. (SDG Proposal) Injured or ill: People suffering from a new or exacerbated physical or psychological harm, trauma or an illness as a result of a hazardous event. [Alt. Injured or ill: The number of people suffering from physical injuries, trauma or cases of disease requiring immediate medical assistance as a direct result of a hazardous event. ] Livelihood*: Means, capabilities, tangible and intangible assets, including human, social, natural, physical, financial resources, that people draw upon to make a living. Alt. Livelihood*: The capacities, productive assets (both living and material) and activities required for securing a means of living, on a sustainable basis, with dignity. 23

People who left their places of residence: The number of people forced or obliged to leave their places of residence due to the threat or impact of hazardous events. This can be alternatively worded as people displaced. In this indicator it consists of people who are evacuated and relocated. People who received food relief aid: The number of persons who received food /nutrition, by government or as humanitarian aid, during or in the aftermath of a hazardous event. People whose houses were damaged or destroyed due to hazardous events: The estimated number of inhabitants previously living in the houses (housing units) damaged or destroyed. All the inhabitants of these houses (housing units) are assumed to be affected being in their dwelling or by direct consequence of the destruction/damage to their housings (housing units). An average number of inhabitants per house (housing unit) in the country can be used to estimate the value. Productive assets*: Assets with both direct and indirect values, which can be used to generate a value-added Relocated: People who, for different reasons or circumstances because of risk or disaster, have moved permanently from their places of residence [to new sites / to safer areas.] [Alt. Relocated: The number of people who moved permanently from their homes to new sites due to hazardous event. (SDG Proposal)]

* Proposals received from Member States via email during the Second Session that were not introduced from the floor.

24

6. Notes from the Secretariat on comments and proposals from Member States Exclusion of Mortality: The Secretariat recommends mortality figures are not counted in this category. Double-counting: In reality, double counting of affected people is unavoidable in many countries (for example, injured and evacuated). Minimum double counting is summing “number of injured” (B-2) and Number of people whose housings were damaged or destroyed (B-4 and B-5). Relocated (B-3b) is sub-set of number of people whose housings were destroyed (B-5). B-4 and B-5: Housing damage and destruction affects both the lives and livelihoods of most urban and rural households. Some members of the OEIWG recommended not using the indicators related with the people whose houses were damaged/destroyed in the computation. The Secretariat and other expert groups - such as IRDR - recommend using such indicators, as they can be estimated from widely available and verifiable data and reflect vulnerability and livelihood issues. Data on housing damaged and destroyed is essential for economic loss estimations, and so using these indicators would not impose additional data collection burden. The average number of people living in a housing unit in the country is required for the computation of these indicators, and the Secretariat expects these data to be relatively stable over time. B-4 and B-5 use the same data set as C-5 and C-6. B-4 (people whose houses are damaged) and B5 (people whose houses are destroyed) are mutually exclusive; as are C-5 and C-6. B-3: the Expert Group recommends the term “Number of people who are forced to leave their places of residence”, and proposes merging B-3a and B-3b, and adding to the definition the wording to allow the inclusion of “people that have been displaced directly by disasters or risk but not included in “evacuated” and “relocated” (e.g. people becoming homeless due to disasters)” to create new B-3 indicator. How to count the new category of people would be a challenge. . Nevertheless, the Secretariat recommends that national reporting includes these categories (B-3a and B-3b) for DRR policy making. Members of the OEIWG suggested that these categories of “affected people” need to be elaborated further. Some members encourage the development of indicators for “affected people” that include the perspective of livelihood; other members propose that this be examined further. [B-2 alt. - Number of people suffering from physical injuries, trauma or cases of disease requiring immediate medical assistance as a direct result of a hazardous event.] This is a rewording of the indicator that was originally proposed. This definition is consistent with the SDG and current data collection practices. [B-3a - Number of [evacuated people / people who are saved] due to hazardous events [after the event.]] This indicator has been suggested to measure the degree to which the relevant authorities have been successful in avoiding human losses by evacuating populations pre-emptively. However, the position of the Secretariat and suggestion to the Working Group is to consider the fact that even if 25

human lives are saved with this measure, the simple fact of being evacuated affects and disrupts the normal lives of people. Furthermore, evacuations in the proposed indicator concern people, and not the property and assets exposed to the hazards and that people may be required to leave behind. The proposal for the inclusion of [after the event] means that the indicator does not discriminate on the timing of the evacuation. Note: Such an indicator could be considered also in the context of Target E, should Members wish to consider a bifurcated approach to the measurement of the target. For example, Target E indicators could measure inputs (national and local disaster risk reduction plans, for instance) and outputs (the outcomes of those plans, the number of people saved / protected for example). In which case, indicators such as this one, could be migrated to Target E. [B-3 alt. – Number of people displaced due to hazardous events.] OR [B-3 alt-bis. - Number of people evacuated, relocated and displaced due to hazardous events.] These two indicators pose data collection problems including with regard to the number of people that self-evacuate, and self-relocate. The Secretariat does not consider data collection feasible, because it would mean collecting data on segments of the population that may not have been tracked or registered by the appropriate authority. Authorities would have no means to measure nor verify this, especially when many of the available resources will be devoted to the response. [B-3c – Number of people [protected / assisted] per 100,000.] The Secretariat does not recommend the use of such indicators: a) all loss indicators are to be collected by hazard event, but the number of protected people is not attached to a specific hazardous event, but can be considered a relatively ‘permanent’ status potentially maintained over many events. This raises the question of multiple counting of this figure; b) this is an ‘input’ indicator, the outcome of which reduces the number of people affected - for example fewer people dead, fewer people injured, fewer people relocated, or fewer people living in houses that have been destroyed and damaged. Counting the number of ‘protected’ people can be considered double counting of the impact of risk reduction measures. See also the Note in indicator B-3a above. [B-3d - Refugees who left their place of residence on their own.] The concept of Refugee may have legal consequences that should be avoided. [B-3d – Number of displaced persons who have not joined shelters.] This indicator poses tremendous data collection problems; it would entail collecting data on segments of the population that may not have been tracked or registered by the appropriate authority, an exercise that the Secretariat considers unfeasible. Many authorities would be challenged in determining the means to measure this, especially at a time when available resources are devoted to the response.

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[B-6 - Number of people who [received / required] [food relief aid / aid including food [and nonfood] and medical aid] [among other things] due to hazardous events.] The Secretariat recognises that while specificity of this nature with regard to assistance provided may be desirable, this entails a significantly greater data collection and reporting burden. [B-7 - Number of people whose livelihoods were disrupted, destroyed or lost due to hazardous events.] The Secretariat recognizes that this indicator responds to the spirit of the Target and is consistent with the people-centred approach of the SDGs, but its practical implementation faces some of the same challenges of the concept of ‘Affected’. There is no definition of ‘Livelihood’ that can be used in a practical way. The concept of ‘disruption’ of livelihood is also difficult to define. For instance, Members may wish to consider if the totality of national reporting systems can capture job losses (for which a definition will be required), or measure business interruption (notably to small and medium sized enterprises). The Secretariat recognises the challenges that confront data collection for this indicator, including problems of subjective interpretation inter alia. So as to adhere to the principle of simplicity, some elements, for example business resilience, could be more appropriately addressed by relevant national indicators for the four priorities for action. In order to measure this indicator, a large number of (subjective) sub-indicators would be required; this will impose higher reporting burden on countries. However, and with the same spirit of providing a ‘proxy’ indicator that could reflect the number of people for whom their livelihoods are affected, the following definition proposed by Member States to the OEIWG could be used: [Livelihood (ref. indicators: B-7): The capacities, productive assets (both living and material) and activities required for securing a means of living, on a sustainable basis, with dignity. ] Some of the most important productive assets required to secure a means of living are those correlated with labour; the current reporting requirements already ask Member States to report on the following: -

Housing unit, where many families host self-employment schemes Agricultural crops Livestock Commercial Facilities Industrial Facilities

The Secretariat has already proposed the use of the Number People living in Houses Damaged and Affected as part of the number of people affected.

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Therefore, in counting the following sub-indicators measuring those whose labour activity has been affected, a more complete picture of the number of people affected can be built: -

-

-

-

B-7a Number of workers in Agriculture with crops damaged or destroyed by hazardous events (would use indicator C-2a, and require that countries or the Secretariat or other UN organization - such as FAO - establish the statistic of Average Number of Workers per hectare) B-7b Number of workers responsible for Livestock lost in hazardous events (would use indicator C-2b, and require that countries or the Secretariat or other UN organization - such as FAO - establish the statistic of Average Number of Workers per livestock) B-7c Number of workers employed in Commercial Facilities damaged or destroyed by hazardous events (would use sub-indicators in C-4 and require that countries or the Secretariat or other UN organization - such as ILO - establish the statistic of Average Number of Workers per commercial facility) B-7d Number of workers employed in Industrial Facilities damaged or destroyed by hazardous events (would use sub-indicator C-3b and require that Member States, or the Secretariat or other UN organization - such as ILO - establish the statistic of Average Number of Workers per industrial facility. If the member states report these industrial facilities by size, the measurement could be much more precise)

As additional note, these indicators could also take into account the number of dependents per worker. [B-8 - Number of people / percentage of population “protected” by evacuation, by improved infrastructure or by other measures that reduce the possible impact of disasters on people.] Note: Mitigation measures could include, as appropriate, a wide range of activities by relevant actors. See definition of “Mitigation” in UNISDR terminology document.] Please see the notes from the Secretariat for indictors B-3 alt-bis. and B-3c.

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7. Computation Methodology In the case of Target B, the method of computation is a simple summation of related indicators from national disaster loss databases divided by the sum of figures of global population data (from national censuses, World Bank or UN Statistics information).

𝑩𝟏 =

𝒔𝒖𝒎(𝑩𝟐 . . 𝑩𝟔 ) ∗ 𝟏𝟎𝟎, 𝟎𝟎𝟎 𝑷𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏

Indicators B4 and B5 shall be computed using the Average Number of Occupants per House of the country, AOH where:

𝑨𝑶𝑯 =

𝑷𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅𝒔

And

𝑩𝟒 = 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐡𝐨𝐮𝐬𝐞𝐬 𝐝𝐚𝐦𝐚𝐠𝐞𝐝 ∗ 𝑨𝑶𝑯 𝑩𝟓 = 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐡𝐨𝐮𝐬𝐞𝐬 𝐝𝐞𝐬𝐭𝐫𝐨𝐲𝐞𝐝 ∗ 𝑨𝑶𝑯 Where the number of houses damaged and destroyed are also to be used in Target C.

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8. Critical issues, sources, data collection and statistical processing: There are a number of important challenges to be addressed: ▫

▫

▫

▫

▫

People can be affected in many ways and to different degrees. As stated above and suggested by the Terminology, affectation can vary in many ways. An approach that seeks to ‘count’ every person affected in one way or another may therefore not be practical nor useful, and the Target may be better assessed on the basis of an ‘estimation’ of the number of people that is affected to a high degree (directly affected). People can be affected at great distance from the disaster site, crossing borders even continents. For example, the eruption of the Eyjafjallajökull volcano in Iceland in 2010 was notable because the volcanic ash plume disrupted air travel in northern Europe (with global impact) for several weeks. People can be affected during the onset of a disaster and/or long after the disaster. Numerous cases studies have shown that the indirect consequences of disasters can be felt for many years after the event. For example, chronic health problems resulting from child malnutrition, or unrecoverable losses in market share. As people can be affected in different ways, is extremely difficult to eliminate double counting. Some people can be both directly and indirectly affected; for example, they can be injured, evacuated, and lose assets even businesses. Accurately calculating the number of exposed persons is extremely challenging. This is especially the case for the large number of small and medium-sized hazardous events. This is further complicated by the fact that not everyone exposed is affected.

Source and data collection Main source: National disaster loss databases. A thorough examination of more than 100 disaster loss databases suggests that during emergencies, competent authorities (such as Emergency Services and sectoral Ministries) have collected certain information in a relatively consistent way. For Targets A through D, time dimension should be defined to establish when data should be recorded and reported. The dynamics of disasters can force changes in the data (e.g. persons injured can pass away after a certain period of time). Defining this issue is critical, especially when recording losses associated with slow-onset disasters such as drought. Therefore, given the propensity for variation after disaster, data for the proposed indicators should be recorded only when data are stable, after the end of sudden-onset disasters, such as earthquakes or floods - 42 days is suggested (see below). It is recommended that a similar, welldefined threshold is determined to establish the ‘end date’ for the recording of data for slowonset disasters also. There are numerous mortality thresholds that could inform Members’ deliberations. The fields of medicine and epidemiology often use terms between 28 and 42 days as benchmarks, among them: - newborn deaths, which are considered in the first four weeks (28 days) of life, and referred to as the neonatal period (WHO, UNICEF, other sources); mortality after traffic accidents for which the WHO employs a period of 30 days. 30

The Secretariat recommends the number of 42 days as it is consistent with medical research in child mortality at birth7, and while recognizing that any proposed threshold is arbitrary, it could be used as standard for the Framework. 42 days would be the period within which all people for which a legal determination of being missing or presumed dead is recorded after an event – this is expected to capture the majority of the reports. The experience gained in the aftermath of disasters suggest that this period may be sufficient to allow authorities to establish stable and appropriately representative figures. The UNISDR / DesInventar methodology suggests practical methods for the establishment of a start and end date of slow-onset disasters. It is suggested that that the date of appearance of first reports of damage can mark the beginning of the disaster (not the actual phenomena, like a drought or similar slow onset event) and the date of the last report of physical damage associated with the event can be taken as the end date of the event. The same methodology also recommends annual reporting as a minimum for slow-onset disasters that span for more than one year, thereby facilitating the reporting of multi-annual events – most of which are associated with climatic phenomena and climate change induced processes.

Statistical processing: Disaster loss data is greatly influenced by large-scale catastrophic events, which represent important outliers in terms of affected people. UNISDR recommends countries report the data by event, so that complementary analysis can be undertaken to obtain trends and patterns in which such catastrophic events (that can represent outliers in terms of affected) can be included or excluded. Disaggregation Further to the recommendations of both the OEIWG and the IAEG-SDGs, the Secretariat recommends disaggregating data: 

By country, by event, by hazard type (e.g. using the IRDR classification, natural hazards can be disaggregated as climatological, hydrological, meteorological, geophysical, biological and extra-terrestrial)



By injured or ill/evacuated/relocated/People whose houses were damaged/people whose houses were destroyed/people who received food relief aid



Additionally, the OEIWG proposed disaggregation by age, sex, location of residence and other characteristics (e.g. disability) as relevant and possible, in order to align with SDG's requirements. The Secretariat encourages the adoption of these recommendations.



Aggregation of “location of residence”: ideally by sub-national administrative unit similar to municipality.

7

Maternal Mortality Death (see http://www.maternalmortalitydata.org/Definitions.html) 42 days after birth or termination of pregnancy

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Comments and limitations Not every country has a comparable national disaster loss database that is consistent with these guidelines (although current coverage exceeds 89 countries). Therefore, by 2020, it is expected that all countries will build/adjust national disaster loss databases according to the recommendations and guidelines of the OEIWG. For B-4 and B-5, the national disaster loss databases developed in many countries have historic data on housing damaged/destroyed. To establish baseline data, it is necessary to identify an average number of inhabitants per house in the country.

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ANNEX I: Summary table of indicators and sub-indicators by Category Target B Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

B-1 B-2 B-2. alt B-3 B-3 alt

Indicator

Methodology

Data

[Number of affected people [by hazardous event / due to hazardous events] per 100,000.]

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

[Number of injured or ill people due to hazardous events.] [Number of people suffering from physical injuries, trauma or cases of disease requiring immediate medical assistance as a direct result of a hazardous event.] [Number of people who left their [places of residence / home][and places where they are] due to hazardous events. ] [Number of people displaced due to hazardous events.]

B-3 alt-bis

[Number of people evacuated, relocated and displaced due to hazardous events.]

Y

N

B-3a

[Number of [evacuated people / people who are saved] due to hazardous events [after the event]]

Y

Y

Y

N

Y

N

B-3b

Number of evacuated people due to hazardous events before the event. Number of evacuated people due to hazardous events during or after the event. [Number of relocated people due to hazardous events. ]

Y

Y

B-3c

[Number of people protected per 100,000.]

N

N

B-3c

[Number of people [protected / assisted] per 100,000.]

N

N

B-3d

[Refugees who left their place of residence on their own.]

N

N

B-3d

[Number of displaced persons who have not joined shelters.]

N

N

Y

Y

Y

Y

Y

Y

Y

Y

N

N

B-3a1 B-3a2

B-4 B-5 B-6 B-7 B-8

[Number of people whose [houses / dwellings or homes] were [damaged / partially destroyed] due to hazardous events.] [Number of people whose [houses / dwellings or homes] were [totally] destroyed due to hazardous events.] [Number of people who [received / required] [food relief aid / aid including food [and non-food] and medical aid] [among other things] due to hazardous events.] [Number of people whose livelihoods were disrupted, destroyed or lost due to hazardous events.] [Number of people / percentage of population “protected” by evacuation, by improved infrastructure or by other measures that reduce the possible impact of disasters on people.]

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REFERENCES Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. Note by the Secretary-General (E/CN.3/2016/2/Rev.1*). United Nations Economic and Social Council. Presented at the Forty-seventh session of the UN Statistical Commission. 8-11 March 2016. JRC, Tom De Groeve, Karmen Poljansek, Daniele Ehrlich. " Recording Disaster Losses: Recommendations for a European approach", European Commission, 2013. EUR 26111 EN – Joint Research Centre – Institute for the Protection and the Security of the Citizen. IRDR. "Guidelines on measuring losses from disasters. Human and Economic Impact Indicators". Integrated Research on Disaster Risk (IRDR), Data Project Report No. 2. 2015. EM-DAT The OFDA/CRED international disaster database—www.emdat.net. Universite Catholique de Louvain, Brussels, Belgium. http://emdat.be/. Visited the 2nd of February 2012. OSSO Desinventar.org—DesInventar Project. Corporación OSSO, Cali, Colombia. http://desinventar.org/en/ UNISDR "Loss Data and Extensive Risk Analysis". United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2015/en/gar-pdf/Annex2Loss_Data_and_Extensive_Risk_Analysis.pdf UNDP (United Nations Development Programme), 2013. A comparative review of country-level and regional disaster loss and damage databases. Bureau for Crisis Prevention and Recovery. New York. UNISDR (The United Nations Office for Disaster Risk Reduction). 2009. GAR 2009: Global assessment report on disaster risk reduction: risk and poverty in a changing climate. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011a. GAR 2011: Global Assessment Report on disaster risk reduction: revealing risk, redefining development. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011b. Desinventar.net database global disaster inventory. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2013a. GAR 2013: Global Assessment Report on disaster risk reduction: from shared risk to shared value; the business case for disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. 34

Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 United Nations Office for Disaster Risk Reduction. Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. United Nations Office for Disaster Risk Reduction. Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015.

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Concept note on Methodology to Estimate Direct Economic Losses from Hazardous Events to Measure the Achievement of Target C of the Sendai Framework for Disaster Risk Reduction: A Technical Review

10 June 2016

The United Nations Office for Disaster Risk Reduction

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1. Overview This document outlines a methodology to estimate the value of direct economic losses associated with hazardous events. The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction requested the UNISDR to propose a methodology at the First Session, held in Geneva on 29-30 September 2015, as informed by the “Indicators to monitor global targets of the Sendai Framework for Disaster Risk Reduction 20152030 - a technical review”. (UNISDR, 2015a). The purpose of this document is to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of the global target C of the Sendai Framework for Disaster Risk Reduction 2015-2030. Target C: Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030 The methodology described here is based on the work published in the Global Assessment Report on Disaster Risk Reduction (GAR) editions of 2013 and 2015 (UNISDR, 2013b and 2015c; Velazquez, et.al 2014), which is a simplified and adapted version of the UN Economic Commission for Latin America and the Caribbean methodology for disaster assessment (UN-ECLAC, 2014) and built on continuing work with scientific partners including the team of scientists that developed a probabilistic global risk model in collaboration with UNISDR. The methodology has been tested with datasets of 56 and 82 countries, respectively. In the latest round of tests, UNISDR produced the economic assessment of 350,000 reports of small, medium and large scale disasters. Disaster loss economic assessments have been conducted and reported by different actors using different approaches, with the notable exception of UN-ECLAC and World Bank post-disaster damage and loss assessments (DaLA’s and PDNA’s), which proposes a uniform, rigorous and consistent methodology, however conducted only for large scale disasters. This lack of uniform approach is reflected in inconsistencies in economic losses currently reported by both national and international data sources. In the cases these estimates are present it is most often difficult to know which elements of loss were taken into consideration and the methodology, criteria and parameters used for estimation. The methodology proposed here will allow assigning a consistent, conservative and homogeneously estimated economic value to physical losses in hundreds of thousands of disasters at all scales expected to be reported as part of the Sendai Framework Targets monitoring process. The economic evaluation methodology is presented for each of the Indicators proposed. Each section contains a brief explanation of the three steps (data collection, conversion of physical value into economic value and conversion from national currency into US dollars) while identifying challenges and suggesting options for countries to consider how to address them. The following are the indicators for which an economic valuation is proposed in this guideline: C-1, C-2, C-3, C-4, C-5, C-6, C-7, C-8, C-9 and C-10. 37

2. Summary This methodology proposes, whenever possible, the collection and use of simple and uniform physical indicators of damage (counts of assets affected) as the starting point for calculations, instead of requesting countries to directly evaluate the economic value of direct losses. A centralized and common approach to estimate direct economic losses will result in a homogeneous and consistent indicator. National Disaster loss databases, the source of data used in this methodology to estimate direct economic loss, usually contain a large number of records of hazardous events at all scales, and include quantitative and qualitative indicators of physical damage. From the experience of working with 89 countries developing disaster loss databases, it can be concluded that simple physical damage indicators are in general robust, more practical and easier to obtain and collect. Using a simple and consistent pricing methodology for indicators of losses in respect of houses, roads, agriculture, schools, commercial, industrial and health facilities, it is possible to estimate a significant part of total direct economic loss. Suggestions are also made as to economic valuations of Environmental and Cultural Heritage loss and damages. For all of the sectors that refer to the built environment (i.e. housing, health, education, commercial, and industrial facilities) a simple methodology is proposed estimating the price of lost assets, using the cost of construction as the basis for replacement value. The Economic Commission for Latin America and the Caribbean (ECLAC) methodology suggests that the value of the physical damage to buildings can be calculated based on the:  size of the building  price per square meter of construction  damage to furniture and equipment contained in the building (as % of the value of building)  associated infrastructure (utility networks access roads, landscaping, as % of the value of building) In turn, the values of equipment and associated infrastructure are estimated as a percentage of the value of the construction, a percentage that varies between sectors. In the case of houses, for example, the equipment contained is suggested to be 25% of the value of the house; this percentage is much higher in health and industrial sectors. For transportation infrastructures, the methodology uses rehabilitation costs per lineal meter, extracted from common projects in the sector. Agricultural damage is estimated as a proxy value calculated based on the output of the crops. The underlying principle is that direct losses (seeds, fertilizers, pesticides, labor and other costs that comprise what farmers invest in their crops) can be estimated as a percentage of the expected yield of crops, valued using the price to producer of the yield per hectare. In the case of livestock, the direct cost of loss of animals is assessed as the price to producer of animals calculated with the price per kilo of meat of livestock. Environmental losses are proposed to be evaluated using a minimal number of indicators of 38

physical damage recording damage data for up to 11 biomes considered by the TEEB methodology. For each biome, the Secretariat initially proposes the use of “Raw Materials” service as a proxy for direct economic losses, the most relevant of the 22 ecosystem services associated with these biomes in terms of loss of assets. The rest of these services are considered part of indirect losses. Cultural Heritage economic losses are much more difficult to assess, therefore for the purpose of assigning a direct economic loss value, a simple division of assets lost in two groups is proposed: ▫ one composed of buildings, monuments and fixed infrastructure, and ▫ the second composed of ‘mobile’ elements such as art, historical artefacts. The economic assessment of direct losses associated with these items will come from rehabilitation costs, and in the case of mobile assets, from market value. In all cases, the Secretariat is proposing, as best practice, that all of the physical damage indicators are collected and kept by countries as these are important information assets. Physical damage indicators will allow the future connection of loss data with risk assessments or disaster forensics. It will make the assessment of direct losses more transparent, and will allow, among other things, the incremental improvement of assessment; as improved methodologies are developed and better and more comprehensive baseline data are collected by countries.

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The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

No.

Indicator

Methodology

Data

B-1

Recommended - for measurement at the global level

Y

Y

C-1

Direct economic loss due to hazardous events in relation to global gross domestic product.

Y

Y

C-2

Direct agricultural loss due to hazardous events.

Y

Y

Y

N

Y

N

C-3 C-4

Direct economic loss due to industrial facilities damaged or destroyed by hazardous events Direct economic loss due to commercial facilities damaged or destroyed by hazardous events.

C-5

Direct economic loss due to houses damaged by hazardous events

Y

Y

C-5b

Damage and loss on administrative buildings.

Y

N

C-6

Direct economic loss due to houses destroyed by hazardous events Direct economic loss due to damage to critical infrastructure caused by hazardous events. Direct economic loss due to cultural heritage damaged or destroyed by hazardous events. Direct economic loss due to environment degraded by hazardous events. [Total insured direct losses due to hazardous events]

Y

Y

Y

Y

Y

N

Y

N

Y

Y

[Direct economic losses due to disruptions to basic services.] [Direct economic loss due to services sectors (such as transportation, tourism, finance) caused by hazardous events.]

N

N

N

N

B-1

Not Recommended

Y

Y

C-13

[Total of risk informed investments relative to Gross Domestic Product.]

N

C-14

[Number of micro enterprises affected.]

N C-3 / C-4

C-15

[Number of small and medium enterprises affected (registered enterprises) - sales drop, production drop, profit drop, direct damage to facilities etc.]

C-7 C-8 C-9 C-10

Recommended - for measurement at the national level C-11 C-12

C-3 / C-4

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3. Technical requirements for a “direct economic loss indicator” An indicator is a number that gives an indication of the size of certain phenomena, in this case it estimates the value of direct economic losses that occur in each disaster. It is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure of economic losses. It would be impossible to get rid of certain level of inaccuracy from direct economic loss estimations, depending on the methodology and criteria used to assign monetary value to the assets damaged or destroyed and the exhaustiveness of the data collection. In this sense, the loss estimated is always an approximate value (a “proxy”). The indicators to measure direct economic losses for the Sendai Framework aim to meet following important criteria: Consistent over time: The target requires the comparison of losses of two different decades, the decade of the Hyogo framework (2005-2015) and the last decade of the Sendai Framework (20202030). Monitoring the losses, nevertheless, should occur throughout the entire period of 25 years in order to obtain a continuous view of the progress of implementation and achievements, and data must be recorded and reported in a consistent way for the cycle of measurement, and so avoiding introducing biases. Consistent across countries: It must be a) applicable to any country in the world, to the maximum degree possible, allowing comparison among countries or regions, and b) feasible, such that data can be obtained regardless of the level of development or income of each country. SMART: Specific, Measurable, Achievable, Relevant, Time Bound. Reliable: Results can be trusted and if possible have a measure of dispersion, and for which a particular uncertainty measure can be determined. Transparent: The methodology used is well known, with caveats declared, and for which weaknesses, limitations and strengths, including economic assessment biases, are identified. Verifiable: The estimated economic value can be traced back to the original indicators of damage. Feasible: Easy to collect data in a practical and realistic way, without imposing an extraordinary or even impossible burden on countries. Taking advantage of existing data: Many countries have already collected standardized data. Taking advantage of this fact is more practical than having everyone start from zero. Can be refined/improved over time: when better information is made available, or improved methodologies are developed, the economic estimation can be revised to reflect the improvement. Useful: Results can be used not only for measuring the achievement of targets but also for risk knowledge, awareness raising, risk assessments and the development of DRR and related policies. 41

4. List of Proposed Indicators to Measure Target C: The following indicators are taken from the Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. [C-1 - Direct economic loss due to hazardous events [in relation to global gross domestic product.] (This indicator should be computed based on indicators C-2 to C-9 and GDP figures). C-2 - Direct agricultural loss due to hazardous events. (The indicator measures (1) crops (estimated by agricultural land), [and ] (2) livestock[, (3) fisheries and (4) forestry.]) C-3 - Direct economic loss due to industrial facilities damaged or destroyed by hazardous events. Note: Countries are required to report number of industrial facilities damaged or destroyed. C-4 - Direct economic loss due to commercial facilities [and services] damaged or destroyed by hazardous events. Note: Countries are required to report number of commercial facilities damaged or destroyed. [C-5 - Direct economic loss due to houses damaged by hazardous events] [C-6 - Direct economic loss due to houses destroyed by hazardous events] [C-7 - Direct economic loss due to damage to [critical infrastructure / public infrastructure] caused by hazardous events.] (This indicator should be computed based on indicators D-2, D-3 and D-4 (road)). [C-8 – Direct economic loss due to cultural heritage damaged or destroyed by hazardous events.] [C-9 – Direct economic loss due to environment degraded by hazardous events.] [C-10 – Financial transfer and access to insurance.] Proposal by the Secretariat: C-10 alt. - Total insured direct losses due to hazardous events. [C-11 – Direct economic losses due to disruptions to basic services.] [C-12 – Direct economic loss due to services sectors (such as transportation, tourism, finance) caused by hazardous events.] Member states made the following additional proposals during the Second Session of the OEIWG. These proposals were sent via email and were not introduced from the floor: [C-2a – Damage and loss on education.] [C-2b – Damage and loss on health.] [C-2c – Damage and loss on nutrition.] 42

[C-2d – Damage and loss on the habitat.] [C-3a – Damage and loss on agriculture.] [C-3b – Damage and loss on livestock and livestock production.] [C-3c – Damage and loss on fishing and fishery resources.] [C-3d – Damage and loss on industry.] [C-3e – Damage and loss on trade.] [C-3f – Damage and loss on tourism.] [C-4a – Damage and loss on energy.] [C-4b – Damage and loss on transport.] [C-4c – Damage and loss on telecommunications.] [C-4d – Damage and loss on water, sanitation and hygiene.] [C-5a – Damage and loss on environment and forests.] [C-5b – Damage and loss on administrative buildings.] [C-5c – Damage and loss on patrimony.] [C-13 – Total of risk informed investments relative to Gross Domestic Product.] [C-14 – Number of micro enterprises affected.] [C-15 – Number of small and medium enterprises affected (registered enterprises) – sales drop, production drop, profit drop, direct damage to facilities etc.]

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5. Applicable Definitions and Terminology Sendai Framework Target C specifically requires “direct economic loss” to be estimated. For the purposes of this methodology, the term “Direct economic loss” and related key terms, unless otherwise stated are those defined in the Working Text on Terminology based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016. Direct economic loss: The monetary value of total or partial destruction of physical assets existing in the affected area. Annotation: Examples of physical assets include homes, schools, hospitals, commercial and governmental buildings, transport, energy, telecommunications infrastructures and other infrastructure; business assets and industrial plants; production such as standing crops, agricultural infrastructure and livestock. They may also encompass environment and cultural heritage. Alt. Annotation: Direct loss is nearly equivalent to physical damage. Examples include loss to physical assets such as damaged housings, factories and infrastructure. Direct losses usually happen during the event or within the first few hours after the event and are often assessed soon after the event to estimate recovery cost and claim insurance payments. These are tangible and relatively easy to measure. Direct Economic loss in this indicator framework consists of agriculture loss, damage to industrial and commercial facilities, damage to housings and critical infrastructures.] Economic loss: Total economic impact that consists of direct economic loss and indirect economic loss. Annotation: Direct and indirect economic loss are two complementary parts of the total economic loss. [Indirect economic loss: Declines in value added as a consequence of direct economic loss and/or human and environmental impacts. Indirect economic loss is part of disaster impact. Annotations: Indirect economic loss includes micro-economic impacts (e.g. revenue declines owing to business interruption), meso-economic impacts (e.g. revenue declines owing to impacts on a supply chain or temporary unemployment) and macro-economic impacts (e.g. price increases, increases in government debt, negative impact on stock market prices, and decline in GDP). Indirect losses can occur inside or outside of the hazard area and often with a time lag.] Replacement cost: The cost of replacing damaged assets with materials of like kind and quality. Annotations: This includes both private and public assets. Replacement is not necessarily an exact duplicate of the subject but serves the same purpose or function as the original (not taking into account building back better.

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6. Computation methodology: Direct economic loss using replacement cost approach The methodology proposed is the conversion of physical damage value into economic value using replacement cost to monitor direct economic loss. The methodology is consistent with DALA and PDNA methodology. The methodology consists of the following three steps. We identify challenges in each step. Step 1: Collect good quality of data, ideally disaggregated, on physical damage per hazardous event. Step 2: Apply replacement cost per unit to estimate economic value Step 3: Convert the economic value from the one expressed in national currency into the one expressed into US dollars With difference in details, the basic formula common to all indicators is as follows: Direct economic loss = (a) Number of physical assets affected (e.g. number of facilities damaged) * (b) Size of the physical assets * (c) Unit Cost (e.g. per square meters, per kilometres, per hectare) As the formula shows, it is required to collect three critical data for estimation. In Step 1, the data (a) is collected from national disaster loss databases. In step 2, the data (b) and (c) are collected mainly from disaster loss databases or national socio-economic statistics. In case the data does not exist, it is suggested to be estimated using global methodology. (a) Number of physical assets damage: The data is collected and reported from national disaster loss database. The level of disaggregation will enhance the accuracy of economic loss estimation while increasing the data collection burden. Several options are suggested in the section of each indicator. (b) Size of physical assets: The most accurate estimate is possible if countries collect and report data on individual size of physical assets affected on each hazardous event. However, this involves a huge effort on data collection which is believed not feasible nor practical. Countries are recommended to provide as proxy average size of physical assets (e.g. average size of housing, average size of commercial facility, average weight of livestock, average). Usually such data are found in official statistics or other statistics compiled by sectoral ministries. For example, average size of housing data can be often found in housing statistics. In some cases, instead of average, using the median (middle value in the data set) or mode (the value most often observed in the data set) might be appropriate. If countries additionally report “distribution of assets by certain category” (e.g. type of crops, size category), weighted averages can be also proposed. 45

When countries cannot provide data from their related socio-economic statistics, as the last resort, UNISDR proposes the use of global data, or application of methodology based on the work from Global Assessment Report. (c) Unit cost: As the majority of countries will collect only the number of facilities affected, countries are recommended to provide a proxy construction cost per unit (e.g. housing construction cost per square meter, school construction cost per square meter). If the asset is public assets, usually ministries in charge of the public asset have the data. For example, Ministry of Public Work would have standard road construction cost per kilometre. In case of private assets such as industrial facility, it is more difficult to find such data. However, related ministries or association of construction business are likely to have the data. It is of note that construction cost per unit is usually different across sectors (e.g. industrial vs housing) and within sectors (urban vs rural, industrial sector, building structure). While enhancing reporting these details will significantly improve accuracy of loss estimate, it may raise the costs required to obtain this information. When countries cannot report data based on available socio-economic statistics, the Secretariat proposes the use of global data, or the application of the methodology developed for the Global Assessment Report (See Annex I). For element (c), ideally, a matrix similar to the one below should be completed.

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Table: Suggested MINIMUM REQUIREMENT: proxies to be provided by countries for Step 2 of C3 to C7 indicators (The number and data source filled in is a sample value to show the image of reporting.) Type of buildings

average size of facilities (m2) (a)

Industrial (for C3)

construction cost per m2 (b)

Data source

2,000

1,200

(a) Ministry of Economy (b) Application of national proxy formula

700

800

(a) Ministry of Commerce (b) National Construction Association

Housing (for C5 and C6)

55

500

(a) Ministry of Housing (b) Global Compass Data

Health (for C7)

60

800

(a) Application of recommended fixed value (b) Ministry of Health

200

300

(a)(b) Ministry of Education

-

Estimate based on COMPASS 8 data

UNISDR

Commercial (for C4)

Education (for C7) National Proxy (when data is not provided by countries nor global database)

Depending on data availability in each country, and on the level of detail of the actual physical damage data collection, these proxies could be disaggregated to enhance the quality of the estimates. For example, if a country collects disaggregated data on physical damage for housing sector in rural and urban categories, then countries are recommended to provide both sizes and prices corresponding to each category.

Evolution of price over time How to assure proper comparison across time? It is important to distinguish what part of the change in economic loss data stems from a change in the quantities affected and what part is accrued to a change in prices. Let’s suppose the case that the housing loss is worth USD 10,000 in the first year and USD 12,000 in the second. It is important to know if this 20% loss increase is due to an increase in the number of housing affected or to an increase in its price. The price factor, in this case, the construction cost per unit, change across time due to technical development and other market related factors (e.g. price increase of construction material in 8

Global Construction Cost and Reference Yearbook 2012 (produced by Compass International) is a civil engineering publication which contains construction costs per square meter for more than 90 countries obtained with a documented and consistent methodology. This publication is used worldwide by consulting engineering firms to estimate initial budgets of construction projects.

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relation to other goods and services). General Price level change such as inflation will also influence unit price. When the main objective of monitoring direct economic loss is observing the trend of physical damage, whether it is increased or not, it is recommend to use constant price in all the periods, with inflation adjusted. If the main objective is monitoring the impact of disaster loss on overall economy, nominal price should be used and compared with nominal GDP. The percentage of loss to GDP matters and be compared across time. However, these two directions might not be an issue of selecting either one or the other. As long as the original data is collected, it is easy to estimate both. C-1 indicator is expressed in relation to GDP while others are not. It might need alignment between these two types of indicators. Lastly, the loss expressed in national currency needs to be converted into US dollars. As the main objective is not cross-country comparison but global summation, it is suggested to simply use official exchange rate without taking into consideration of Purchasing Power Parities.

C1 – Direct Economic loss due to hazardous events in relation to global gross domestic product Indicator C1 will be calculated as follows. C1 = (C2 + C3 + C4 +C5 + C6 + C7 + C8 + C9) /global GDP Challenge 1: Should price adjustment be added? Options suggested to be considered and discussed: Option 1: The proportion of loss to GDP matters to estimate the possible impact of disaster loss on the global economy. Therefore, the nominal loss and GDP value is suggested to be taken to monitor progress. Option 2: In addition to the proportion of loss to GDP to assume the possible impact of disaster loss on the global economy, the countries might be interested in monitoring trend of direct economic loss. In that case, UNISDR suggests to compare inflation-adjusted loss and GDP values by dividing nominal value by GDP deflator. Challenge 2: Review of summation. It is already expected that building baseline for indicators C3 and C4 would be extremely difficult. Because it is important to monitor industrial and commercial loss, it would be meaningful and important to have these indicators. However, in the headline indicator C1, should we add C3 and C4? 48

Options suggested to be considered and discussed: Option 1: Retain the current formula. Develop methodology to estimate baseline for C3 and C4. Option 2: Drop C3 and C4 from the current formula. In this case, it is of note that the resulting value would significantly underestimate the loss to industrialized developed countries.

C2 – Direct agricultural loss due to hazardous events From 347,000 records in the 85 national databases analysed in GAR 2015, 26% (91,686) register quantitative indicators (expressed as number of hectares of crops affected and livestock lost) or qualitative (yes/no indicator) about the existence of direct damages to the agricultural sector. Most of agricultural damage (98.5%) is associated to weather-related hazards. Three disaster types, namely flood, drought and forest fire, represent 82 % of the damages with a total of more than 209 million of hectares affected. The importance of agricultural loss due to disasters is undeniable, especially when looking at accumulated impact of small scale but frequent events. This indicator can be calculated based on two indicators, one for crop loss (C2a) and the second for livestock losses (C2b):

C2 = Direct agricultural loss due to crops affected + Direct agricultural loss due to livestock lost The physical damage data that countries will be requested to collect are: C2a=the number of hectares of crops affected C2b=the number of livestock lost These are usually reported by emergency management authorities or ministries of agriculture and are the most available data in disaster reports, especially in small and medium scale disasters.

C2-1 Direct agricultural loss due to crops affected (damaged or destroyed) The general formula proposed is: Loss on crops = number of hectares affected (C2a) * direct cost per hectare * damage_ratio It is proposed that direct costs per hectare (which are very difficult to obtain) would be estimated using crop output. Output is, simply said, price per unit times quantity (yield). Price consists of three elements: variable cost, fixed cost and profit. Cost of crops (direct losses) should include variable cost such as labour and machinery operating costs, costs of raw ingredients, including seeds, fertilizer and pesticides and fixed costs such as damage to productive soil, irrigation infrastructure, machinery and equipment, storage infrastructure, and damages to stored fertilizers and seed. As it can be seen, the methodology simplifies the calculation of all these elements as they are all included in the output. 49

Thus, a more specific formula proposed is: Loss on crops = number of hectares affected (C2a) * average crop output per hectare * 0.25 Where: Average crop output per hectare = average yield per hectare * price per ton Step 1: Collect good quality of data, ideally disaggregated, on physical damage The minimum requirement data proposed to estimate direct loss in crops is: C2a=Number of hectares of crops affected (damaged or destroyed) Challenges: a) Agricultural losses are not recorded as thoroughly as other losses such as human related loss or housing damage and destruction. Further involvement of authorized data sources for all hazardous events will increase the coverage, and thus the reliability of the indicator. b) Disaster loss databases don’t record, with a few exceptions, the type of crops damaged. Additional efforts to capture for each hazardous event the number of hectares affected per type of crop will be beneficial, but will introduce additional workload and complexity for data collection. c) Disaster loss databases don’t record the level of affectation. Additional efforts to capture for each hazardous event the level of damage as a percentage (or simply dividing into partially damaged and totally destroyed crops) would be beneficial. d) Collecting separately other physical damages, such as those to irrigation and equipment could result in better measurements. However, introducing more sub-indicators may pose additional challenges of comparability and the possibility of consolidation. e) Damage to crops is also very dependent of the growth cycle of the crop. Damage varies depending on the intensity of the hazard but also on how early or late in this cycle the disaster hits the crops. For example, FAO (2012) introduces “At various stages of growth, the estimated reduction in harvest per hectare of a specific crop caused by, say, floods can be varied. For instance, a flood that will submerge newly planted taro for 2 to 3 days may cause a 100% reduction in harvest while the same flood may cause only a 50% reduction in harvest of taro at maturing stage.” f) Currently the national disaster loss database compiles forest area damage associated with forest fire. In GAR 2015, losses associated to forests damaged were priced same as farmland. However, forest area losses may be very different from crop losses, therefore it is suggested that losses of forest fires pricing be reviewed, and/or kept separated from agricultural losses. The GAR consolidated database for 82 countries has 253,035,883 hectares lost, about 10% of which (23,003,834 hectares) were forests/grasslands.

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Options suggested to be considered and discussed: Given the benefit and cost of collecting further data, the scope of loss data collection should be decided by countries. Step 2: Apply average output per hectare to estimate direct crop losses As mentioned earlier, with few exceptions, the type of crop damaged is not recorded. The price “producer price per ton” is of course not equal for all crops in a country, and can be very different by country. For example, in El Salvador, the producer price per ton is 30 times higher for green coffee than oranges (USD 4,160 per ton for green coffee, USD 132 per ton for oranges). For GAR 2015, UNISDR devised a methodology to evaluate farmland damage that aims at designing a proxy value for crop losses using publicly available datasets from FAO Statistics, which may also be obtained nationally in ministries of Agriculture. At first, a weighted average agricultural output per hectare (Aoha) of all types of crops is recommended to be calculated per country based on the three variables. Only crops for which all variables are available are taken into account (in most cases, all three are available).

Aoha = ∑ (

𝐴𝑟𝑒𝑎𝑖 ∗𝑌𝑖𝑒𝑙𝑑𝑖 ∗𝑃𝑟𝑖𝑐𝑒𝑖 𝑇𝑜𝑡𝑎𝑙 𝐴𝑟𝑒𝑎

)

Where: 𝐴𝑟𝑒𝑎𝑖 is the total area planted of each crop type i 𝑌𝑖𝑒𝑙𝑑𝑖 is the yield per hectare for crop type i (expressed in ton) 𝑃𝑟𝑖𝑐𝑒𝑖 is the producer price per ton for crop type i Annual Producer Prices or prices received by farmers for primary crops as collected at the farm-gate or at the first point of sale (based on FAO definition) Then, this approach suggests to multiply a conservative percentage (25%) to the output under normal conditions to derive direct loss per damaged hectare (UNISDR, 2015c). The first reason to apply 25% is that the affected farmland does not necessarily imply total crop destruction. The second, much minor reason compared to the first reasons is that cost (variable cost + fixed cost) can be estimated as the total price minus profit. Profit is regarded as indirect loss, it should be excluded. However, profit margin of agriculture is not very high in many countries. Even in the US, 70% of farmers have less than 10% profit margin9. Lastly, Aoha x 25% is multiplied by C2a to derive total agriculture crop direct loss.

9

http://www.ers.usda.gov/amber-waves/2015-januaryfebruary/profit-margin-increases-with-farm-size.aspx#.Vjb6y_kvfIU, accessed as of 3 November 2015.

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Challenge 1: a) Determining the direct cost per type of crop and per hectare is extremely difficult given the lack of sources of information and the diversity of crops and agricultural technologies, from pure manual to highly mechanized. Options suggested to be considered and discussed: Option 1: Countries report three variables (the total area planted for each crop type, the average yield per hectare for each crop type and the producer price per ton for each crop type). It is expected that ministries of Agriculture will be able to supply the required statistical data for the Sendai Framework targets and indicators to enhance the quality and accuracy of the estimate. Option 2: Utilize global data from FAO statistics (http://faostat.fao.org/). It is suggested to utilize data only when three variables are available (usually the most common). The caveat is missing statistical data: Unfortunately the FAO statistics coverage is not global, and in several countries is not complete, i.e. not exhaustive in terms of types of crops. Complementary method for both options 1 and 2: For those countries for which these statistics are not available, UNISDR designed a method which extrapolates a good proxy indicator for the producer price by using a set of regressions of known prices against GDP per capita. To further improve the methodology, UNISDR grouped countries by income groups using World Bank’s income group classification (high income (OECD), high income (non-OECD), upper middle income, lower middle income and low income). The calibration via GDP per capita plus income groups leads to results that go from USD 6,875/ha (y = 0.0344x + 3051.3) for high income (OECD) countries to USD 720 /ha (y = 0.6891x + 565.8) for low income countries. This method gives a proxy price for all countries with missing FAO data. Challenge 2: Direct losses as a percentage of output: The percentage chosen (25%) is an expert criteria based on different factors. If more information on damage level and general profit margin is available, the ratio can be refined to enhance the quality of estimate. Challenge 3: How to assure proper comparison across time? The agriculture output will change in terms of volume and price due to different reasons from disasters. Technical development will increase the yield per hectare. Price level changes such as inflation will influence unit price. Technical development or other factors in agriculture product market will influence relative price of agriculture product higher or lower compared to other goods and services. Should the methodology apply nominal price per unit or the same unit price for all period?

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Options suggested to be considered and discussed: Option 1: The relative unit price increase of agricultural goods in relation to other goods and services indicates the increased influence of agriculture loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030. Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

C2-2 Direct agricultural loss due to livestock lost It is proposed that total price to producer of livestock lost would indicate the direct agricultural loss due to livestock. The price10 per livestock is, simply said, price per kilo times weight of livestock. The general formula proposed is: Loss on livestock = number of livestock lost * average weight per animal * average price per kilogram For the purposes of assessing the direct losses in livestock, it is necessary to convert headcount of livestock to total weight of meat taken from livestock and multiply it by average price per kilo. Step 1: Collect good quality of data, ideally disaggregated, on physical damage National disaster loss databases typically record losses of 4-legged animals such as goats, sheep, cows, buffalos and horse. The minimum requirement data proposed to estimate direct loss to livestock is C2b=Number of livestock lost

10

the concept of price here is equivalent to the concept of “output” in economic theory.

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Challenges: a. Livestock losses are not recorded as thoroughly as other losses such as human related loss or housing damage and destruction. Further involvement of authorized data sources for all hazardous events will increase the coverage and thus the reliability of the indicator. b. Disaster loss databases do not record, with a few exceptions, the type of livestock damaged. Additional efforts to capture the number of livestock lost per type of livestock will be beneficial, but will introduce additional workload and complexity for data collection. In particular, losses to poultry could be recorded separately in order to measure the economic value of these losses; which is very important in terms of livelihoods. See note below in the section Options to address this challenge. c. Collecting separately other physical damages, such as those to farm equipment could result in better measurements. However, introducing more sub-indicators may pose additional challenges of comparability and the possibility of consolidation. d. Damage to livestock is also very dependent of the growth cycle of the livestock. Damage varies depending on how early or late in this cycle the disaster hits the livestock. Options suggested to be considered and discussed: Given the benefit and cost of collecting further data, the scope of livestock loss data collection should be decided by countries. During the Second Session of the OEIWG, several member states proposed that small livestock such as poultry - should also be taken into account. This methodology can accommodate all sizes of livestock. However, the number of poultry lost may introduce unwanted biases, as although the number of poultry lost is often high, its weight is small. If countries desire a much more precise measure of losses - especially if countries wish to include Poultry losses - then indicator C2b should be split in three categories as follows: 𝑪𝟐𝒃𝟏 : 𝑪𝟐𝒃𝟐 : 𝑪𝟐𝒃𝟑 :

Number of large livestock lost (cows, horses, camel, buffalo, etc, 4 legged weight >200Kg.) Number of medium size livestock (sheep, goat, etc., all other not in 𝑪𝟐𝒃𝟑 and 𝑪𝟐𝒃𝟏 ) Number of poultry lost (chicken, duck, etc, 2 legged, feathered)

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Step 2: Apply average price per kilo and average weight per livestock to estimate economic value As in the case of agricultural crops the economic value of these animals has high variance in terms of price per kilo and number of kilos per animal, which in general determines its value. In order to obtain and average price per kilo, if data is available, a weighted average could be used. Average price of livestock (i.e. price of one animal) to producer per kilo (Apkg) is

Apkg =

∑(𝑆𝑡𝑜𝑐𝑘𝑖 ∗𝑊𝑒𝑖𝑔ℎ𝑡𝑖 ∗𝑃𝑟𝑖𝑐𝑒𝑖 ) ∑(𝑆𝑡𝑜𝑐𝑘𝑖 ∗ 𝑊𝑒𝑖𝑔ℎ𝑡𝑖)

i=1...n

Where: 𝑆𝑡𝑜𝑐𝑘𝑖 is the headcount number of livestock type i (ex. 1 million cows or goats) 𝑊𝑒𝑖𝑔ℎ𝑡𝑖 is the average weight of livestock type i (ex. 350 kg per cow) 𝑃𝑟𝑖𝑐𝑒𝑖 is the producer price per kilo for meat of livestock type i (ex. 10 USD per kilo of beef) If data is not available, it is suggested a simple average of producer price per kilogram (Apkg) be calculated. The simple average can be calculated as

Apkg = (∑ Price_i)/n

i=1...n

Where: Price_i is the producer price per kilo for meat live weight of livestock type i

n is the number of livestock type in a country Accuracy of the estimation can be greatly improved using an average weight, but it requires the existence of livestock data in the country. The average weight can also be calculated as a weighted average:

AwL =

𝑆𝑡𝑜𝑐𝑘𝑖 ∗𝑊𝑒𝑖𝑔ℎ𝑡𝑖

∑(

𝑇𝑜𝑡𝑎𝑙 𝑆𝑡𝑜𝑐𝑘

)

Where: 𝑆𝑡𝑜𝑐𝑘𝑖 is the headcount number of livestock type i in the country

𝑊𝑒𝑖𝑔ℎ𝑡𝑖 is the average weight of livestock type i Total Stock is the total headcount number of all types of livestock n the county Note that if losses in livestock are to be categorised in three sizes (large, medium and poultry) then it will be necessary to also calculate averages per category. Price and weight can also be potentially determined as the simple average, median or the mode of the prices and weights.

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Therefore the final formula would look like: Loss on livestock = C2b * AwL * Apkg Where: C2b is the number of livestock lost. If losses are to be recorded separately by type of livestock: Loss on livestock = 𝑪𝟐𝑩𝟏 ∗ 𝑨𝒘𝑳𝟏 ∗ 𝑨𝒑𝒌𝒈𝟏 + 𝑪𝟐𝑩𝟐 ∗ 𝑨𝒘𝑳𝟐 ∗ 𝑨𝒑𝒌𝒈𝟐 + 𝑪𝟐𝑩𝟑 ∗ 𝑨𝒘𝑳𝟑 ∗ 𝑨𝒑𝒌𝒈𝟑 Where: 𝑪𝟐𝒃𝟏 : Number of large livestock lost (cows, horses, camel, buffalo, etc.) 𝑪𝟐𝒃𝟐 : Number of medium size livestock (sheep, goat, etc.) 𝑪𝟐𝒃𝟑 : Number of poultry lost (chicken, duck, etc) AwL1, AwL2, AwL3 are average weight per animal on each category Apkg1, Apkg2, Apkg3 are average price to producer per kilo on each category Challenge 1: a) Determining the price per kilo of meat of livestock is difficult given the lack of sources of information. Options suggested to be considered and discussed: Option 1: Countries report the number of livestock per type, average meat prices per kilogram and average livestock weight. It is expected that ministries of Agriculture will be able to supply the required statistical data for the Sendai Framework targets and indicators to enhance the quality and accuracy of the estimate. Option 2: Utilize global data from FAO statistics (http://faostat.fao.org/). It is suggested to utilize this data only when data for most meat types are available. To calculate average price of meat using the 2011 FAO datasets, the following variable is used: Producer price per ton in USD per type of livestock, which is defined as “Annual Producer Prices or prices received by farmers for live animals and livestock primary products as collected at the farm-gate or at the first point of sale.” (FAO). For GAR 2015, in order to obtain one unique value per country, the average producer price per ton has been calculated. For Bulgaria, the average price per ton is USD 2,215.35 with a maximum of USD 3,464.7/ton for sheep and USD 1,572.3/ton for Buffalo (FAO, 2011). An average price per ton in USD (at 2011 price) is obtained for 82 countries, ranging from USD 746/ton for Slovak Republic to USD 8,735.85/ton for Japan. The caveat is missing statistical data. Unfortunately, the FAO statistics coverage is not global, and in several countries is not complete, i.e. not exhaustive in terms of types of livestock. 56

Option 3: Complementary methods for options 1 and 2: There are, however, several countries for which these statistics are not available in national sources, nor in FAO. To extrapolate a proxy for the price of meat for such countries, UNISDR conducted a set of FAO data regressions against GDP per capita and produced proxy values which allow estimation of livestock loss. Countries can be grouped by income groups from the World Bank income group classification (high income (OECD), high income (non-OECD), upper middle income, lower middle income and low income). The calculation for missing FAO data using calibration via GDP per capita plus income groups leads to results that go from USD 424/100 kg (y = 0.0022x + 179.78) for high income (OECD) countries to USD 73/100kg for low income countries (y = 0.3439x - 4.5952). The regression using the equations per income groups calibrated with GDP per capita gives an artificial price for all countries with missing FAO data.

Challenge 2: The average weight per livestock type is an extremely important element in the estimation of direct loss of livestock. However, the global data by country does not exist. There are several alternatives as follows: Options suggested to be considered and discussed: Option 1: Countries report the average weight per livestock. It is expected that ministries of Agriculture will be able to supply the required statistical data for the Sendai Framework targets and indicators. Option 2: Utilize FAO data in countries where it is provided, and in those countries not covered by FAO statistics, use a world weighted average of weight based on other countries for which data is available. Option 3: Use the GAR 2015 average size of 75 Kg per animal. The weight is an expert criteria based on different factors. Challenge 3: How to assure proper comparison over time? The agriculture output will change in terms of volume and price due to different reason from disasters. Technical development will increase the output per unit. Price level change such as inflation will influence unit price. Technical development or other factors in agriculture product market will also influence relative price of agriculture product higher or lower compared to other goods and services. Options suggested to be considered and discussed: Option 1: The relative unit price increase of agricultural goods in relation to other goods and services indicates the increased influence of agricultural loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time.

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Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

C3 – Direct Economic loss due to industrial facilities damaged or destroyed by hazardous events The methodology proposed here to evaluate damage to industrial facilities is also a broad simplification of the DALA/PDNA methodology which suggests that basic estimation would take into account the area of the affected premises, the construction cost per square meter and the estimated value of equipment and products (raw materials and finished product) stored in these premises. The data are usually reported by emergency management authorities and/or ministries of economy. The general formula proposed is:

C3 = Loss = Number of affected facilities * average size of the facilities * construction cost per square meter * affected ratio Step 1: Collect good quality of data, ideally disaggregated, on physical damage The size of industrial and manufacturing facilities can have large variations in terms of construction cost. The ECLAC handbook suggests three typologies based on number of employees: large establishments employing 200 workers or more; medium-sized establishment employing between 199 and 40 workers; and small establishments employing 39 or fewer workers Depending on availability of data countries can collect information on physical damage with increasing levels of detail. The minimum requirement would be to collect data on total number of affected industrial facilities (Option 1 below) and the maximum level of detail would be to collect separately the damage level and size category of facility (Option 4). There could be intermediate levels of data collection:

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Option 1: Total number of facilities damaged or destroyed is collected and reported. (Minimum Requirement) Option 2: The number of facilities damaged and destroyed are collected and reported separately. Option 3: The number of facilities damaged or destroyed is collected and reported by each category of size (i.e. number of large industrial facilities damaged/destroyed, number of medium facilities damaged/destroyed, number of small facilities damaged/destroyed). Option 4: The number of facilities affected is collected reported separately by damaged or destroyed and by each category of size. Table: Damage data collection and reporting options Size

Damaged

Destroyed

Affected (damaged or destroyed)

Small facilities

Option 4

Option 4

Option 3

Medium facilities

Option 4

Option 4

Option 3

Large facilities

Option 4

Option 4

Option 3

Total number

Option 2 strongly recommended

Option 2 strongly recommended

Option 1 MINIMUM REQUIREMENT

Step 2: Apply replacement cost per unit to estimate economic value Challenge 1 UNISDR could not find the global data on the average size of industrial facility and construction cost per square meter. The country is recommended to report information on the average size of facility and construction cost per square meter, if possible, for each size category. If the reporting of size and price information is not possible, several alternatives are suggested below. Each subsequent alternative involves more work and challenges in the data collection but provides a more accurate estimation of the losses. Options suggested to be considered and discussed Option 1: (MINIMUM REQUIREMENT) Total number of facilities damaged or destroyed is reported. C3a: number of industrial facilities damaged or destroyed Loss =

C3a * average size of the facilities * construction cost per square meter * affected ratio 59

Where: average size of the facilities can be: - The average size of facilities in the country (if reported by the country). - The median or mode of the sizes of facilities in the country. (if reported by the country) - A fixed value defined on the design of a very small and conservative Industrial facility, for example 100 square meters construction cost per square meter can be : - The average value of construction cost per square meter nationally (if reported by the country) - Application of the formula for housing construction cost per square meters. affected ratio: calculated from the estimated percentage of damaged facilities out of total damaged/destroyed facilities. Assuming 20% of the industries reported are totally destroyed and the rest (80%) suffered some degree of damage (suggested to be estimated the same as in the housing sector, 25%), then the overall affected ratio would be the composite of 100% damage for 20% of premises plus 25% damage to 80% of premises, 40%: Option 2: The number of facilities damaged and destroyed are reported separately C3b: number of industrial facilities damaged C3c: number of industrial facilities destroyed Loss =

C3b * average size of damaged facilities * construction cost per square meter * damage ratio + C3c * average size of destroyed facilities * construction cost per square meters

Where: damage ratio: The percentage of the total value of the premise that would represent the damage, suggested to be the same as in the housing sector, 25% Average size of damaged facilities, construction cost per square meter: Same method used as the option1. Note for damage ratio: Ideally, damage ratio (0-100%) and size (m2) of each facility affected is collected and reported separately. In this case total damage would be estimated as: C3=∑ (𝑆𝑖𝑧𝑒𝑖 ∗ 𝐷𝑎𝑚𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜𝑖 ∗ 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑠𝑞𝑢𝑎𝑟𝑒 𝑚𝑒𝑡𝑒𝑟𝑠) for Industries facilities affected i=1...n

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Option 3: The total number of facilities damaged or destroyed is reported by each category of size (i.e. number of large industrial facilities damaged/destroyed, number of medium facilities damaged/destroyed, number of small facilities damaged/destroyed). C3d: number of Large industrial facilities damaged or destroyed C3e: number of Medium industrial facilities damaged or destroyed C3f: number of Small industrial facilities damaged or destroyed Loss = C3d * average size of large facilities * construction cost per square meters * affected ratio + C3e * average size of medium facilities * construction cost per square meters * affected ratio + C3f * average size of small facilities * construction cost per square meters * affected ratio where Average size is specified for each size range. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method. Affected ratio would be same as in Option 1. Option 4: The total number of facilities damaged or destroyed is reported separately by each category of size: C3g: number of Large industrial facilities damaged C3h: number of Medium industrial facilities damaged C3i: number of Small industrial facilities damaged C3j: number of large industrial facilities destroyed C3k: number of Medium industrial facilities destroyed C3l: number of Small industrial facilities destroyed Loss =

C3g * average size of large facilities* construction cost per square meter * damage ratio + C3h * average size of medium facilities * construction cost per square meter * damage ratio + C3i * average size of small facilities * construction cost per square meter * damage ratio + C3j * average size of large facilities* construction cost per square meter + C3k * average size of medium facilities * construction cost per square meter + C3l * average size of small facilities* construction cost per square meter 61

Where: Average size is specified for each size range. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method. Damage ratio would be same as in Option 2. More sophisticated approaches can be devised (for example using types of industries) that could make the estimation more accurate, but would exponentially increase the burden of data collection in countries. Methodologies that could be feasible only in developed, information-rich countries would not be recommended. Challenge 2: How to estimate the overhead of equipment and stored assets? Option suggested to be considered and discussed: As in the case of the Housing Sector (see Indicators C5 and C6) an additional loss has to be assigned corresponding to the value of equipment, associated urban infrastructure and products stored in premises. An overhead of 25% is proposed to be used in the case of industrial facilities. Challenge 3: How to assure proper comparison across time? The construction cost per square meter will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of industrial facility loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030. Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar). 62

C4 –Direct economic loss due to commercial facilities damaged or destroyed by hazardous events As with previous indicators, the methodology proposed for commercial facilities is also a broad simplification of the DALA/PDNA methodology, which suggests that basic estimation would take into account the area of the affected premises, the construction cost per square meter and the estimated value of equipment and products (raw materials and finished product) stored in these premises. The data are usually reported by emergency management authorities and/or ministries of economy or commerce. The general formula proposed is:

Loss = Number of affected facilities * average size of the facilities * construction cost per square meter * affected ratio Step 1: Collect good quality of data, ideally disaggregated, on physical damage In this methodology the term “Commercial Facility” is defined as any building or real estate property that is used for business activities classified in ISIC Code G (wholesale and retail trade) (Rev.4). Commercial properties fall into many categories and include including department store, big shopping centres and malls, super market and individual small shops. It is suggested that when a shopping centre is affected it is reported as the sum of individual shops affected within a shopping centre. While the size of individual shops has a relevant variation, the variance is not as high as the industrial facilities. Except for small number of department store and large supermarkets, the great majority of commercial establishments will fit a more or less uniform pattern in most countries. Therefore, compared to industrial facilities, there is less benefit to collect and report affected facilities by size category at global level. Depending on the desired accuracy of the evaluations countries should collect and report the following possible data: Option 1: (MINIMUM REQUIREMENT) Total number of facilities damaged or destroyed is reported. Option 2: The number of facilities damaged and destroyed are reported separately Option 3: Damage level and size of each facility affected is collected separately.

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Step 2: Apply replacement cost per unit to estimate economic value Challenge 1: Construction cost estimate To estimate the economic value, it is necessary to have information on the average size of facilities and construction cost per square meter. UNISDR could not find the global data on the average size of facility and construction cost per square meters. The country needs to collect and report the information on the size of commercial facilities (average or ideally, affected) and construction cost per square meter (average). It is expected that ministries of Economy will be able to supply the required statistical data for the Sendai Framework targets and indicators. If this is not possible the option is to simply apply construction cost per square meter for housing using formula explained in Annex for each of the options below. There are several alternatives which require different levels of work. The more detailed assessment is possible, however, it means more workload for data collection in Step1. It is estimated that average size of commercial facilities would be 25 square meters (the design of a very small and conservative commercial facility, comprising as one sales area of 4x4 m2 plus storage and miscellaneous usage (washroom, administrative) area of 3x3 m2). To account for the associated urban infrastructure, equipment and product stored in the commercial facility it is proposed to add the same overhead as applied for industrial facilities to this basic cost of 25%. Adding this element raises average size of establishment to 35 square meters. Options suggested to be considered and discussed Option 1: Total number of commercial facilities damaged or destroyed is reported. (MINIMUM REQUIREMENT) C4a - Number of commercial facilities damaged or destroyed by hazardous events

Loss =

C4a * average size of facilities * construction cost per square meter * affected_ratio

Where: Average size of the facilities can be - The average size of facilities in the country (if reported by the country). - The median (middle value in the data set) or mode (the value most often observed in the data set) of the sizes of facilities in the country. (if reported by the country) - A fixed value defined on the design of a very small and conservative commercial facility, for example 35 square meters, see above.

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Construction cost per square meter can be : - The average value of construction cost per square meter nationally (if reported by the country) - Application of the formula for housing construction cost per square meter. Affected ratio: calculated from the estimated percentage of damaged facilities out of total damaged/destroyed facilities. Assuming 20% of the industries reported are totally destroyed and the rest (80%) suffered some degree of damage (suggested to be estimated the same as in the housing sector, 25%), then the overall affected ratio would be the composite of 100% damage for 20% of premises plus 25% damage to 80% of premises, 40%:

Option 2: The number of facilities damaged and destroyed are reported separately C4b - Number of commercial facilities damaged by hazardous events C4c - Number of commercial facilities destroyed by hazardous events The economic loss would be calculated as:

Loss =

C4b * average size of damaged facilities * construction cost per square metre * damage ratio + C4c * average size of destroyed facilities * construction cost per square meter

Where: Damage ratio: The percentage of the total value of the premise that would represent the damage, suggested to be the same as in the housing sector, 25% Average size of damaged facilities, construction cost per square meter: Same method used as the option1. Note for damage ratio: Ideally, damage ratio (0-100%) and size (m2) of each facility affected is collected and reported separately. In this case total damage would be estimated as:

C4=∑ (𝑆𝑖𝑧𝑒𝑖 ∗ 𝐷𝑎𝑚𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜𝑖 ∗ 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑠𝑞𝑢𝑎𝑟𝑒 𝑚𝑒𝑡𝑒𝑟𝑠) for commercial facilities affected i=1...n

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Challenge 2: How to estimate the overhead of equipment and stored assets? Option suggested to be considered and discussed: As in the case of the Housing Sector (see Indicators C5 and C6) an additional loss has to be assigned corresponding to the value of equipment, products stored in premises and associated urban infrastructure. An overhead of 25% is proposed to be used for commercial facilities. Challenge 3: How to assure proper comparison across time? The construction cost per square meter will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of commercial facility loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

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C5 - Direct economic loss due to houses damaged by hazardous events C6 - Direct economic loss due to houses destroyed by hazardous events The methodology proposed here to evaluate damage to the housing is a broad simplification of the DALA/PDNA methodology which suggests that basic economic loss estimation would take into account the sizes of houses, the value of construction cost per square meter and the estimated value of equipment and associated urban infrastructure. It is proposed to estimate direct economic loss of housing damaged and destroyed using the following formula: C5 = number of houses damaged * average size of damaged facilities * construction cost

per square metre * damage ratio Where: damage ratio is 25%. Following suggestions in the DALA/PDNA methodologies the average losses of partially damaged houses is evaluated as 25% of the loss of a completely destroyed house.

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C6 = number of houses destroyed * average size of destroyed facilities * construction cost per square meter Step 1: Collect good quality of data, ideally disaggregated, on physical damage Challenge Given the benefit and cost of collecting further data, the scope of loss data collection should be decided by countries. Options suggested to be considered and discussed: Option 1: Total number of houses damaged and destroyed collected separately (MINIMUM REQUIREMENT). However, housing can have large variations in terms of the size and structure and therefore construction cost though not being as large as industrial and commercial facilities. Option 2: Total number of houses damaged and destroyed collected separately and disaggregated by other criteria such as urban/rural, income level, type of construction structure or other characteristics, when this criteria is relevant for the estimation of the loss. Disaggregated data, for example housing loss by structural type would provide basis for building vulnerability assessment and evidence for strengthening enforcement of building codes or retrofitting policy. Disaggregated data collection could make the estimation more accurate and more usable for policy making, but countries need to be aware it would exponentially increase the burden of data collection.

Step 2: Apply replacement cost per unit to estimate economic value Challenge1 Determining the construction cost per square meter and size of housing affected is extremely difficult given the lack of sources of information and the diversity of housing structure from concrete to wooden barrack. Options suggested to be considered and discussed: Option 1 (highly recommended): Countries report the necessary two variables (i.e. construction cost per square meter, average size of housing in the country). If the disaggregated data is collected, a weighted average of house size in the country taking into account distribution of each size segment (income, structural type or range of size) and the average size of the houses on each segment would increase the reliability of the indicator and would solve to a large extent the issue of choosing a fixed house size. If it is difficult to obtain price information from private market, construction cost of social housing might provide a useful benchmark. It is expected that ministries of housing will be able to supply the required statistical data for the Sendai Framework targets and indicators to enhance accuracy of the estimate.

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Option 2: When the housing construction cost per square meter is missing, it is suggested to utilize global data sources regarding unit cost information. After a review of different sources, we recommend to use “Global Construction Cost and Reference Yearbook 2012” from Compass International to determine the construction cost per square meter. However, unfortunately the COMPASS statistics coverage is not global, and in several countries is not complete, i.e. not exhaustive in terms of types of constructions. To extrapolate a proxy for the unit cost for countries for which no information is available, the following formula is proposed, as explained in the Annex in more detail. 1m2=304 + 0.0118*GDP per capita. Option 3: When the average size is not reported, based on GAR methodology, it is suggested to apply a small ‘social housing solution’ and its associated equipment and urban infrastructure (furniture, water network, power, communications, etc.) as estimation methodology. The concept of a “Social Interest Housing solution “has been used in many types of risk assessments (CIMNE, 2013). It is inspired by the fact that in many cases the state, acting as ultimate insurer of losses especially for the poorest segments of the population, tends to provide homogeneously small housing solutions and/or compensation packages. The concept and size of social housing varies by country. But for the purpose of a homogeneous estimation across countries it is proposed the size of a social housing to be set to 45 square meters – i.e. a very small housing solution. In order to assess the value of the equipment of the house and the additional urban infrastructure associated to loss of houses (such as connection to road networks, water, sewage, green areas, energy and communications infrastructure that usually results damaged in disasters), an additional 40% is proposed to be added to the 45 square meters (CIMNE, 2012), raising the estimated average size of housing to the equivalent of 63 square meters. Challenge 2: How to assure proper comparison across time? The construction cost per square meter will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of housing loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

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Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

C7 – Direct economic loss due to damage to critical infrastructure/public infrastructure caused by hazardous events. (to be calculated based on the following indicators D2 to D4) Proposed indicator C7 is suggested to be calculated based on the indicators D2, D3 and D4 (road only). C7 = the sum of the direct economic loss estimated for indicators D2 to D4 (road only)

D2 - Number of health facilities destroyed or damaged by hazardous events The general formula proposed is:

Loss = C2a = Number of affected facilities * average size of the facilities * construction cost per square meter * affected ratio Step 1: Collect good quality of data, ideally disaggregated, on physical damage Challenge Health facilities range from small clinics, rural health posts and doctor's offices to urgent care centres and large hospitals with advanced emergency rooms and trauma centres. The size of these facilities and replacement costs are varied more than housing sector. Options suggested to be considered and discussed. Additional effort can be invested in collecting disaggregated data per size/type of health facility damaged on each hazardous event. Several categories can be established (regional (large) hospital, local (medium) hospital, health centre, clinic, etc.), with typical sizes and economic replacement values. While disaggregated data collection could make the estimation more accurate, countries need to be aware it would exponentially increase the burden of data collection, which may not justify the additional accuracy of the indicator.

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Depending on availability of data countries can collect information on physical damage with increasing levels of detail (See table below). The minimum requirement would be to collect data on total number of affected health facilities (Option 1 below) and the maximum level of detail would be to collect separately by damaged/destroyed and per size category (Option 4). There could be intermediate levels of data collection: Option 1: Total number of health facilities damaged or destroyed is collected and reported. (MINIMUM REQUIREMENT) Option 2: The number of health facilities damaged and destroyed are collected and reported separately Option 3: The total number of facilities affected (damaged or destroyed) is collected and reported by each category of size (i.e. number of large health facilities damaged/destroyed, number of medium facilities damaged/destroyed, number of small facilities damaged/destroyed) Option 4: The total number of facilities affected is reported separately by damaged or destroyed and by each category of size Table: Physical damage data collection and reporting options Size

Damaged

Destroyed

Affected (damaged or destroyed)

Small health facilities

Option 4

Option 4

Option 3

Medium health facilities

Option 4

Option 4

Option 3

Large health facilities

Option 4

Option 4

Option 3

Option 2 strongly recommended

Option 2 Strongly recommended

Option 1 MINIMUM REQUIREMENT

Total number

Step 2: Apply replacement cost per unit to estimate economic value The DALA/PDNA methodology suggests that the value of the physical damage to the buildings of health facilities can be calculated based on the size of the premise (area), the construction cost per square meter and an overhead to estimate the value of losses in equipment in the premises. Challenge1: It is necessary to have information on the average size of facilities and construction cost per square meter. UNISDR could not find the global data on the average size of facility and construction cost per square meter. The country needs to report the information on the size of facilities and construction cost per square meter, if possible, for each size category. The easier option is to simply apply construction cost per square meter for housing using formula explained in Indicators C6 and Annex for each of the options below. There are several alternatives which requires different level of work. The more detailed assessment is possible however it means more workload for data collection. 71

Options suggested to be considered and discussed: Option 1 (highly recommended): Countries collect and report the two variables (average size of facilities and construction cost per square meter). It is expected that ministries of Health will be able to supply the required statistical data for the Sendai Framework targets and indicators. If not possible, countries are recommended to consider the options below. Option 2: When construction cost per square meter is missing, it is suggested to utilize housing formula as explained in the Annex in more detail. 1m2=304 + 0.0118*GDP per capita. Option 3: When the average size (existing or affected) is not reported, based on GAR methodology, it is suggested to apply a small conservative minimum unit scenario and its associated equipment and urban infrastructure (e.g. connection to water network, power, communications). The idea behind this is that in the developing world health facilities are often small and very inexpensive (GAR 2013). UNISDR recognizes values of minimal size used in the GAR will not likely to apply for developed countries where these facilities tend to be much larger. Size of minimum unit is characterized as a small outpatient clinic consisting of a waiting room of 3x4 meters (12 m2), a consulting room of 3x4 meters (12 m2), an operating/first aid section of 5x4 meters (20 m2), with a medicine depot and maintenance area (4 m2), for a total of 48 m2. In order to assess the value of the equipment of the facility and the additional urban infrastructure associated to loss of facilities (e.g. connection to road networks, water, sewage, green areas, energy and communications infrastructure that usually results damaged in disasters), an additional 25% is proposed to be added to the 45 square meters (CIMNE, 2012), raising the estimated average size of facility to the equivalent of 60 square meters.

Summary Depending on the options taken in Step 1 and 2 above, the following options can be suggested: Option 1: (MINIMUM REQUIREMENT) Total number of health facilities damaged or destroyed is reported. D2a number of health facilities damaged or destroyed

C2a = D2a * average size * construction cost per square meter * affected ratio Where: average size of the facilities can be - The average size of facilities in the country (if reported by the country). - The median (middle value in the data set) or mode (the value most often observed in the data set) of the sizes of facilities in the country. (if reported by the country) 72

- A fixed value defined on the design of a very small and conservative Industrial facility, for example 60 square meters construction cost per square meter can be: - The average value of construction cost per square meter nationally (if reported by the country) - Application of the formula for housing construction cost per square meters. affected ratio: calculated from the estimated percentage of damaged facilities out of total damaged/destroyed facilities. Assuming 20% of the industries reported are totally destroyed and the rest (80%) suffered some degree of damage (suggested to be estimated the same as in the housing sector, 25%), then the overall affected ratio would be the composite of 100% damage for 20% of premises plus 25% damage to 80% of premises, 40%: Option 2: The number of health facilities damaged and destroyed is reported separately. D2b number of health facilities damaged D2c number of health facilities destroyed

C2a =

D2b * average size of damaged facilities * construction cost per square meter * damage ratio + D2c * average size of destroyed facilities * construction cost per square meter

Where: damage ratio: The percentage of the total value of the premise that would represent the damage, suggested to be the same as in the housing sector, 25% Average size of damaged facilities, construction cost per square meter: Same method used as the option1. Note for damage ratio: Ideally, damage ratio (0...100%) and size (m2) of each facility affected is collected and reported separately. In this case total damage would be estimated as:

D2=∑ (𝑆𝑖𝑧𝑒𝑖 ∗ 𝐷𝑎𝑚𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜𝑖 ∗ 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑠𝑞𝑢𝑎𝑟𝑒 𝑚𝑒𝑡𝑒𝑟𝑠) for health facilities affected i=1...n

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Option 3: The total number of health facilities damaged or destroyed is reported by each category of size (i.e. number of large health facilities damaged/destroyed, number of medium facilities damaged/destroyed, number of small facilities damaged/destroyed). D2d number of Large health facilities damaged or destroyed D2e number of Medium health facilities damaged or destroyed D2f number of Small health facilities damaged or destroyed

C2a =

D2d * average size of large facilities* construction cost per square meter * affected ratio + D2e * average size of medium facilities * construction cost per square meter * affected ratio + D2f * average size of small facilities * construction cost per square meter * affected ratio

Where: Average size is specified for each size range. Affected ratio would be same as in Option 1. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method.

Option 4: The total number of health facilities damaged or destroyed is reported separately by each category of size. D2g number of Large health facilities damaged D2h number of Medium health facilities damaged D2i number of Small health facilities damaged D2j number of Large health facilities destroyed D2k number of Medium health facilities destroyed D2l number of Small health facilities destroyed

Loss = D2g * average size of large facilities damaged * construction cost per square meter * damage ratio + + +

D2h * average size of medium facilities damaged* construction cost per square meter * damage ratio D2i * average size of small facilities damaged * construction cost per square meter * damage ratio D2j * average size of large facilities destroyed* construction cost 74

+ +

per square meter D2k * average size of medium facilities destroyed * construction cost per square meter D2l * average size of small facilities damaged * construction cost per square meter

Where: Average size is specified for each size range. Damage ratio would be same as in Option 2. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method. It is clear that more sophisticated approaches can be devised (for example using types of health facility) that could make the estimation more accurate, but would exponentially increase the burden of data collection in countries. Methodologies that could be feasible only in developed, information-rich countries would not be recommended. Challenge 2: How to estimate the overhead of equipment and stored assets? Option suggested to be considered and discussed: As in the case of the Housing Sector (see Indicators C5 and C6) an additional loss has to be assigned corresponding to the value of equipment, stocks in premises and associated urban infrastructure. While the overhead of equipment and stock would be larger in health facilities than in housings, given the lack of information, the same overhead of 25% is proposed to be used for health facilities. Challenge 3: How to assure proper comparison across time? The construction cost per square meter will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of industrial facility loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

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Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

D3 - Number of educational facilities destroyed or damaged by hazardous events The general formula proposed is:

Loss = Number of affected facilities * average size of the facilities * construction cost per square meter * affected ratio Step 1: Collect good quality of data, ideally disaggregated, on physical damage Challenge: Schools range from small rural schools to large universities with similar variances as seen in the health sector, therefore these facilities have a much higher variance than houses in size and therefore in economic value. Depending on availability of data countries can collect information on physical damage with increasing levels of detail. The minimum requirement would be to collect data on total number of affected educational facilities (Option 1 below) and the maximum level of detail would be to collect separately the damage level and size category of facility affected (Option 4). For the purposes of the data collection it is proposed to consider three categories of sizes: - Small schools (up to 100 students), similar to rural schools and other small education and training facilities. - Medium schools (100-700 students), similar to urban elementary or secondary schools - Large educational compounds like university campuses. Options suggested to be considered and discussed: While disaggregated data collection could make the estimation more accurate, countries need to be aware it would exponentially increase the burden of data collection. There could be several options for data collection (see table below):

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Option 1: Total number of education facilities damaged or destroyed is reported. (MINIMUM REQUIREMENT) Option 2: The number of education facilities damaged and destroyed are reported separately Option 3: The total number of facilities affected (damaged or destroyed) is reported by each category of size (i.e. number of large education facilities damaged/destroyed, number of medium facilities damaged/destroyed, number of small facilities damaged/destroyed) Option 4: The total number of facilities affected is reported separately by damaged or destroyed and by each category of size Table: Damage data collection and reporting options Size

Damaged

Destroyed

Affected (damaged or destroyed)

Small education facilities

Option 4

Option 4

Option 3

Medium education facilities

Option 4

Option 4

Option 3

Large education facilities

Option 4

Option 4

Option 3

Option 2 strongly recommended

Option 2 strongly recommended

Option 1 MINIMUM REQUIREMENT

Total number

Step 2: Apply replacement cost per unit to estimate economic value The DALA/PDNA methodology suggests that the value of the physical damage to the buildings of education facilities can be calculated based on the size of the premise (area), the price per square meter of construction and an overhead to estimate the value of losses in equipment in the premises. Challenge1: It is necessary to have information on the average size of facilities and construction cost per square meter. UNISDR could not find the global data on the average size of facility and construction cost per square meter. The country needs to report the information on the average size of facilities and construction cost per square meter, if possible, for each size category. The easier option is to simply apply construction cost per square meter for housing using formula explained in Annex for each of the options below. There are several alternatives which requires different level of work. The more detailed assessment is possible however it means more workload for data collection. Options suggested to be considered and discussed: Option 1 (highly recommended): Countries collect and report the two variables (average size of facilities and construction cost per square meters) from countries. It is expected that ministries of Education will be able to supply the required statistical data for the Sendai Framework targets and indicators. If not possible, countries are recommended to consider the options below. 77

Option 2: When the construction cost per square metre is missing, it is suggested to utilize housing formula as explained in the Annex in more detail. 1m2=304 + 0.0118*GDP per capita. Option3: When the average size (existing or affected) is not reported, based on GAR methodology, it is suggested to apply a small conservative minimum unit scenario and its associated equipment and urban infrastructure (furniture, water network, power, communications, etc.). The idea behind this is that in the developing world school facilities tend to be small and inexpensive (GAR 2013). UNISDR recognizes values of minimal size used in the GAR will not likely apply for developed countries where these facilities tend to be much larger. In order to assess the value of the equipment of the facility and the additional urban infrastructure associated to loss of facilities (e.g. connection to road networks, water, sewage, green areas, energy and communications infrastructure that usually results damaged in disasters), an additional 25% is proposed to be added to the 60 square meters (CIMNE, 2012), raising the estimated average size of facility to the equivalent of 75 square meters. Summary Depending on the options taken in Step 1 and 2 above, the following options can be suggested: Option 1: (MINIMUM REQUIREMENT) Total number of educational facilities damaged or destroyed is reported. D3a number of educational facilities damaged or destroyed

C3a (Loss) = D3a * average size * construction cost per square meter * affected ratio Where: average size of the facilities can be

- The average size of facilities in the country (if reported by the country). - The median (middle value in the data set) or mode (the value most often observed in the data set) of the sizes of facilities in the country. (if reported by the country) - A fixed value defined on the design of a very small and conservative educational facility, for example 75 square meters construction cost per square meter can be: - The average value of construction cost per square meter nationally (if reported by the country) - Application of the formula for housing construction cost per square meters.

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affected ratio: calculated from the estimated percentage of damaged facilities out of total damaged/destroyed facilities. Assuming 20% of the industries reported are totally destroyed and the rest (80%) suffered some degree of damage (suggested to be estimated the same as in the housing sector, 25%), then the overall affected ratio would be the composite of 100% damage for 20% of premises plus 25% damage to 80% of premises, 40%:

Option 2: The number of educational facilities damaged and destroyed are reported separately. D3b number of educational facilities damaged D3c number of educational facilities destroyed

C3a =

D3b * average size of damaged facilities * construction cost per square meter * damage ratio + D3c * average size of destroyed facilities * construction cost per square meter

Where: damage ratio: is the percentage of the total value of the premise that would represent the damage, suggested to be the same as in the housing sector, 25% Average size of damaged facilities, construction cost per square meter: Same method used as the option1. Note for damage ratio: Ideally, damage ratio (0...100%) and size (m2) of each facility affected is collected and reported separately. In this case total damage would be estimated as:

D2= ∑ (𝑆𝑖𝑧𝑒𝑖 ∗ 𝐷𝑎𝑚𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜𝑖 ∗ 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑠𝑞𝑢𝑎𝑟𝑒 𝑚𝑒𝑡𝑒𝑟𝑠) for educational facilities affected i=1...n

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Option 3: The total number of educational facilities damaged or destroyed is reported by each category of size (i.e. number of large educational facilities damaged/destroyed, number of medium facilities, number of small facilities) D3d number of Large educational facilities damaged or destroyed D3e number of Medium educational facilities damaged or destroyed D3f number of Small educational facilities damaged or destroyed

C3a =

D3d * average size of large facilities* construction cost per square meter * affected ratio + D3e * average size of medium facilities * construction cost per square meter * affected ratio + D3f * average size of small facilities * construction cost per square meter * affected ratio

Where: Average size is specified for each size range. Affected ratio would be same as in Option 1. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method.

Option 4: The total number of educational facilities damaged or destroyed is reported separately by each category of size: D3g number of Large educational facilities damaged D3h number of Medium educational facilities damaged D3i number of Small educational facilities damaged D3j number of Large educational facilities destroyed D3k number of Medium educational facilities destroyed D3l number of Small educational facilities destroyed

Loss = D3g * average size of large facilities damaged * construction cost per square meter * damage ratio + + +

D3h * average size of medium facilities damaged* construction cost per square meter * damage ratio D3i * average size of small facilities damaged * construction cost per square meter * damage ratio D3j * average size of large facilities destroyed* construction cost 80

+ +

per square meter D3k * average size of medium facilities destroyed * construction cost per square meter D3l * average size of small facilities damaged * construction cost per square meter

Where: Average size is specified for each size range. Damage ratio would be same as in Option 2. Construction cost per each size category (if reported by country). If not reported, apply the same value to all, based on the option 1 method. It is clear that more sophisticated approaches can be devised (for example using types of educational facility) that could make the estimation more accurate, but would exponentially increase the burden of data collection in countries. Methodologies that could be feasible only in developed, information-rich countries would not be recommended. Challenge 2: How to estimate the overhead of equipment and stored assets? Option suggested to be considered and discussed: As in the case of the Housing Sector (see Indicators C5 and C6) an additional loss has to be assigned corresponding to the value of equipment, stocks in premises and associated urban infrastructure. While the overhead of equipment and stock would be smaller in educational facilities than in housings, given the lack of information, the same overhead of 25% is proposed to be used for educational facilities. Challenge 3: How to assure proper comparison across time? The construction cost per square meter will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of industrial facility loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

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Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

D4 - Number of transportation infrastructures destroyed or damaged by hazardous events Damage to transportation facilities can be extremely complex to record and evaluate. Member States have requested that this methodology take into account the following elements of transportation networks: -

Roads Railways Ports Airports

The data available in disaster loss databases, which is based on a very large number of disaster reports, suggests that roads are the infrastructure that experience the most frequent damage. Large infrastructures like ports, airports and railways that are unlikely to be damaged by extensive events should be reported both as the number of facilities damaged as well as the assessed cost of damage. This is because the economic assessment of direct loss of these facilities cannot be easily expressed in terms of a unit cost (such as length of road or square meter of construction) and because these facilities can be of extremely high value. For ports, airports and railways losses that should be reported also as direct economic costs, it is recommended to use assessed costs (as detailed in the ECLAC / DALA methodology), or produced by expert engineering teams with formal and rigorous methodologies. Damage to roads should be reported, as suggested, in terms of physical damage, i.e. length of roads damaged. The following suggested indicators are divided in two groups, one reporting Physical Damage, and the second, the reported estimated economic assessment of these damages: ▫ ▫ ▫ ▫ ▫

D4a Number of kilometres of road destroyed or damaged per hazardous event. (MINIMUM REQUIREMENT) D4b Number of railway networks damaged D4c Number of ports affected D4d Number of Airports affected D4e Number of bridges affected 82

▫ ▫ ▫ ▫ ▫

C7c Economic value of damages to road networks (calculated from D4a) C7d Economic value of damages to railway networks C7e Economic value of damages to ports affected C7f Economic value of damages to airports affected C7g Economic value of damages to bridges affected

The general formula proposed for loss in roads is: Loss on roads = Number of kilometres affected * average rehabilitation cost per kilometre Step 1: Collect good quality of data, ideally disaggregated, on physical damage For the economic cost estimation of transportation infrastructures damaged, the current UNISDR methodology has proposed to use the following sub-indicator only due to limited data availability for several other transport infrastructures: D4a Number of kilometres of road destroyed or damaged per hazardous event. (MINIMUM REQUIREMENT) It is recommended to collect only the length of roads affected. Other road infrastructure such as bridges are taken into account separately and reported in terms of number of units and the estimated cost of rehabilitation/reconstruction. UNISDR recognizes roads are the most universal basic transportation infrastructure while railways, ports and airports might have global variance in terms of the importance and presence in different countries. Additional effort can be invested in collecting disaggregated data per type of road affected on each hazardous event. Several categories can be established, with three categories suggested for simplicity - Highway paved road, unpaved). For those countries that are interested in having a more precise estimation of losses, the following optional indicators are suggested: ▫ ▫ ▫

D4a1 Number of kilometres of unpaved road destroyed or damaged per hazardous event. D4a2 Number of kilometres of paved road destroyed or damaged per hazardous event. D4a3 Number of kilometres of paved highway roads destroyed or damaged per hazardous event.

Step 2: Apply replacement cost per unit to estimate economic value The DALA/PDNA methodology suggests that the value of the physical damage can be calculated based on the size of the damage and the construction cost per unit. Challenge: Determining the construction cost per kilometre is extremely difficult. 83

Options suggested to be considered and discussed: Option 1: Countries report the average construction cost per kilometre for paved and unpaved roads, and if possible, other related information. It is expected that ministries of Infrastructure or Transport will be able to supply the data for the Sendai Framework targets and indicators. Though UNISDR recognizes classifying roads in paved and unpaved may be too simplistic in countries where the road network is very developed, it is assumed to be the minimum cost information. Option 2: It is suggested to utilize global data from the ROad Costs Knowledge System (ROCKS) developed by the Transport Unit (TUDTR) of the World Bank (accessible at http://www.worldbank.org/transport/roads/tools.htm). The ROCKS Worldwide Database compiles data collected primarily from World Bank financed projects and has more than 1,500 records from 65 developing countries. ROCKS compiles cost estimates for maintenance work (renovation, rehabilitation and improvement of existing roads) and for development work (construction of new roads). Roads are categorized as paved and unpaved. The cost of road rehabilitation is proposed to be a proxy for replacement cost, as most of the work on roads after disasters must be considered as rehabilitation, despite in some cases a full reconstruction of the roads have to be undertaken. Rehabilitation cost is more conservative than development cost. In order to reflect the significant cost difference in cost between paved and unpaved roads (Table below), UNISDR proposes to assume that distribution of road damage on each category would roughly follow the same pattern as the national distribution of roads. It is recommended to use the latest year data published by the World Bank for the percentage of the road network of the country that are paved (“paved ratio” in the formulas below), reported on annual basis (see http://data.worldbank.org/indicator/IS.ROD.PAVE.ZS). The distribution of paved and unpaved roads does not change significantly over the years, and does not justify the additional complexity in the calculation by updating the data annually.

Table: Road related costs (global average costs per km, expressed in the USD of year 2002) PAVED Roads

UNPAVED Roads

Seals USD 20,000 /km

Regravelling USD 11,000/km

Functional Overlays USD 56,000 /km

Improvement USD 72,000/km

Structural Overlays USD 146,000 /km Rehabilitation USD 214,000 /km

Rehabilitation USD 31,000 /km

Construction USD 866,000 /km

Paving USD 254,000/km

Source: World Bank, ROCKS database

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The caveat of ROCKS is missing statistical data: Unfortunately the data coverage is not global. However, ROCKS summarizes the data by World Bank regions. While the averages per region are slightly different, the number of records per region per type of work is not statistically representative enough in certain regions with very few projects; therefore UNISDR proposes to use global averages instead of the regional averages instead of the regional average of rehabilitation costs. The current formula to estimate direct cost of damage using replacement cost is: Loss on roads = ((rehabilitation cost paved per Km*paved ratio)+ (rehabilitation cost unpaved per Km*(1-paved ratio))* Kilometres affected

Challenge 2: How to assure proper comparison across time? The construction cost per kilometre will change across time due to technical development and other market related factors (e.g. price increase of construction material in relation to other goods and services). Price level change such as inflation will also influence unit price. Options suggested to be considered and discussed: Option 1: The relative unit price increase of construction cost in relation to other goods and services indicates the increased influence of road loss on overall economy. Impact of general inflation will be considered in C1 if agreed so. Suggested to use nominal per unit price in each moment of time. To adjust inflation factor, the ROCKS are expressed in 2002 US dollars. UNISDR assumes that relative price of construction materials and other elements for road construction remains stable from the simplicity reason under the current data limitation.

Option 2: Simply to observe affected volume trend, use the same unit price for all the moments from baseline period until 2030.

Step3: Convert the value expressed in national currency into the one in USD and derive global loss value It is recommended to convert the value expressed in national currency into USD by using the official exchange rate at the year of event (Data source: Official exchange rate of the World Bank Development indicator). Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

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7. Notes from the Secretariat on comments and proposals from Member States [C-8 –Direct economic loss due to cultural heritage damaged or destroyed by hazardous events.] The Secretariat recognizes the added value of this indicator, and offers a number of suggestions for modification and implementation, together with caveats. Research conducted by the Secretariat has shown that the value of cultural heritage assets cannot be assessed in simple economic terms, and even less in terms of Direct Economic Loss. Most losses associated with cultural heritage are intangible losses; i.e. associated with the historical and/or artistic value of cultural heritage assets. Also, most economic losses associated with cultural assets are indirect losses, mainly connected to tourism, culture, and recreation. However, in order to calculate at least a portion of the direct economic loss, the following indicators are proposed. For the purpose of assigning a direct economic loss value, a simple division of assets lost in two groups is proposed: one composed of buildings, monuments and fixed infrastructure, and the second composed of ‘mobile’ elements such as art, historical artefacts: C-8a Cost of Rehabilitation or Reconstruction of buildings, monuments and fixed infrastructure of cultural heritage assets C-8b Cost of Rehabilitation or Reconstruction of mobile cultural heritage damaged C-8c Market value of mobile cultural heritage destroyed or totally lost. Along with these economic loss estimations, it is also recommended to record simple measures of physical damage: C-8d Number of buildings, monuments and fixed infrastructures of cultural heritage assets C-8e Number of mobile cultural heritage assets (such as artworks) damaged C-8f Number of mobile cultural heritage assets destroyed The proposed indicators do not measure physical damage (as is the case with other indicators in this technical note), rather they measure the economic costs to be evaluated by experts and on a per case basis. This is a function of the great variation in the value of cultural heritage assets. As for buildings and monuments, it makes no sense to speak of the ‘average’ value per square meter of construction (take for instance, the Colosseum in Rome, or Angkor Wat in Siem Riep, Cambodia, etc.). As for ‘mobile’ artefacts, the number damaged or destroyed is less relevant, given that the value of each artefact must be evaluated on a case by case basis. For example, the value of the Mona Lisa (one artefact) cannot be compared with the value of a painting of a similar size but from a relatively unknown painter. 86

[C-9 – Direct economic loss due to environment degraded by hazardous events] The Secretariat recognizes the added value of this indicator, and offers a number of suggestions for modification and implementation, together with caveats. Although the Secretariat has concerns as to the complexity of the collection of data required to correctly measure these indicators, as well as the burden that this will impose on countries, the impact of a degraded environment, including on the economy, is undeniable. Considering the work of several stakeholders in the environmental field, particularly The Economics of Ecosystems and Biodiversity (TEEB) methodology, the Secretariat has identified a set of potential environment elements affected by disasters, and which could be candidates for data collection. These are: 

Forests (tropical – temperate or boreal)



Grasslands



Coral Reefs



Wetlands (Inland and Coastal wetlands, for example Mangrove, tidal marsh, etc.)



Fragile Coastal areas (Sea grass/algae beds, Estuaries, Continental Shelf Sea)



Fresh water sources (rivers, lakes, pond, etc.)



Marine areas

Measuring losses associated with damage to these types of elements could provide a good ‘proxy’ value, or indicator, of environmental losses. Methodologies for evaluating the value of ecosystems (and related) services are still embryonic, and is important to note that inherent uncertainties and margins of error remain significant. It is also important to note that as is observed in other economic assessments, existing methodologies produce estimations of both direct and indirect economic losses, and all of which concur that indirect losses far exceed the simple value of the assets lost. For example, direct economic losses of forests lost due to wild fires are estimated using the commercial value of timber and wood contained in those forests. If indirect economic losses were to be measured, losses to numerous related services should also be estimated. Ecosystems services provided by forest include but are not limited: carbon storage, production of oxygen, recreational and touristic value, intrinsic support to biodiversity, protection of water sources, reduction of soil erosion, coastal zone protection, production of pharmaceutical products, not to mention many related to livelihoods. It is important to note that Forest Fires (one of the hazards recommended to be recorded separately by the Secretariat) seem to be the cause of the majority of the economic losses registered to date with regard to the environment. Taking into account figures produced by previous research, biomass fires are on average burning 87

between 3 - 4.5 million km2/annum; depending on the period of measurement and sensors used to detect burnt areas. Africa is by far the region most affected. Biomass fires affect both the local and global environment, with impacts to local biodiversity, soil, as well as the global climate as a result of increased Green House Gases production. Until a systematic collection of data is conducted – supported by sound economic valuation methodologies that are designed, tested and endorsed by the scientific community – it will not be possible to determine if the following suggestions are appropriately representative of economic losses as a result of environmental degradation. The Secretariat suggests the collection of a minimal number of indicators of physical damage, which could be converted into economic loss following a standardized methodology. In the future it may be possible to measure or estimate at least some of the indirect losses by taking advantage of these physical damage indicators recorded for the Sendai Framework. The methodology proposed is similar to that proposed to measure agricultural losses. It is based on the proposal from The Economics of Ecosystems and Biodiversity (TEEB), a global initiative focused on “making nature’s values visible”. It follows a structured approach to valuation that helps decision-makers recognize the wide range of benefits provided by ecosystems and biodiversity, and to demonstrate their values in economic terms. The authors of the TEEB Methodology have collected data for all of the 11 biomes considered. For each biome, the TEEB methodology estimates (and provides both data and detailed methodologies used for estimation) 22 services associated with these biomes. As most of these services are to be considered part of indirect losses, the Secretariat initially proposes the use of “Raw Materials” as a proxy for direct economic losses. The Secretariat recommends that this be discussed and validated with both an expert group and the authors of the methodology and database. For each biome, the Secretariat will review this record and will propose a set of services to be considered “direct economic loss”. Therefore, direct economic loss associated with each element = the number of hectares affected multiplied by the sum or the values of the services selected to represent direct economic loss. The following is a list of indicators to be reported by the number of hectares affected of each element. Indented, there are a number of even more detailed, optional indicators that could provide additional precision. C-9a Hectares of Coral Reef affected C-9b Hectares of Forest affected C9b1 Hectares of Forest - Temporate and Boreal forest C9b2 Hectares of Forest - Tropical Forest C-9c Hectares of Grassland affected C-9d Hectares of Wetlands affected 88

C9d1 Hectares of Inland Wetlands affected C9d2 Hectares of Coastal wetlands/Mangrove affected C9d3 Hectares of Coastal wetlands/Tidal marsh affected C-9e Hectares of Coastal area affected C9e1 Hectares of Coastal area - Seagrass/algae beds affected C9e2 Hectares of Coastal area – Estuaries affected C9e3 Hectares of Coastal area - Continental Shelf Sea affected C-9f Hectares of Fresh water surfaces affected C-9g Hectares of Marine surface affected

Additional proposals made in the Second Session of the OEIWG: For Indicator C-2: C-2a - Damage and loss on education. This indicator corresponds to Indicator D3, which is used in Indicator C7 of Target C. Being a duplicate, the Secretariat does not recommend that this proposal is retained. C-2b - Damage and loss on health. This indicator corresponds to Indicator D2, which is used in Indicator C7 of Target C. Being a duplicate, the Secretariat does not recommend that this proposal is retained. C-2c - Damage and loss on nutrition. The Secretariat recognizes that disasters (for example drought) can have a clear negative impact on the nutrition of exposed populations. However, as this term is not an asset, the Secretariat cannot provide a methodology for measuring and assessing the value of direct economic losses in nutrition. Most, if not all of the impacts in nutrition, are represented in indirect losses (and thus ineligible for in this Target) or intangible losses (for example stunted growth in children due to malnutrition – also ineligible for this Target). The Secretariat has used the following definitions of the term “Nutrition” in its analysis: Nutrition11: the intake of food, considered in relation to the body’s dietary needs. Nutrition12: 1: the act or process of nourishing or being nourished; specifically : the sum of the processes by which an animal or plant takes in and utilizes food substances. 2: Nourishment, food. C-2d - Damage and loss on the habitat. If the intention of the term ‘Habitat’ refers to the built environment, the Secretariat suggests not availing this indicator and instead continuing to support the indicators C-5 and C-6, which will provide an accurate proxy for this value. In the broader case of all animals and living things, in 11 12

World Health Organization Meriam-Webster Dictionary

89

addition to buildings the indicator C-8 would reflect direct losses in the environmental domain, also suggesting not using and additional indicator that would duplicate values. The Secretariat has used the following definitions of the term “Habitat” in its analysis: Habitat13: 1a : the place or environment where a plant or animal naturally or normally lives and grows; 1b : the typical place of residence of a person or a group; 1c : a housing for a controlled physical environment in which people can live under surrounding inhospitable conditions (as under the sea).

For Indicator C-3: C-3a - Damage and loss on agriculture. This is the current indicator C-2, or specifically C-2a, losses in agricultural crops. The Secretariat suggests not duplicating this indicator. C-3b - Damage and loss on livestock and livestock production. This is the current indicator C-2b, losses in Livestock. The Secretariat suggests not duplicating this indicator. C-3c - Damage and loss on fishing and fishery resources. The Secretariat recognizes the added value of this indicator, and offers a number of suggestions for modification and implementation, together with caveats: -

-

-

-

13

The fisheries sector is an industrial sector as classified by the International Standard Industrial Classification of All Economic Activities (ISIC), a United Nations industry classification system, coordinated by the United Nations Statistics Division. It receives the classification A03, being classification A - Agriculture, forestry and fishing, and A03 - Fishing and aquaculture. Fisheries is an industrial sector based on several main types of assets. A first group of assets includes ships or boats used for fishing activities. A second group of assets is connected to land-based infrastructure required to support the industry, including processing plants and ports (piers, docks and other land based infrastructures) and other groups of assets referred to as Aquaculture (artificial lakes, ponds, aquariums, and protected areas in seas or lakes dedicated to Mariculture, etc). It is also important to recognize that fishing methods vary according to the region, the species being fished for, and especially the technology available to fishermen. A fishing enterprise can represent a single individual with a small, traditional boat with hand-casting nets or a few pot traps, to a huge fleet of trawlers processing tons of fish every day. For the purposes of this methodology the Secretariat proposes to divide the vessels dedicated to fishing into two groups: i) industrial vessels (usually industrial, large boats with powerful engines and fishing equipment, and belonging typically to a fleet), and ii) ‘traditional’ or ‘artisanal’ fishing boats, usually small in size and value and operated by very few crewmen, and operated by the owner. The Secretariat suggests that, in order to keep indicators simple the following indicators are

Merriam-Webster Dictionary

90

used to measure losses in the fisheries sector: 

All land-based infrastructures affected to be reported (classified by size, small, medium, large) as industrial facilities (C3a to C3l in this document), as appropriate;



All aquaculture areas affected to be reported as Agricultural crops (C2a) as appropriate;

And the addition of the following additional indicators: C-3m – Number of Industrial Fishing Vessels destroyed or lost (sunk) C-3n – Number of Industrial Fishing Vessels Damaged C-3o – Number of Traditional/Artisanal Fishing Vessels Destroyed or lost C-3p – Number of Traditional/Artisanal Fishing Vessels Damaged With the additions of these indicators in Fisheries, direct loss can be calculated as the damages calculated by previous indicators plus the value of the ships and boats affected: C3m*Vind + C3n*Vind*damage% + C3m*Vtra+ C3m* Vtra *damage% Where: Vind : average value of an industrial fishing boat Vtra : average value of an traditional or artisanal fishing boat Damage%: The percentage of the total value of the vessels that would represent the damage, suggested to be the same as in other sectors, 25%

C-3d – Damage and loss on industry. This is indicator C-3. The Secretariat suggests not duplicating this indicator. C-3e – Damage and loss on trade. This is indicator C-4. The Secretariat suggests not duplicating this indicator. C-3f – Damage and loss on tourism. The Secretariat recognizes the importance of a potential independent valuation of tourism, but recommends not to request Member States to collect it separately. As per the ISIC Classification, the Secretariat suggests that all Accommodation and Food and Beverage services should be reported as Commercial Facilities. This recommendation is made to reduce the burden of data collection, the additional value of which to the process is limited, given that the economic assessment would conform to the proposed Commercial sector assessment methodology.

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For Indicator C-4: C-4a – Damage and loss on energy. This note also applies to note on Additional proposal [D-1 bis. - Number of electricity plants and transmission towers destroyed or damaged by hazardous events.] The indicator Damage and Loss on Energy can be extremely complex to estimate. An electrical network may contain multiple elements of very different sizes and values, ranging from a single pole or connecting wire to a large hydroelectric generation facility. Given its complexity and the added data collection burden, the Secretariat does not recommend a dedicated indicator to estimate damage. If measurement of critical energy facilities is desirable, it is suggested that measurement be limited to one or two indicators providing a ‘proxy’ value’ – for example: “Number of electricity plants” (for generation, stations, substations) which could be easily integrated into the original proposed methodology, and reported as part of Industrial Facilities damaged. Related to “Transmission”, noting that one energy transmission line may be composed of a very large number of transmission towers. As for direct losses at neighbourhood or block level, it is important to note that the entire urban infrastructure (energy, water, sewerage and communications) is already being estimated by the current methodology, using a factor of 40% on top of the value of the houses damaged or destroyed. Therefore, assessing it again would be a duplication of the damage. C-4b – Damage and loss on transport. This is covered by Indicator D-4 – Damage to transportation units and infrastructure, the Secretariat suggests retaining the original enumeration of D-4 in place of this proposal. C-4c – Damage and loss on telecommunications. C-4d – Damage and loss on water, sanitation and hygiene. As for direct losses at neighbourhood or block level, it is important to note that the entire urban infrastructure (energy, water, sewerage and communications) is already being estimated by the current methodology, using a factor of 40% on top of the value of the houses damaged or destroyed. Therefore, assessing it again would be a duplication of the damage. In the case of both of these networks, in accounting for large plant facilities - such as Communications centres, Water treatment facilities, Sewerage treatment facilities - it is suggested that in order to be easily integrated into the original proposed methodology, these facilities are reported as part of Industrial Facilities damaged.

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For Indicator C-5: C-5a alt - Damage and loss on environment and forests. This would be covered by Indicator C-9. The Secretariat suggests not duplicating this indicator. C-5b - Damage and loss on administrative buildings. The Secretariat recognizes the added value of this indicator, and offers a number of suggestions for modification and implementation, together with caveats: The Secretariat recommends using the same methodology as that proposed for the assessment of economic losses of buildings used for the Commercial Sector. As the Government is a Service, for the purposes of assessing the economic value of the construction, ‘administrative buildings’ can be appraised as would a commercial establishment. Equipment found within Administrative buildings should be evaluated also as a percentage of the construction value following the same methodology used for different building types. The Secretariat suggests it should be similar to the percentage for commercial establishments. Therefore, the Secretariat proposes the following indicators to assign a value to C-5b: Option 1: (MINIMUM REQUIREMENT) Total number of administrative facilities damaged or destroyed is reported. Option 2: The number of facilities damaged and destroyed are reported separately Option 3: Damage level and size of each facility affected is collected separately. For the case of Option 3, the set of indicators should be: C-5b1 – Number of small administrative buildings damaged. C-5b2 – Number of medium administrative buildings damaged. C-5b3 – Number of large administrative buildings damaged. C-5b4 – Number of small administrative buildings destroyed. C-5b5 – Number of medium administrative buildings destroyed. C-5b6 – Number of large administrative buildings destroyed. In the event that an administrative building is also considered a Cultural Heritage Building, the loss of this building should be reported using Indicator C-8 instead. C-5c - Damage and loss on patrimony. This would be covered by Indicator C-8. The Secretariat suggests not duplicating this indicator.

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New additional Indicators proposed: C-10 – Financial transfer and access to insurance . This is an ‘input’ indicator, the outcome of which reduces the number of people and assets exposed - for example the financial consequences of a single (or multiple hazardous events) are reduced for an individual, community or business. The Secretariat does not recommend the use of such indicators in the global targets. However, the Secretariat agrees with Member States that this is an important aspect to measure, and so in order to provide a global perspective of access to, and coverage of risk transfer mechanisms, the Secretariat proposes the following: C-10 alt. – Total insured direct losses due to hazardous events The Secretariat proposes C-10 alt. replace C-10. This indicator could be reported to governments by insurance and reinsurance stakeholders, or estimated if precise numbers of insurance market penetration indexes are available. It has the advantage over the original structural measure, in that it actually measures a specific facet of the losses, and that it can be clearly associated and reported by hazardous event. Such an indicator would enable comparison on a global level of the proportion of uninsured to insured economic losses due to hazardous events. C-11 – Direct economic losses due to disruptions to basic services. The Secretariat recognizes that this indicator could conform to the requirements of Target C, and such direct economic losses would refer to losses in assets damaged or destroyed. It can be argued that loss of assets due to disruptions is a direct loss as the damage is not associated with the hazardous event itself – for example, frozen produce lost to electric cuts, etc. However as stated in the Note on Target D, it is the view of the Secretariat that obtaining the information required to understand all dimensions of disruption, is simply not feasible at the global level. Even at the local level and/or from the point of view of each specific service provider, this is considered extremely complex. C-12 – Direct economic loss due to services sectors (such as transportation, tourism, finance) caused by hazardous events. The Services sector is considered part of the Commercial Activities sector, and as such is reported in damages to commercial establishments. C-13 – Total of risk informed investments relative to Gross Domestic Product. This is an ‘Input’ indicator, the outcome of which reduces the number of people and assets exposed - for example the financial consequences of a single (or multiple hazardous events) are reduced for an individual, community or business. The Secretariat does not recommend the use of such indicators in the global targets. C-14 – Number of micro enterprises affected. Depending on the nature of the activity of the micro-enterprise, this should be reported under the indicator for Industrial or Commercial facilities. If Governments want to monitor impacts on this segment, then it is recommended that reporting is organised by facility categorized by size (small, medium, large). This would facilitate reporting of micro-enterprises as either small industries or 94

small commercial establishments. C-15 – Number of small and medium enterprises affected (registered enterprises) – sales drop, production drop, profit drop, direct damage to facilities etc.] Please see Secretariat note on proposal C-14.

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ANNEX I: Summary table of indicators and sub-indicators by Category Target C Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

Indicator [Direct economic loss due to hazardous events [in relation to global gross domestic product.]] Direct agricultural loss due to hazardous events.

C-1* C-2*

Methodology

Data

Y

Y

Y

Y

C-2a

Number of hectares of crops affected

Y

Y

C-2b

Number of livestock lost Number of large livestock lost (cows, horses, camel, buffalo, llama, vicuna, etc, 4 legged weight >200Kg.) Number of medium size livestock (sheep, goat, etc., all other not in C-2b3 and C-2b1)

Y

Y

Y

N

Y

N

Y

N

Y

N

Y

N

C-2b1 C-2b2 C-2b4

Number of poultry lost (chicken, duck, etc, 2 legged, feathered) Direct economic loss due to industrial facilities damaged or destroyed by hazardous events. Number of industrial facilities damaged or destroyed

C-3* C-3a C-3b

Number of industrial facilities damaged

Y

N

C-3c

Number of industrial facilities destroyed

Y

N

C-3d

Number of Large industrial facilities damaged or destroyed

Y

N

C-3e

Number of Medium industrial facilities damaged or destroyed

Y

N

C-3f

Number of Small industrial facilities damaged or destroyed

Y

N

C-3g

Number of Large industrial facilities damaged

Y

N

C-3h

Number of Medium industrial facilities damaged

Y

N

C-3i

Number of Small industrial facilities damaged

Y

N

C-3j

Number of large industrial facilities destroyed

Y

N

C-3k

Number of Medium industrial facilities destroyed

Y

N

C-3l

Number of Small industrial facilities destroyed

Y

N

C-3m

Number of Industrial Fishing Vessels destroyed or lost (sunk)

Y

N

C-3n

Number of Industrial Fishing Vessels Damaged

Y

N

C-3o

Number of Traditional/Artisanal Fishing Vessels Destroyed or lost

Y

N

C-3p

Number of Traditional/Artisanal Fishing Vessels Damaged

Y

N

Y

N

Y

N

C-4* C-4a

Direct economic loss due to commercial facilities [and services] damaged or destroyed by hazardous events. Number of commercial facilities damaged or destroyed by hazardous events

96

C-4b

Number of commercial facilities damaged by hazardous events

Y

N

C-4c

Number of commercial facilities destroyed by hazardous events

Y

N

Y

Y

Y

Y

Y

N

C-5* C-5a C-5b

[Direct economic loss due to houses damaged by hazardous events] Number of houses damaged by hazardous events [Damage and loss on administrative buildings.]

C-5b1

Number of small administrative buildings damaged.

Y

N

C-5b2

Number of medium administrative buildings damaged.

Y

N

C-5b3

Number of large administrative buildings damaged.

Y

N

C-5b4

Number of small administrative buildings destroyed.

Y

N

C-5b5

Number of medium administrative buildings destroyed.

Y

N

C-5b6

Number of large administrative buildings destroyed.

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

C-6* C-6a C-7* C-7a C-7b C-7c

[Direct economic loss due to houses destroyed by hazardous events] Number of houses destroyed by hazardous events [Direct economic loss due to damage to [critical infrastructure / public infrastructure] caused by hazardous events.] Economic value of damages to health facilities (calculated from D-2) Economic value of damages to Educational facilities (calculated from D-3) Economic value of damages to road networks (calculated from D-4a)

C-7d

Economic value of damages to railway networks

Y

N

C-7e

Economic value of damages to ports affected

Y

N

C-7f

Economic value of damages to airports affected

Y

N

Economic value of damages to bridges affected

Y

N

Y

N

Y

N

Y

N

Y

N

Y

Y

C-7g C-8* C-8a C-8b C-8c C-9*

[Direct economic loss due to cultural heritage damaged or destroyed by hazardous events.] Cost of Rehabilitation or Reconstruction of buildings, monuments and fixed infrastructure of cultural heritage assets Cost of Rehabilitation or Reconstruction of mobile cultural heritage damaged Market value of mobile cultural heritage destroyed or totally lost. [Direct economic loss due to environment degraded by hazardous events.]

C-9a

Hectares of Coral Reef affected

Y

Y

C-9b

Hectares of Forest affected

Y

Y

C-9b1

Hectares of Forest - Temporate and Boreal forest

Y

Y

C-9b2

Hectares of Forest - Tropical Forest

Y

Y

C-9c

Hectares of Grassland affected

Y

Y

C-9d

Hectares of Wetlands affected

Y

Y

C-9d1

Hectares of Inland Wetlands affected

Y

Y

C-9d2

Hectares of Coastal wetlands/Mangrove affected

Y

Y

C-9d3

Hectares of Coastal wetlands/Tidal marsh affected

Y

Y

C-9e

Hectares of Coastal area affected

Y

Y

C-9e1

Hectares of Coastal area - Seagrass/algae beds affected

Y

Y

C-9e2

Hectares of Coastal area - Estuaries affected

Y

Y

C-9e3

Hectares of Coastal area - Continental Shelf Sea affected

Y

Y

97

C-9f

Hectares of Fresh water surfaces affected

Y

Y

C-9g

Hectares of Marine surface affected

Y

Y

C-10*

[Financial transfer and access to insurance.]

N

N

C-10 alt.

Total insured direct losses due to hazardous events

Y

Y

C-11*

[Direct economic loss due to disruptions to basic services.]

N

N

C-12*

[Direct economic loss due to services sectors (such as transportation, tourism, finance) caused by hazardous events.]

N

N

C-2a*

[Damage and loss on education.]

C-7b

0

C-2b*

[Damage and loss on health.]

C-7a

0

C-2c*

[Damage and loss on nutrition.]

N

N

C-2d*

[Damage and loss on the habitat.]

C-9

0

C-3a*

[Damage and loss on agriculture.]

C-2 / C-2a

0

C-3b*

[Damage and loss on livestock and livestock production.]

C-2b

0

C-3c*

[Damage and loss on fishing and fishery resources.]

C-3m..p

0

C-3d*

[Damage and loss on industry.]

C-3

0

C-3e*

[Damage and loss on trade.]

C-4

0

C-3f*

[Damage and loss on tourism.]

N

N

C-4a*

[Damage and loss on energy.]

N

N

C-4b*

[Damage and loss on transport.]

C-7c

0

C-4c*

[Damage and loss on telecommunications.]

N

N

C-4d*

[Damage and loss on water, sanitation and hygiene.]

N

N

C-5a*

[Damage and loss on environment and forests.]

C-9

0

C-5c*

[Damage and loss on patrimony.]

C-8

0

C-13*

Total of risk informed investments relative to Gross Domestic Product.

N

N

C-14*

Number of micro enterprises affected. Number of small and medium enterprises affected (registered enterprises) - sales drop, production drop, profit drop, direct damage to facilities etc.

C-3

0

C-3

0

C-15*

* Indicators marked with an asterisk (*) are extracted from the Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. All other indicators listed in this Annex and throughout this document are Secretariat proposals from technical papers previously submitted to the OEIWG. See also section 6 above.

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ANNEX II: Classification of facilities according to Economic activity. The following tables summarize Secretariat suggestions for the determination of the indicator to which any facility could be reported and observing the main Indicators - for which the methodology of economic valuation is provided in this guideline. The table contains all headers of the International Standard Industrial Classification of All Economic (ISIC) Activities, Rev.4.

Indicators C2 C3 C4 C5, C6 C7 C8 C9 D2 D3

Methodology Agricultural Industrial Commercial Housing Public Infrastructure Cultural Heritage Environmental Health Education

Those recording damage must exercise judgment in interpreting this summary table. Facilities in some of these activity lines may belong to different indicators depending if the facility is public or private (ex. Entertainment), or depending on the type of facility (example Aquaculture in fisheries is assimilated to Agricultural crops, while land based fisheries installation are considered industrial facilities).

This methodology also suggests that plant installations in public service networks (water and sewerage treatment plants, electric generation, stations and substations, communication stations, etc.) should be assimilated to industrial facilities. It is worth reiterating that losses in the neighbourhood networks of public services are factored as part of the housing sector.

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A 01 02 03 B 05 06 07 08 09 C 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 D 35 E 36 37 38 39 F 41 42 43 G 45 46 47 H 49 50 51 52 53 I 55 56 J 58

Agriculture, forestry and fishing Crop and animal production, hunting and related service activities Forestry and logging Aquaculture Fishing Mining and quarrying Mining of coal and lignite Extraction of crude petroleum and natural gas Mining of metal ores Other mining and quarrying Mining support service activities Manufacturing Manufacture of food products Manufacture of beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and related products Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products and pharmaceutical preparations Manufacture of rubber and plastics products Manufacture of other non metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of computer, electronic and optical products Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c. Manufacture of motor vehicles, trailers and semi trailers Manufacture of other transport equipment Manufacture of furniture Other manufacturing Repair and installation of machinery and equipment Electricity, gas, steam and air conditioning supply Electricity, gas, steam and air conditioning supply Water supply; sewerage, waste management and remediation activities Water collection, treatment and supply Sewerage Waste collection, treatment and disposal activities; materials recovery Remediation activities and other waste management services Construction Construction of buildings Civil engineering Specialized construction activities Wholesale and retail trade; repair of motor vehicles and motorcycles Wholesale and retail trade and repair of motor vehicles and motorcycles Wholesale trade, except of motor vehicles and motorcycles Retail trade, except of motor vehicles and motorcycles Transportation and storage Land transport and transport via pipelines Water transport Air transport Warehousing and support activities for transportation Postal and courier activities Accommodation and food service activities Accommodation Food and beverage service activities Information and communication Publishing activities

100

59 60 61 62 63 K 64 65 66 L 68 M 69 70 71 72 73 74 75 N 77 78 79 80 81 82 O 84 P 85 Q 86 87 88 R 90 91 92 93 S 94 95 96 T 97 98 U 99

Motion picture, video and television programme production, sound recording and music publishing activities Programming and broadcasting activities Telecommunications Computer programming, consultancy and related activities Information service activities Financial and insurance activities Financial service activities, except insurance and pension funding Insurance, reinsurance and pension funding, except compulsory social security Activities auxiliary to financial service and insurance activities Real estate activities Real estate activities Professional, scientific and technical activities Legal and accounting activities Activities of head offices; management consultancy activities Architectural and engineering activities; technical testing and analysis Scientific research and development Advertising and market research Other professional, scientific and technical activities Veterinary activities Administrative and support service activities Rental and leasing activities Employment activities Travel agency, tour operator, reservation service and related activities Security and investigation activities Services to buildings and landscape activities Office administrative, office support and other business support activities Public administration and defence; compulsory social security Public administration and defence; compulsory social security Education Education Human health and social work activities Human health activities Residential care activities Social work activities without accommodation Arts, entertainment and recreation Creative, arts and entertainment activities Libraries, archives, museums and other cultural activities Gambling and betting activities Sports activities and amusement and recreation activities Other service activities Activities of membership organizations Repair of computers and personal and household goods Other personal service activities Activities of households as employers; undifferentiated goods and services producing activities of households for own use Activities of households as employers of domestic personnel Undifferentiated goods and services producing activities of private households for own use Activities of extraterritorial organizations and bodies Activities of extraterritorial organizations and bodies

101

ANNEX III: Method to derive a national proxy construction cost per square meter for all sectors in case no cost information is reported by countries Especially for countries that are likely to face difficulty reporting construction cost for each type of sector, UNISDR and scientific partners devised a methodology aimed to obtain a national proxy construction cost per square meter that could be used as approximation to be applied for each of these sectors that the cost information is missing. The method is based on data analysis of global housing construction cost database “Global Construction Cost and Reference Yearbook 2012” (Compass International, 2012).14 The housing construction cost per square meter for more than 90 countries in Compass and GDP per capita showed a moderate but sufficiently high correlation factor (about 60%). (See Figure below) Figure: Correlation between housing construction cost per square meter and GDP per capita

The statistical regression produced the following formula to assess the construction cost per square meter in the 85 countries of the GAR sample: Construction cost per square meter = 304 + 0.0118*GDP per capita. This formula is suggested to be applied to all facilities in case construction cost for each sector cannot be obtained.

14

This is the only source that contains multiple country information with a documented and consistent methodology. This publication is used worldwide by consulting engineering firms to estimate initial budgets of construction projects.

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REFERENCES Cardona OD (1985) Hazard, vulnerability analysis and risk assessment. Institute of Earthquake Engineering and Engineering Seismology IZIIS, Skopje Cardona OD, Ordaz MG, Marulanda MC, Barbat AH (2008) Estimation of probabilistic seismic losses and the public economic resilience—an approach for a macroeconomic impact evaluation. Cardona OD, Ordaz MG, Reinoso E, Yamin LE, Barbat AH (2010) Comprehensive approach for probabilistic risk assessment (CAPRA): international initiative for disaster risk management effectiveness. Presented at the 14th European conference on earthquake engineering, Ohrid, Macedonia CIMNE, EAI, INGENIAR, ITEC (2013a) Probabilistic modeling of natural risks at the global level: global risk model. Background paper prepared for the 2013 global assessment report on disaster risk reduction, UNISDR, Geneva, Switzerland. http://www.preventionweb.net/gar CIMNE, EAI, INGENIAR, ITEC (2013b) Probabilistic modelling of natural risks at the global level: the hybrid loss exceedance curve. Background paper prepared for the 2013 global assessment report on disaster risk reduction, UNISDR, Geneva, Switzerland. http://www.preventionweb.net/gar Compass International Inc., 2012. Global construction cost and reference yearbook (2012). ECLAC (2003) Manual para la estimación de los efectos socio-económicos de los desastres naturales (report LC/MEX/G.5). CEPAL, Banco Mundial, Mexico DF ECLAC (2012) Valoración de daños y pérdidas: Ola invernal en Colombia 2010–2011. ECLAC, IDB, Bogota EM-DAT The OFDA/CRED international disaster database—www.emdat.net. Universite Catholique de Louvain, Brussels, Belgium. http://emdat.be/. Visited the 2nd of February 2012. FAO (United Nations Food and Agriculture Organization), 2012. Post Disaster Damage, Loss and Needs Assessment in Agriculture. This document can be accessed online in: http://www.fao.org/docrep/015/an544e/an544e00.pdf OSSO Desinventar.org—DesInventar Project. Corporación OSSO, Cali, Colombia. http://desinventar.org/en/ UNDP (United Nations Development Programme), 2013. A comparative review of country-level and regional disaster loss and damage databases. Bureau for Crisis Prevention and Recovery. New York. UN-ECLAC (The United Nations Economic Commission for Latin America and the Caribbean), 2014. Handbook for Disaster Assessment. Santiago, Chile. This document can be accessed online in: http://repositorio.cepal.org/bitstream/handle/11362/36823/S2013817_en.pdf?sequence=1 103

UNISDR (The United Nations Office for Disaster Risk Reduction). 2009. GAR 2009: Global assessment report on disaster risk reduction: risk and poverty in a changing climate. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011a. GAR 2011: Global Assessment Report on disaster risk reduction: revealing risk, redefining development. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011b. Desinventar.net database global disaster inventtory. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2013a. GAR 2013: Global Assessment Report on disaster risk reduction: from shared risk to shared value; the business case for disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ UNISDR (The United Nations Office for Disaster Risk Reduction) 2013b. GAR 2013 ANNEX II: Loss Data and Extensive/Intensive Risk Analysis. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2013/en/gar-pdf/Annex_2.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015a. Indicators to Monitor Global Targets of the SEndai Framework for Disaster Risk Reduction 2015-2030: A Technical Review. Background paper presented for the Open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva, Switzerland. This document can be accessed online in: http://www.preventionweb.net/files/45466_indicatorspaperaugust2015final.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015d. Proposed Updated Terminology on Disaster Risk Reduction: A Technical Review. Background paper presented for the Open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva, Switzerland. This document can be accessed online in: http://www.preventionweb.net/files/45462_backgoundpaperonterminologyaugust20.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015c. GAR 2015: Global Assessment Report on disaster risk reduction: Making development sustainable: The future of disaster risk management. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ UNISDR (The United Nations Office for Disaster Risk Reduction). 2015d. GAR 2015 ANNEX II: Loss Data and Extensive Risk Analysis. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2015/en/gar-pdf/Annex2Loss_Data_and_Extensive_Risk_Analysis.pdf Velásquez, C. A., Cardona, O. D. , Mora, M. G., Yamin, L. E., Carreño, M.L and Barbat, A. H. 2014. “Hybrid loss exceedance curve (HLEC) for disaster risk assessment”. Nat Hazards (2014) 72:455– 479. DOI 10.1007/s11069-013-1017-z 104

Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 United Nations Office for Disaster Risk Reduction. Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. United Nations Office for Disaster Risk Reduction. Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015.

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Concept note on Methodology to Estimate Damages to Infrastructure and Interruptions to Basic Services to Measure the Achievement of Target D of the Sendai Framework for Disaster Risk Reduction: A Technical Review

10 June 2016

The United Nations Office for Disaster Risk Reduction 106

1. Overview This document outlines a methodology to construct an indicator that will allow the measurement of Target D of the Sendai Framework, related to damage to critical infrastructure and disruption of basic services associated with hazardous events. The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OEIWG) requested the UNISDR to propose a methodology at the First and Second Sessions, held in Geneva on 29-30 September 2015 and 10-11 February 2016. The purpose of this document is to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of the global Target D of the Sendai Framework for Disaster Risk Reduction 2015-2030. Target D: Substantially reduce disaster damage to critical infrastructure and disruption of basic services, among them health and educational facilities, including through developing their resilience by 2030 The methodology described here is a product of the original work of the Secretariat as part of the research and production of the Global Assessment Report on Disaster Risk Reduction (GAR) editions of 2013 and 2015, enriched and guided by the comments and suggestions raised in the OEIWG. The methodology can be tested with the GAR 2015 consolidated dataset covering 82 countries, using 350,000 reports of small, medium and large scale disasters.

2. Summary One of the most difficult challenges of Target D is that it refers to two separate but interconnected situations. The first is the situation in which critical infrastructure is damaged (without services necessarily being interrupted or compromised in terms of quality) and the second is when basic services are interrupted (which could potentially happen with or without damage). It is important to note that during the discussions held in the First and Second Sessions of the OEIWG, the issue of partial interruptions and reduced levels of service - as well as lower quality of the service - was raised by several Member states. It is also important to note that the chosen indicator will have to consider the two impacts to critical infrastructure, one being the damage and the second the interruption of basic services. Interruption of basic services, in turn, could be measured not only as those situations in which the service is completely shut down, but those in which there is either a partial interruption of the service or a reduction in the quality of the service provided. Other elements to take into account are the length of time these interruptions last and the number of users that suffer the interruption or lower quality of service. Combining all these elements can be complex and would demand enormous effort in data collection by countries, especially for the period of the baseline where all situations involving damage or interruption must be revisited for the period 2005-2015.

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The indicators proposed independently monitor the two elements of “damage to critical infrastructures” and “disruption of basic services” of Target D. They also monitor elements included in direct economic loss (Target C) and affected people (Target B). The methodology proposed here by the Secretariat suggests the collection and use of a simple inventory of situations in which either damage (expressed as the number of assets damaged) was recorded to critical infrastructures OR situations in which the provision of the basic service was affected to a noticeable degree, including interruptions, partial interruptions and reduced quality of service. Indicators D-2 to D-4 directly monitor the elements of “damage to critical infrastructures” by measuring the number of times and the number of facilities which provide Education, Health and Transportation services are damaged or destroyed. These also indirectly monitor elements of “disruption of basic services” associated to these infrastructures in Target D. The indicators proposed are also used and included in direct economic loss indicators (Target C) and affected people (Target B), reducing in this way the burden of data collection. Indicator D-5 and its sub-indicators directly monitor the elements of “disruption of basic services” of Target D by counting the number of times the provision of basic services are disrupted, either by interruptions of the services, by damage to the facilities that provide the service or by a measurable reduction in the quality of the service or the population covered by the service – or combination of all the above. Finally, the methodology proposes three alternative methodologies for the creation of an index that combines these two elements and its indicators, and additionally proposes the correlation of this index with the population of the country in order to reflect the importance of damage to critical infrastructure and basic services in small countries. The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

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No.

Indicator

Methodology

Data

B-1

Recommended - for measurement at the global level

Y

Y

D-1

Damage to critical infrastructure due to hazardous events.

Y

N

D-2

Number of health facilities destroyed or damaged by hazardous events.

Y

Y

Y

Y

Y

N

D-4b Kilometres of road destroyed or damaged per hazardous event.

Y

Y

D-4c Number of bridges destroyed/damaged by hazardous event.

Y

N

D-4d Kilometres of railway destroyed / damaged by hazardous event.

Y

N

D-4k Number of airports destroyed / damaged by hazardous event

Y

N

D-4l Number of ports destroyed / damaged by hazardous event

Y

N

Y

N

Y

N

D-3 D-4

D-7 D-1 bis *

Number of educational facilities destroyed or damaged by hazardous events. Number of transportation units and infrastructures destroyed or damaged by hazardous events.

[Number / percentage] of security service structures destroyed or damaged by hazardous events. Number of electricity plants and transmission towers destroyed or damaged by hazardous events.

* The Secretariat recommends D-1 bis. for measurement at the global level as part of the compound indicator D-1; so that D-1 is computed using indicators D-2 to D-4, plus D-7 and D-1 bis. It is suggested that D-1 bis. be renumbered to avoid confusion with the compound indicator D-1 to which it contributes.

Recommended - for measurement at the national level D-4e [Number of days airport(s) have been closed due hazardous event.]

N

N

D-4f [Number of days port(s) have been closed due hazardous event.]

N

N

N

N

N

N

D-4i [Number of days without water supply due to hazardous event.]

N

N

D-4j [Number of days without sanitation services due hazardous event.]

N

N

N

N

Y

N

N

N

N

N

[Number of days telecommunications breakouts have been experienced due hazardous event.] [Number of days power breakouts have been experienced due to D-4h hazardous event.] D-4g

D-5 D-10 D-13 D-14

[Number / Length / Percentage] of [time / days / person days] basic services have been disrupted due to hazardous events. Number of communication infrastructure destroyed or damaged by hazardous events. Number of agricultural facilities destroyed or damaged by hazardous events. Number of water and sanitation infrastructures destroyed or damaged by hazardous events.

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D-15

Number of days financial services have been disrupted due to hazardous events.

N

N

B-1

Not Recommended

Y

Y

D-2a Number of health facilities removed from risk areas.

N

N

D-3a Number of educational facilities removed from risk areas.

N

N

D-4a Extent of damage to ports and airports.

N

N

N

N

N

N

D-6 D-6 alt D-8 D-9 D-11 D-12

[Number / Percentage] of education or health facilities [removed from risk areas / retrofitted. Critical Infrastructure replaced from risk areas or retro-fitted, and/or protective infrastructure installed. [Number / percentage] of tourist infrastructure facilities destroyed or damaged by hazardous events. Number of states with resilience programmes or strategies for health and education facilities. Percentage of education facilities developed under the safe school program. Percentage of health facilities developed under the safe hospital program.

C-3a N

N

N

N

N

N

3. Technical Requirements for an indicator to measure “damages to infrastructure and interruptions to basic services” An indicator, is a number that gives an indication of the size of certain phenomena15, in this case it estimates the damages to critical infrastructure and interruptions of basic services that occur in each disaster. It is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure. It would be impossible to remove a certain level of uncertainty or inaccuracy from loss estimations, which is contingent on the methodology and criteria used to define: assets damaged or destroyed; an ‘interruption’, as well as the exhaustiveness of data collection. In this sense, the proposed index is an approximate value (a “proxy”). The indicators to measure damages to infrastructure and interruptions to basic services for the Sendai Framework aim to meet following criteria: Consistent over time: The target requires the comparison of losses of two different decades, the decade of the Hyogo framework (2005-2015) and the last decade of the Sendai Framework (20202030). Monitoring the losses, nevertheless, should occur throughout the entire period of 25 years in order to obtain a continuous view of the progress of implementation and achievements, and 15

http://www.oxforddictionaries.com/es/definicion/learner/indicator

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data must be recorded and reported in a consistent way for the cycle of measurement, and so avoiding introducing biases. Consistent across countries: It must be a) applicable to any country in the world, to the maximum degree possible allowing comparison among countries or regions, and b) feasible, such that data can be obtained regardless of the level of development or income of each country. SMART: Specific, Measurable, Achievable, Relevant, Time Bound. Reliable: Results can be trusted and if possible have a measure of dispersion, and for which a particular uncertainty measure can be determined. Transparent: The methodology used is well known, with caveats declared, and for which weaknesses, limitations and strengths, including economic assessment biases, are identified. Verifiable: The estimated index can be traced back to the original indicators of damage. Feasible: Easy to collect data in a practical and realistic way, without imposing an extraordinary or even impossible burden on countries. Taking advantage of existing data: Many countries have already collected standardized data. Taking advantage of this fact is more practical than having everyone start from zero. Can be refined/improved over time: when better information is made available, or improved methodologies are developed, the economic estimation can be revised to reflect the improvement. Useful: Results can be used not only for measuring the achievement of targets but also for DRR strategy planning, awareness raising, risk assessments and the development of DRR and related policies.

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4. List of Proposed Indicators to Measure Target D: The following indicators are taken from the Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. D-1 - Damage to critical infrastructure due to hazardous events. (This indicator should be computed based on indicators D-2, D-3 and D-4 (road). Note: and other indicators approved by the OEIWG. [D-1 bis. - Number of electricity plants and transmission towers destroyed or damaged by hazardous events.] [D-2 – [Number / percentage] of health facilities [including mental health services] destroyed or damaged by hazardous events.] [D-2a. Number of health facilities removed from risk areas.] [D-3 - [Number / percentage] of educational facilities destroyed or damaged by hazardous events.] [D-3a. - Number of educational facilities removed from risk areas.] D-4 - [Number / percentage] of [major] transportation [units and] infrastructures destroyed or damaged by hazardous events. Note: the indicator should measure (1) road (in kilometres of paved/unpaved), (2) railway (in kilometres), (3) port (number of facilities) and (4) airport (number of facilities). [D-4a. – Extent of damage to ports and airports] [D-4b. – Kilometres of road destroyed/damaged by hazardous event.] [D-4c. – Number of bridges destroyed/damaged by hazardous event.] [D-4d. – Kilometres of railway destroyed/damaged by hazardous event.] [D-4e. – Number of days airport(s) have been closed due hazardous event.] [D-4f. – Number of days port(s) have been closed due hazardous event.] [D-4g. – Number of days telecommunications breakouts have been experienced due hazardous event.] [D-4h. – Number of days power breakouts have been experienced due to hazardous event.] [D-4i. – Number of days without water supply due to hazardous event.] [D-4j. – Number of days without sanitation services due hazardous event.]

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D-5 – [Number / Length / Percentage] of [time / days / person days] basic services have been disrupted due to hazardous events. Note: Sectors monitored include healthcare services, education services, transport sector, ICT, water supply, sewage system, solid waste management, power/energy system and emergency response.

[D-6 [Number / Percentage] of education or health facilities [removed from risk areas / retrofitted.]] [D-6 alt. - Critical Infrastructure replaced from risk areas or retro-fitted, and/or protective infrastructure installed.] [D-7 - [Number / percentage] of security service structures destroyed or damaged by hazardous events.] [D-8 - [Number / percentage] of tourist infrastructure facilities destroyed or damaged by hazardous events.] [D-9- Number of states with resilience programmes or strategies for health and education facilities.] [D-10 – Number of communication infrastructure destroyed or damaged by hazardous events.] [D-11 – Percentage of education facilities developed under the safe school program.] [D-12 – Percentage of health facilities developed under the safe hospital program.] [D-13 – Number of agricultural facilities destroyed or damaged by hazardous events.] [D-14 – Number of water and sanitation infrastructures destroyed or damaged by hazardous events.] [D-15 – Number of days financial services have been disrupted due to hazardous events.] Services proposed to be monitored individually: Education (D-2)

Water (D-5d)

Health (D-3)

Sewerage (D-5e)

Transport (D-5a)

Government (D-5f)

Energy (D-5b)

Emergency services (D-5g)

Communications (D-5c)

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5. Applicable Definitions and Terminology Terminology 2009 comments that “Critical facilities are elements of the infrastructure that support essential services in a society. They include such things as transport systems, air and sea ports, electricity, water and communications systems, hospitals and health clinics, and centres for fire, police and public administration services.” For the purposes of this methodology, key terms with regards to Critical Infrastructure and Basic Services are those defined in the Working Text on Terminology based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016 Basic services: Services that are needed for all of society to function [effectively / appropriately]. Annotation: Examples of basic services include water supply, sanitation, health care, education, housing, and food supply. They also include services provided by critical infrastructure such as electricity, telecommunications, transport, finance or waste management that are needed for all of society to function. For this indicator, disruption, interruption or lower quality of basic services is proposed to be measured for the following public services: Educational facilities: play schools, kindergartens, primary, secondary or middle schools, technical-vocational schools, colleges, universities, training centres, adult education, military schools and prison schools Emergency Response: disaster management office, fire management service, police, army and emergency operation centres. Healthcare facilities: health centres, clinics, local and regional hospitals, outpatient centres and in general facilities used by primary health providers Information and Communication Technology (ICT) system: plants and telephone networks (telecommunication network), radio and television stations, post offices and public information offices, internet services, radio telephones and mobile phones Power/energy system: generation facilities, transmission and distribution system and dispatch centres and other works Sewerage system: sanitation and sanitary sewage systems and collection and treatment of solid waste. Solid waste management: collection and treatment of solid waste. Transport system: road networks, railways (including stations), airports and ports Water supply: drinking water supply system (water outlets, water treatment plants, aqueducts and canals which carry drinking water, storage tanks.) 114

Critical infrastructure: The physical structures, facilities, networks and other assets that support services that are socially, economically or operationally essential to the functioning of a society or community. Annotation: Critical infrastructures are elements of the infrastructure that support essential services in a society. They include electricity/power, water, transport systems, air and sea ports, communication systems, [satellite services], health and educational facilities (including hospitals, health centres, schools), as well as [infrastructures for the protection against flooding and water (risk) management,] public administration services, financial services, centres for fire and police, etc.

6. Notes from the Secretariat on comments and proposals from Member States [D-1 bis. - Number of electricity plants and transmission towers destroyed or damaged by hazardous events.] The indicator “Number of electricity plants” could easily be integrated into the original methodology proposed here. The Secretariat notes that one energy transmission line may be composed of a very large number of transmission towers, therefore if Member States wish to adopt a more detailed indicator on critical energy facilities, it is recommended to use “Transmission lines”. [D-2 – [Number / percentage] of health facilities [including mental health services] destroyed or damaged by hazardous events.] The percentage in this proposal is an alternate to the Secretariat proposal of normalization by Population. It poses the additional challenge of knowing the exact number or facilities of each type. The Secretariat recommendation is to keep normalization by population as it conveys the same message and requires data that are much easier to obtain and maintain. The Secretariat suggests not to identify the type of health facility, so as to avoid the possibility of a hierarchy of importance for such facilities emerging. [D-2a. Number of health facilities removed from risk areas.] The Secretariat does not recommend the use of this indicator because: a) all loss indicators are to be collected by hazardous event but the number of health facilities removed from risk areas is not attached to one specific hazardous event, but is rather considered a relatively ‘permanent’ status potentially maintained over many events. This raises the question of multiple counting of this figure, and b) this is an ‘Input’ indicator, the outcome of which reduces the number of health facilities affected - for example fewer, even no facilities are destroyed and damaged. Counting the number of health facilities ‘removed’ can be considered double counting of the positive results of risk reduction measures. The Secretariat recommends retaining the proposed text for D-2a that was presented in previous 115

technical papers by the Secretariat16, and which was accompanied by the following proposed optional indicators for measuring direct economic loss on health facilities. So that: D2 - Number of health facilities destroyed or damaged by hazardous events D2b - number of health facilities damaged D2c - number of health facilities destroyed D2d - number of Large health facilities damaged or destroyed D2e - number of Medium health facilities damaged or destroyed D2f - number of Small health facilities damaged or destroyed D2g - number of Large health facilities damaged D2h - number of Medium health facilities damaged D2i - number of Small health facilities damaged D2j - number of Large health facilities destroyed D2k - number of Medium health facilities destroyed D2l - number of Small health facilities destroyed [D-3 - [Number / percentage] of educational facilities destroyed or damaged by hazardous events.] See previous note on Percentages or normalization by population (D-2).

[D-3a. - Number of educational facilities removed from risk areas.] See previous note for indicator [D-2a. Number of health facilities removed from risk areas]. The Secretariat recommends retaining the proposed text for D-3a that was presented in previous technical papers by the Secretariat, and which was accompanied by the following proposed optional indicators for measuring direct economic loss on educational facilities. So that: D3 - Number of educational facilities destroyed or damaged by hazardous events D3b - number of educational facilities damaged D3c - number of educational facilities destroyed D3d - number of Large educational facilities damaged or destroyed D3e - number of Medium educational facilities damaged or destroyed D3f - number of Small educational facilities damaged or destroyed D3g - number of Large educational facilities damaged D3h - number of Medium educational facilities damaged D3i - number of Small educational facilities damaged D3j - number of Large educational facilities destroyed D3k - number of Medium educational facilities destroyed D3l - number of Small educational facilities destroyed

16

Concept note on Methodology to Estimate Direct Economic Losses from Hazardous Events to Measure the Achievement of Target C, issued 11 November 2015, and updated in Appendix A: Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction issued 23 December 2015.

116

D-4 - [Number / percentage] of [major] transportation [units and] infrastructures destroyed or damaged by hazardous events. The Secretariat recommends simplifying D-4 so that it measures the Number of transportation units and infrastructures destroyed or damaged by hazardous events. The Secretariat recognizes the value of the additional indicators proposed by Members in the Second Session; these can also be used in Target C. The Secretariat encourages the separate collection of data for the four types (roads, railways, airports and ports, corresponding to D-4b, D-4d, D-4e and D-4f). This will entail an additional (although relatively light) reporting burden. [D-4b. – Kilometres of road destroyed / damaged by hazardous event.] [D-4c. – Number of bridges destroyed / damaged by hazardous event.] [D-4d. – Kilometres of railway destroyed / damaged by hazardous event.] In place of the language in the Working Text for D-4e. and D-4f, the Secretariat is proposing simplified language for measuring damage to airports and ports and new numbering: D-4k. – Number of Airports destroyed / damaged by hazardous event D-4l. – Number of ports destroyed / damaged by hazardous event In the specific case of Target D and under the methodological approach proposed in this document, the number of facilities would satisfy data requirements for ports and airports. In order to be relevant for Target C also, these indicators should also discriminate facilities on the basis of size. See previous note on Percentages or normalization by population (D-2). The Secretariat does not recommend the use of the following indicators for measurement at the global level: [D-4a. – Extent of damage to ports and airports] [D-4e. – Number of days airport(s) have been closed due hazardous event.] [D-4f. – Number of days port(s) have been closed due hazardous event.] [D-4g. – Number of days telecommunications breakouts have been experienced due hazardous event.] [D-4h. – Number of days power breakouts have been experienced due to hazardous event.] [D-4i. – Number of days without water supply due to hazardous event.] [D-4j. – Number of days without sanitation services due hazardous event.] These indicators are not suitable for measurement at the global level because: ▫ Interruption of services provided by critical infrastructure could be measured also by the degree by which the service is affected, i.e. the interruption of services can be partial, for example a service given only at 50% of capacity. ▫ Disruption of services may occur at irregular periods of time, for example in those cases where the service is restricted to a specific schedule in rationing situations. ▫ Disruptions of services can also be due to lower levels of quality. For example water may continue to be supplied at certain percentage of normal operating capacity, but the water provided may not be fully compliant with all sanitary requirements. ▫ Disruptions of services can be measured in smaller units of time, for example hours or even minutes or seconds. ▫ Disruptions of services may affect different segments of the population with differing degrees of severity, including cases in which service delivery continues. 117

▫

For one specific hazardous event, multiple services can be affected in different ways, multiplying or compounding the effects of disruptions. Power disruptions, for example, may have a direct impact on water, transport, communications, etc. ▫ Most importantly, the measure of all previously noted dimensions (severity of the disruption, quality ramifications, duration, population affected) can - and usually do - vary during the period of the disruption, making these measures extremely complex to obtain. As an example, water supply can completely stop for the entire population for several hours, and then return partially for a period of time for a segment of the population, served at lower quality, with incremental improvements in dimension (level, time, coverage, quality, etc.) over a period of time. It is the view of the Secretariat that obtaining the information required to understand all dimensions of disruption, is simply not feasible at the global level. These indicators are recommended for use at the national, but even at the local level and/or from the point of view of each specific service provider, this is considered extremely complex. In conclusion, the Secretariat recommends Member States adopt the simpler method of providing a qualitative Yes/No marker to each service disruption/affectation. Simpler, but effective for the purposes of measuring the global Target. D-5 – [Number / Length / Percentage] of [time / days / person days] basic services have been disrupted due to hazardous events. See note on previous group of indicators as regards the complexity of measurement in terms of time/coverage/quality and other dimensions.

[D-6 [Number / Percentage] of education or health facilities [removed from risk areas / retrofitted.]] [D-6 alt. - Critical Infrastructure replaced from risk areas or retro-fitted, and/or protective infrastructure installed.] See previous note for indicator [D-2a. Number of health facilities removed from risk areas]. [D-7 - [Number / percentage] of security service structures destroyed or damaged by hazardous events.] The Secretariat recognizes the value of this additional indicator. However, Members may wish to consider the additional burden in data collection and the need for specific definitions and terminology. If this indicator is adopted by the OEIWG, the Secretariat suggests using the proposed normalization by population instead of a percentage. [D-8 - [Number / percentage] of tourist infrastructure facilities destroyed or damaged by hazardous events.] The Secretariat does not consider this type of infrastructure as Critical and therefore advises against including this indicator in the Target. [D-9- Number of states with resilience programmes or strategies for health and education facilities.] See previous note for indicator [D-2a. Number of health facilities removed from risk areas].

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[D-10 – Number of communication infrastructure destroyed or damaged by hazardous events.] The Secretariat recognizes the value of this additional indicator; it can also be also used in Target C. However, Members may wish to consider the additional burden in data collection and the need for specific definitions and terminology, and whether this would be better measured at the national rather than the global level. [D-11 – Percentage of education facilities developed under the safe school program.] See previous note for indicator [D-2a. Number of health facilities removed from risk areas]. [D-12 – Percentage of health facilities developed under the safe hospital program.] See previous note for indicator [D-2a. Number of health facilities removed from risk areas]. [D-13 – Number of agricultural facilities destroyed or damaged by hazardous events.] The Secretariat does not consider this type of infrastructure as Critical and therefore advises against including this indicator in the Target. [D-14 – Number of water and sanitation infrastructures destroyed or damaged by hazardous events.] The Secretariat recognizes the value of this additional indicator; it can also be also used in Target C. However, Members may wish to consider the additional burden in data collection and the need for specific definitions and terminology, and whether this would be better measured at the national rather than the global level. [D-15 – Number of days financial services have been disrupted due to hazardous events.] The Secretariat does not consider this type of infrastructure as Critical and therefore advises against including this indicator in the Target. See note on previous group of indicators as regards the complexity of measurement in terms of time/coverage/quality and other dimensions.

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7. Computation Methodology Background to the proposed methodology The methodology proposed here will allow the use of a consistent, conservative and homogeneously estimated index that will be computed from hundreds of thousands of records of disasters at all scales that are expected to be reported as part of the Sendai Framework monitoring process. The methodology for the index proposes the collection and use of a simple inventory of situations in which either damage (expressed as the number of assets damaged) was recorded OR situations in which the service provider was affected to some noticeable degree, including interruptions, partial interruptions and reduced quality of service. National disaster loss databases provide the data source used in this methodology to estimate direct economic loss. These databases usually contain information about a large number of hazardous events at all scales, including quantitative and qualitative indicators of physical damage. The national disaster loss databases available in 89 countries indicate that simple (yes/no) physical damage indicators are in general available and robust. During the First Session of the OEIWG, member states provided inputs as to a possible definition of “Critical Infrastructure”. Given concerns on measurability and data availability, the Secretariat recommends the monitoring of damage - at either global or national levels - to the following aspects of critical infrastructure: Infrastructure Sector

Recommended to be monitored at global level

national level depending on the needs and context of each country

Education

X

X

Health

X

X

Transport

X (road)

X Other transportation (e.g. airport, seaport, railway, etc.)

Energy

X

X (including Nuclear, Other)

Communications

X

X (ICT Sector)

Finance

X

Food

X (Agriculture, Livestock, Irrigation)

Water

X

X

Sewerage

X

X (Sewerage, Waste management)

Government

X

X

Safety/Security Emergency services

X X

X

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Color coding: X Available quantitatively in 89 National loss databases (i.e. # schools) X Available qualitatively in 89 National loss databases (Affected: Yes/No) X Not currently available in most National loss databases

Having conducted a literature review for countries that are not part of the UN supported DesInventar initiative, the Secretariat found references to sectoral coverage of critical infrastructure protection plans in selected countries as follows: Sector

Australia

Canada

Netherlands

UK

US

EU

Energy (including nuclear)

x

x

x

x

X

x

ICT

X

x

x

x

x

x

Finance

X

x

x

x

x

x

Health care

X

x

x

x

x

x

Food

X

x

x

x

x

x

Water

X

x

x

x

x

x

Transport

X

x

x

x

x

x

Safety

Emergency services

x

x

Emergency services

Emergency services

x

Government

x

x

x

x

x

Chemicals

x

x

x

x

x

x

x

Defence industrial base

X

Other sectors or activities

Public spaces, national heritage

Legal judicial

/

Dams, commercial facilities, national monuments

Space and research facilities

Source: OECD (2008), “Protection of Critical Infrastructure and the Role of Investment Policies Relating to National Security. Australia: “What is critical infrastructure?” Australian National Security (www.ag.gov.au/add), Canada: About Critical Infrastructure, Public Policy Canada (www.ps-sp.gc.ca) Netherlands: Report on Critical Infrastructure Protection; Ministry of Interior 16/9/05; UK: Counter-terrorism strategy (www.security.homeoffice.gov.uk), USA: Department of Homeland “Security Sector Specific Plans” (www.dhs.gov); Commission of the European Communities Green Paper on a European Programmes for Critical Infrastructure Protection (COM 2005)576

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Computation Methodology The proposed methodology suggests the construction of an index based on a simple inventory of occurrences of damage and interruptions, related to the size of the population of each country, so as to reflect the relative importance of these interruptions and damages. The methodology consists of three steps – the Secretariat highlights challenges in each step. Step 1: Collect good quality data on physical damage and interruptions by hazardous event. Step 2 : Calculate the number of times an interruption or damage happens, based on source data. Step 3: Convert the number of interruptions relative to population, calculating the number of interruptions per 100,000. The Secretariat proposes several options to calculate the index, all of which follow this general pattern: Index of Critical Infrastructure Damage and Service Interruptions = number of times interruption or damage occurs/ population * 100,000 (a) number of times interruption or damage occurs: The data is collected and reported from national disaster loss databases. The following are three options that compute the number of interruptions in different ways, from simple to more complicated: a) Simple count of number of events in which an interruption or damage is accounted for. b) Consolidated count of interruptions or damages per sector. c) Consolidated count of interruptions or damages per sector with emphasis on health and education. Simple count of number of events in which an interruption or damage is accounted for: this method will count the number of records in the national disaster loss database that contain damage or interruption to any service. Pros: Simplicity Cons: Cases that affect multiple services and infrastructure will have the same weight as cases where only one service/infrastructure was affected. Consolidated count of interruptions or damages per sector: this method will sum the number of sectors affected per record for all records. Situations in which more than one sector has been affected will contribute more to the sum. Pros: Simplicity, cases that affect multiple services and infrastructure will have more weight than cases where only one service/infrastructure was affected. Cons: All sectors will have the same weight.

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Consolidated count of interruptions or damages per sector with emphasis on health and education: this method will separately sum, for all records, the number of schools and health facilities from the number of other sectors affected. Situations in which more than one sector has been affected, or more than one school or health facility was affected will contribute more to the sum. Pros: Cases that affect multiple services and infrastructure will have more weight than cases where only one service/infrastructure was affected. Greater weight is accorded Education and Health interruptions and damage. Cons: Less simple. The number of health and education facilities affected will have to be reported (NOTE: indicators D2 and D3 are also required for Target C, so the adoption of this option could be advantageous).

8. Critical issues, sources, data collection and statistical processing Source and data collection Main source: National disaster loss databases For Targets A through D, time dimension should be defined to establish when data should be recorded and reported. The dynamics of disasters can force changes in the data (e.g. damage to critical infrastructure may only manifest after a certain period of time after the hazardous event). Defining this issue is critical, especially when recording losses associated with slow-onset disasters such as drought. Therefore, given the propensity for variation after disaster, data for the proposed indicators should be recorded only when data are stable, after the end of sudden-onset disasters, such as earthquakes or floods - 42 days is suggested (see below). It is recommended that a similar, welldefined threshold is determined to establish the ‘end date’ for the recording of data for slowonset disasters also. There are numerous mortality thresholds that could inform Members’ deliberations. The fields of medicine and epidemiology often use terms between 28 and 42 days as benchmarks, among them: - newborn deaths, which are considered in the first four weeks (28 days) of life, and referred to as the neonatal period (WHO, UNICEF, other sources); mortality after traffic accidents for which the WHO employs a period of 30 days. The Secretariat recommends the number of 42 days as it is consistent with medical research in child mortality at birth17, and while recognizing that any proposed threshold is arbitrary, it could be used as standard for the Framework. 42 days would be the period within which all people for which a legal determination of being missing or presumed dead is recorded after an event – this is expected to capture the majority of the reports. The experience gained in the aftermath of disasters suggest that this period may be sufficient to allow authorities to establish stable and appropriately representative figures.

17

Maternal Mortality Death (see http://www.maternalmortalitydata.org/Definitions.html) 42 days after birth or termination of pregnancy

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The UNISDR / DesInventar methodology suggests practical methods for the establishment of a start and end date of slow-onset disasters. It is suggested that that the date of appearance of first reports of damage can mark the beginning of the disaster (not the actual phenomena, like a drought or similar slow onset event) and the date of the last report of physical damage associated with the event can be taken as the end date of the event. The same methodology also recommends annual reporting as a minimum for slow-onset disasters, thereby facilitating the reporting of multi-annual events – most of which are associated with climatic phenomena and climate change induced processes. For D1 through D4, more than 89 countries have historic data.

Statistical processing: Disaster loss data is greatly influenced by large-scale catastrophic events, which represent important outliers in terms of damage to critical infrastructure. UNISDR recommends countries report the data by event, so that complementary analysis can be undertaken to obtain trends and patterns in which such catastrophic events (that can represent outliers in terms of damage) can be included or excluded.

Disaggregation Further to the recommendations of both the OEIWG and the IAEG-SDGs, the Secretariat recommends disaggregating data: 

By country, by event, by hazard type (e.g. using the IRDR classification, natural hazards can be disaggregated as climatological, hydrological, meteorological, geophysical, biological and extra-terrestrial)



By sub-national administrative unit similar to municipality.



By asset (health/education/road) (for D-1)



By destroyed/damaged (for D-3)



By transportation mode (for D-4)



By service sector (for D-5)

Comments and limitations Not every country has a comparable national disaster loss database that is consistent with these guidelines (although current coverage exceeds 89 countries). Therefore, by 2020, it is expected that all countries will build/adjust national disaster loss databases according to the recommendations and guidelines of the OEIWG. Counting the number of facilities does not necessarily reflect the size of the facility and related impact on the communities.

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For D-4, measuring the length of roads and railways does not necessarily reflect the quality and function of roads/railways and related impact on the communities. The national disaster loss database developed does not have historic data on damage to railways, ports and airports. Establishing baseline data is a challenge. For D-5, the Expert Group recommended replacing “times” with “days” and adding “how many people have not received basic services (figure to be normalized over population)”. The Secretariat is concerned that this proposal will be extremely difficult to define; in addition, recording the duration of service disruption and the number of people who did not receive basic services will be extremely challenging. Introducing certain scales (duration: short, medium and long; an affected scale in terms of household numbers) might offer a practical solution; this would require further consideration of thresholds.

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ANNEX I: Summary table of indicators and sub-indicators by Category Target D Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

Indicator

D-1*

Damage to critical infrastructure due to hazardous events. [Number of electricity plants and transmission towers destroyed or damaged by hazardous events.] [Number / percentage] of health facilities [including mental health services] destroyed or damaged by hazardous events.

D-1 bis* D-2* D-2a* D-2a

[Number of health facilities removed from risk areas] Number of health facilities damaged or destroyed

Methodology

Data

Y

N

Y

N

Y

Y

N

N

Y

Y

D-2b

Number of health facilities damaged

Y

N

D-2c

Number of health facilities destroyed

Y

N

D-2d

Number of Large health facilities damaged or destroyed

Y

N

D-2e

Number of Medium health facilities damaged or destroyed

Y

N

D-2f

Number of Small health facilities damaged or destroyed

Y

N

D-2g

Number of Large health facilities damaged

Y

N

D-2h

Number of Medium health facilities damaged

Y

N

D-2i

Number of Small health facilities damaged

Y

N

D-2j

Number of Large health facilities destroyed

Y

N

D-2k

Number of Medium health facilities destroyed

Y

N

D-2l

Number of Small health facilities destroyed

Y

N

126

Code

Indicator

[Number / percentage] of educational facilities destroyed or damaged by hazardous events.

D-3* D-3a*

[Number of educational facilities removed from risk areas.] Number of educational facilities destroyed or damaged by hazardous events

D-3a

Methodology

Data

Y

Y

N

N

Y

Y

D-3b

Number of educational facilities damaged

Y

N

D-3c

Number of educational facilities destroyed

Y

N

D-3d

Number of Large educational facilities damaged or destroyed

Y

N

D-3e

Number of Medium educational facilities damaged or destroyed

Y

N

D-3f

Number of Small educational facilities damaged or destroyed

Y

N

D-3g

Number of Large educational facilities damaged

Y

N

D-3h

Number of Medium educational facilities damaged

Y

N

D-3i

Number of Small educational facilities damaged

Y

N

D-3j

Number of Large educational facilities destroyed

Y

N

D-3k

Number of Medium educational facilities destroyed

Y

N

Number of Small educational facilities destroyed

Y

N

Y

N

N

N

Y

Y

Y

N

Y

N

Y

N

D-3l D-4* D-4a* D-4a (D-4b*) D-4a1 D-4a2 D-4a3

[Number / percentage] of [major] transportation [units and] infrastructures destroyed or damaged by hazardous events. [Extent of damage to ports and airports] [Kilometres of road destroyed / damaged by hazardous event.] Number of kilometres of unpaved road destroyed or damaged per hazardous event. Number of kilometres of paved road destroyed or damaged per hazardous event. Number of kilometres of paved highway roads destroyed or damaged per hazardous event

D-4c*

[Number of bridges destroyed/damaged by hazardous event.]

Y

N

D-4d*

[Kilometers of railway destroyed / damaged by hazardous event]

Y

N

D-4e*

[Number of days airport(s) have been closed due hazardous event.]

N

N

D-4f*

[Number of days port(s) have been closed due hazardous event.] [Number of days telecommunications breakouts have been experienced due hazardous event.] [Number of days power breakouts have been experienced due to hazardous event.]

N

N

N

N

N

N

D-4i*

[Number of days without water supply due to hazardous event.]

N

N

D-4j*

[Number of days without sanitation services due hazardous event.]

N

N

D-4k

Number of Airports destroyed / damaged by hazardous event

Y

N

D-4l

Number of ports destroyed / damaged by hazardous event

Y

N

D-4g* D-4h*

127

Code

D-5* D-6* D-6 alt* D-7* D-8* D-9* D-10* D-11* D-12* D-13* D-14* D-15*

Indicator

[Number / Length / Percentage] of [time / days / person days] basic services have been disrupted due to hazardous events. [Number / Percentage] of education or health facilities [removed from risk areas / retrofitted. Critical Infrastructure replaced from risk areas or retro-fitted, and/or protective infrastructure installed. [Number / percentage] of security service structures destroyed or damaged by hazardous events. [Number / percentage] of tourist infrastructure facilities destroyed or damaged by hazardous events. Number of states with resilience programmes or strategies for health and education facilities. Number of communication infrastructure destroyed or damaged by hazardous events. Percentage of education facilities developed under the safe school program. Percentage of health facilities developed under the safe hospital program. Number of agricultural facilities destroyed or damaged by hazardous events. Number of water and sanitation infrastructures destroyed or damaged by hazardous events. Number of days financial services have been disrupted due to hazardous events.

Methodology

Data

N

N

N

N

N

N

Y

N

C-3

0

N

N

Y

N

N

N

N

N

N

N

N

N

N

N

* Indicators marked with an asterisk (*) are extracted from the Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. All other indicators listed in this Annex and throughout this document are Secretariat proposals from technical papers previously submitted to the OEIWG. See also the note in section 6 above.

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REFERENCES OECD (2008), “Protection of Critical Infrastructure and the Role of Investment Policies Relating to National Security. It cites Australia: “What is critical infrastructure? Australian National Security (www.ag.gov.au/add), Canada: About Critical Infrastructure, Public Policy Canada (www.ps-sp.gc.ca) Netherlands: Report on Critical Infrastructure Protection; Ministry of Interior 16/9/05; UK: Counter-terrorism strategy (www.security.homeoffice.gov.uk), USA: Department of Homeland “Security Sector Specific Plans” (www.dhs.gov); Commission of the European Communities Green Paper on a European Programmes for Critical Infrastructure Protection (COM 2005)576, ECLAC (2012) Valoración de daños y pérdidas: Ola invernal en Colombia 2010–2011. ECLAC, IDB, Bogota EM-DAT The OFDA/CRED international disaster database—www.emdat.net. Universite Catholique de Louvain, Brussels, Belgium. http://emdat.be/. Visited the 2nd of February 2012. FAO (United Nations Food and Agriculture Organization), 2012. Post Disaster Damage, Loss and Needs Assessment in Agriculture. This document can be accessed online in: http://www.fao.org/docrep/015/an544e/an544e00.pdf UN-ECLAC (The United Nations Economic Commission for Latin America and the Caribbean), 2014. Handbook for Disaster Assessment. Santiago, Chile. This document can be accessed online in: http://repositorio.cepal.org/bitstream/handle/11362/36823/S2013817_en.pdf?sequence=1 UNISDR (The United Nations Office for Disaster Risk Reduction). 2009. GAR 2009: Global assessment report on disaster risk reduction: risk and poverty in a changing climate. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011a. GAR 2011: Global Assessment Report on disaster risk reduction: revealing risk, redefining development. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011b. Desinventar.net database global disaster inventory. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2013a. GAR 2013: Global Assessment Report on disaster risk reduction: from shared risk to shared value; the business case for disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/

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UNISDR (The United Nations Office for Disaster Risk Reduction) 2013b. GAR 2013 ANNEX II: Loss Data and Extensive/Intensive Risk Analysis. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2013/en/gar-pdf/Annex_2.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015a. Indicators to Monitor Global Targets of the SEndai Framework for Disaster Risk Reduction 2015-2030: A Technical Review. Background paper presented for the Open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva, Switzerland. This document can be accessed online in: http://www.preventionweb.net/files/45466_indicatorspaperaugust2015final.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015b. Proposed Updated Terminology on Disaster Risk Reduction: A Technical Review. Background paper presented for the Open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Geneva, Switzerland. This document can be accessed online in: http://www.preventionweb.net/files/45462_backgoundpaperonterminologyaugust20.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2015c. GAR 2015: Global Assessment Report on disaster risk reduction: Making development sustainable: The future of disaster risk management. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ UNISDR (The United Nations Office for Disaster Risk Reduction). 2015d. GAR 2015 ANNEX II: Loss Data and Extensive Risk Analysis. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/2015/en/gar-pdf/Annex2Loss_Data_and_Extensive_Risk_Analysis.pdf Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 United Nations Office for Disaster Risk Reduction. Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. United Nations Office for Disaster Risk Reduction. Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015.

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Concept note on Methodology to Estimate the Progress of National and Local DRR Strategy to Measure the Achievement of Target E of the Sendai Framework for Disaster Risk Reduction: A Technical Review

10 June 2016

The United Nations Office for Disaster Risk Reduction

131

1. Overview This note outlines the core elements of national and local disaster risk reduction (DRR) strategies and suggests options regarding computation methodology to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of global Target E of the Sendai Framework for Disaster Risk Reduction 2015-2030. The note responds to the request for additional information from Members at the First and the Second Sessions of the Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OEIWG). It complements Appendix C of the “Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction” produced by the Secretariat on 23 December 2015 in which two indicators were proposed for consideration by the OEIWG. Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020 E-1: Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 E-2: Percentage of local governments that adopt and implement local DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 In the First Session of the Open-ended Intergovernmental Expert Working Group, several countries called for clarification of the core requirements of a DRR strategy and computation method for these indicators. The issue was raised if it is possible to have quantitative indicators to measure the level of progress, rather than applying only Yes/No as regards plan availability. Several countries also addressed the need to make these indicators more related with the Goals and Priorities of action of the Sendai Framework for Disaster Risk Reduction. The two indicators E-1 and E-2 are also currently being examined in the SDG indicator discussion in the Inter-Agency Expert Group (IAEG-SDGs) which requested the urgent development of methodology. Indicators that simply count the number of countries, are not technically recommended in the SDG discussion. Instead, indicators to measure global and national progress have been promoted. This argument can also be applied to the local level wherein indicators monitoring gradual progress might be welcomed. There was also discussion that the population coverage of such strategies would be important to ensure a people-centred approach. Based on the considerations expressed by countries in the Sendai and SDG indicators discussion, this note can also contribute to further examination by Members of both groups. In suggesting design and methodology for indicators for Target E, this document draws from the experiences of these countries and is also informed by analysis of the reports of the 140+ countries that undertook at least one cycle of self-assessment of progress in implementing the Hyogo Framework for Action during the period 2005-2015.

132

2. Summary National and local disaster risk reduction strategies should be based on, and aligned with, the guiding principles, scope, outcomes, and strategic priorities of the Sendai Framework, and in particular Paragraph 27 (b) which identifies that strategies should be adopted and implemented and that they should set: a. b. c. d.

time frames, targets and indicators, objectives and measures aiming at preventing the creation of risk, objectives and measures aiming at the reduction of existing risk, and objectives and measures aiming at the strengthening of economic, social, health and environmental resilience.

The four elements of Paragraph 27(b) can be regarded as the core requirements of a national or local strategy. These requirements should be complemented by additional common standards which can promote the development and application of functional DRR strategies at national and local levels. Members of both the OEIWG and the IAEG-SDGs have emphasised that indicators that simply count the number of countries are not recommended. Instead, indicators to measure global and national progress over time have been promoted. This argument can also be applied to the local level, for which indicators monitoring gradual progress are considered more useful. Further to the deliberations of the OEIWG as well as the IAEG, UNISDR proposed computation methodologies that allow the monitoring of improvement in national and local DRR strategies over time, indictors E-1 (National Strategies) and E-2 (Local Strategies). These methodologies range from a simple quantitative assessment of the number of these strategies to a qualitative measure of alignment with the Sendai Framework, as well as population coverage for local strategies. In addition, this document proposes the introduction of a compound indicator E-0 (National and Local Strategies) that is a composite of E-1 and E-2 and provides one headline indicator for the Target. The minimum requirements identified in the Sendai Framework for inclusion in a DRR strategy, apply as much to local DRR strategies as they do to national DRR strategies. The responsibility for the development of local DRR strategies generally falls to respective local governments, the responsibilities, characteristics and capabilities of which can vary significantly between and within countries. Additionally, the Secretariat proposes the computation methodology for the composite indicator E-0, which will consolidate E-1 and E-2 in a single indicator measuring the Target. E-0: Percentage of countries that adopt and implement national and local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030.

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The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, non-alignment with Framework, inter alia.

No.

B-1 E-0 E-1 E-2

B-1 E-3 E-4 E-5

E-6 E-7 E-8 E-9 E-10 E-12 E-13

Indicator

Recommended - for measurement at the global level Percentage of countries that adopt and implement national and local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030. Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 Percentage of local governments that adopt and implement local DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030

Recommended - for measurement at the national level Number of countries that integrate climate and disaster risk reduction into development planning. Number of countries that adopt and implement critical infrastructure protection plan. Number of countries with cross-sectoral bodies/forums, with clear roles and responsibilities identified across state institutions and other stakeholders, established for the implementation and review of DRR measures. Number of countries accounting for future risk in public and private balance sheets, setting financial targets to inform investment strategies for reducing risk and enhancing future prosperity. Number of countries and local governments conducting periodic outcome reviews of the implementation of national and local DRR strategies. Number of countries that adopt and implement sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction. Number of countries that have national financing mechanisms for DRR. Number of countries that have spatial and land use planning mechanisms for DRR. Number of people protected by evacuation, improved infrastructure and other relevant measures that reduce the possible impact of disasters on people. Number of people who received/require relief aid or assistance due to a hazardous event.

Methodology

Data

Y Y

N

Y

Y

Y

N

Y

Y

N

N

Y

N

Y

N

N

N

Y

Y

Y

N

Y

N

N

N

N

N

Y

Y

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B-1

Not Recommended

E-1 alt. E-2 alt. E-2 altbis. E-2a E-3 alt. E-4 bis. E-4 ter.

E-5 alt.

E-11

Number of countries with national DRR strategies for implementation of the Sendai Framework for DRR [Number of countries and local governments that adopt and implement local and sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] [Percentage of local governments with DRR strategy for implementation of national strategy for the Sendai Framework for DRR.] [Percentage/number of local governments that have adopted or committed to the new 10 essentials defined in the UNISDR global campaign “Making Cities Resilient.”] [Number of countries that have integrated DRR and climate change into their national development plan.] [Number of countries with resilience programmes or strategies for health and education facilities in the framework of the DRR plans.] [Number of sector/hazard specific DRR strategy/plan developed in a country.] [Number of countries that adopt and implement specific DRR strategies in line with the Sendai Framework for DRR, including through cross-sectoral bodies/forums with identified roles and responsibilities, as appropriate, for relevant actors.] [Number of countries that mainstream DRR into national development planning.]

Y

Y

E-1 E-2 / E-4 ter. E-2 E-2 E-3 E-4 E-8

E-5

E-3

3. Suggested Requirements for a National and Local Disaster Risk Reduction Strategy National and local DRR strategies should be based on, and aligned with, the guiding principles, scope, outcomes, and strategic priorities of the Sendai Framework. Paragraph 27 (b) of the Sendai Framework states that national and local disaster risk reduction strategies and plans should be adopted and implemented, and that they should satisfy four elements: a) b) c) d)

setting time frames, targets and indicators setting objectives and measures aiming at preventing the creation of risk, setting objectives and measures aiming at the reduction of existing risk, and setting objectives and measures aiming at the strengthening of economic, social, health and environmental resilience.

These elements can be regarded as the core requirements of a national or local strategy that should be complemented by additional common standards which can promote the development and application of functional DRR strategies at national and local levels.

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Scope and outcome of national and local DRR strategies. The national/local DRR strategy provides orientation to avoid the creation of new risks, reduce existing risks, and strengthen economic, social, health and environmental resilience. The scope and outcome(s) of the national/local DRR strategy should allow for actions to be executed in the short, medium and long term, may encompass sector-specific or hazard-specific considerations, and permit geographical prioritisation (where appropriate). The scope and outcome(s) of the national/local DRR strategy should be aligned with the scope, purpose and outcome of the Sendai Framework for DRR. Strategic priorities of national and local DRR strategies. Informed by risk assessment and aligned with the strategic development priorities of the country or local authority, the development of priorities for each outcome (potentially identifying critical sectors) is recommended. National/local strategic priorities should be informed by the four priorities of action of the Sendai Framework, be aligned with existing regional/national DRR strategy, and inform/be informed by regional/national development plans. Targets of national and local DRR strategies. Targets that are applicable, verifiable, comparable and measurable, should be developed for national and local DRR strategies targets to determine progress, and identify the need for adjustment. It is recommended that national and local targets are aligned with the global targets of the Sendai Framework. After having considered historic losses, evolving risk and development priorities and patterns, and choosing between conservative, moderate and ambitious scenarios, the development of quantitative targets is recommended. In terms of national level aggregation of progress made in reaching local targets, a weighted percentage can be applied. This is described in more detail in a ‘methodology for setting national and local Sendai targets’ developed by UNISDR. Periodic review of national and local DRR strategies. In Paragraph 27 (e), the Sendai Framework calls for the development and strengthening of mechanisms to follow up, periodically assess and publicly report on progress on national and local plans. It is therefore recommended that progress in implementation of national and local DRR strategies, and thus the Sendai Framework, should be assessed using a monitoring and review framework for national and local reporting against targets and indicators that are consistent with countries’ development priorities, respective level of disaster risk, capacity and other contextual characteristics. Public policy (input) indicators can measure whether a country has public policies for preventing and reducing risk and for strengthening social and economic resilience. Such indicators can measure whether a country has the necessary risk governance and risk knowledge arrangements in place to underpin these policy areas. In measuring the efficacy of public policies for DRR, a group of (output) indicators measure social and economic resilience of both the public and the private sector; specifically the fiscal resilience of the state, the social resilience of households and communities, and business resilience. These indicators assist an understanding of whether a country can absorb and recover from disaster losses in a way that minimizes short and long run negative social and economic impacts. 136

Periodic monitoring of the national/local DRR strategy involving State and non-State actors is advised, potentially to entail annual progress reports on implementation. Disaster risk governance and investment in disaster risk reduction. To promote the successful implementation of national and local DRR strategies in effectively managing disaster risk, the Sendai Framework stresses the importance of strengthening disaster risk governance arrangements underpinned by the necessary resources for implementation. Paragraph 16 states: Strengthening disaster risk governance and coordination across relevant institutions and sectors and the full and meaningful participation of relevant stakeholders at appropriate levels. As a public policy tool, the development of a national or local DRR strategy should be based on a formal mandate or executive order by the relevant national and/or local authority and in adherence with national and local laws and regulations. It will support an increased understanding of current (and ideally, future) levels of disaster loss, risk, and resilience, as well as cost-effective risk reduction options in critical sectors. It is recommended that the planning process involves an all-of-society engagement - all State institutions, civil society, academic and private sector - and takes into consideration a gender, age, disability and cultural perspective, as well as the needs of people living under particular conditions of vulnerability, in particular women and children. As such, the establishment of a multi-sectoral, inter-disciplinary national coordinating mechanism - which can inter alia secure agreement and time-bound commitment of national and local stakeholders - is considered important in the development and implementation of national and local DRR strategies. Additionally, Paragraph 30 (a) identifies the need to allocate the necessary resources, including finance and logistics, as appropriate, at all levels of administration for the development and the implementation of disaster risk reduction strategies, policies, plans, laws and regulations in all relevant sectors. In summary, efforts to achieve the targets and objectives of the four elements will be enhanced if strategies satisfy the following: ▫ Are based on an understanding of disaster risk, supported by multi-hazard risk information and assessment. ▫ Set scope, outcomes, strategic priorities aligned to the Sendai Framework and its guiding principles. ▫ Are defined across different timescales, with targets, indicators and time frames for delivery. ▫ Are underpinned by a formal mandate / executive order from the relevant national/local authority, be consistent with national/local laws and regulations, and have roles and responsibilities of state institutions, public and private stakeholders and cross-sectoral bodies/forums clearly defined. ▫ Allocate the necessary resources, including finance, as appropriate, at all levels of administration with accountable lead entities, targets and benchmarks for implementation. ▫ Incorporate a mechanism of periodic outcome reviews of the progress in implementation 137

▫

▫

of the Sendai Framework. Are integrated in (sectoral) strategies and plans for the implementation of the 2030 Agenda for Sustainable Development, including national development and climate change adaptation plans. Consider people living under particular conditions of vulnerability, in particular women and children.

Suggested common standards are informed by the relevant text of the Sendai Framework, the deliberations of the OEIWG and IAEG-SDGs, and are additionally informed by elements of common practice of countries having developed or in the process of developing national and local DRR strategies aligned with the Sendai Framework. The components identified in the Sendai Framework for inclusion in a DRR strategy, apply as much to local DRR strategies as they do to national DRR strategies. The responsibility for the development of local DRR strategies generally falls to respective local governments, the responsibilities, characteristics and capabilities of which can vary significantly between and within countries. National and local disaster risk reduction strategies frequently contain a large number of components. As the components of the strategy can vary with time, not least as the national and local enabling and risk environment evolves, enumerating these components can be challenging. Enumeration is possible, but the measurement and reporting burden may prove an impediment for some countries.

4. List of Proposed Indicators to Measure Target E: The following indicators are taken from the Working Text on Indicators. Based on negotiations during the Second Session of the OEIWG held in Geneva from 10-11 February 2016. (Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016) E-1 - Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030. Note: the DRR strategies need to be based on risk information and assessments. [E-1 alt. - Number of countries with national DRR strategies for implementation of the Sendai Framework for DRR.] [E-2 – Percentage of local governments that adopt and implement local DRR strategies in line with the [Sendai Framework for Disaster Risk Reduction 2015-2030 / national disaster risk reduction strategy]. Note: the DRR strategies need to be based on risk information and assessments. [E-2 alt. - Number of countries and local governments that adopt and implement local and sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] 138

[E-2 alt-bis. - Percentage of local governments with DRR strategy for implementation of national strategy for the Sendai Framework for DRR.] [E-2a – Percentage/number of local governments that have adopted or committed to the new 10 essentials defined in the UNISDR global campaign “Making Cities Resilient.”] [E-3 – Number of countries that [integrate / integrated] [climate and disaster risk / climate change / adaptation] into [development planning / development plan].] Note: This indicator also functions as indicator contributing to the outcome of the Target C “economic loss” [E-3 alt. - Number of countries that have integrated DRR and climate change into their national development plan.] [E-4 – Number of countries that adopt and implement critical infrastructure protection plan.] [E-4 bis. - Number of countries with resilience programmes or strategies for health and education facilities in the framework of the DRR plans.] [E-4 ter. - Number of sector/hazard specific DRR strategy/plan developed in a country.] Note: This indicator directly supports progress of Target D and indirectly contributes to reduction of affected people (Target B) and economic loss (Target C). [E-5 - Number of countries with cross-sectoral bodies/forums, with clear roles and responsibilities identified across state institutions, civil society, private sector and international actors, in the implementation and review of DRR measures.] [E-5 alt. - Number of countries that adopt and implement specific DRR strategies in line with the Sendai Framework for DRR, including through cross-sectoral bodies/forums with identified roles and responsibilities, as appropriate, for relevant actors.] [E-6 - Number of countries accounting for future risk in public and private balance sheets, setting financial targets to inform investment strategies for reducing risk and enhancing future prosperity.] [E-7 - Number of countries and local governments conducting (independent) periodic outcome reviews of the implementation of national and local DRR strategies.] [E-8 - Number of countries that adopt and implement sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] [E-9 – Number of countries that have national financing mechanisms for DRR.] [E-10 – Number of countries that have spatial and land use planning mechanisms for DRR.] [E-11 - Number of countries that mainstream DRR into national development planning.]

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[E-12 – Number of people protected by evacuation, improved infrastructure and other relevant measures that reduce the possible impact of disasters on people.] [Footnote: Disaster mitigation and protection measures could include, as appropriate, a wide range of activities before, during and after disasters by relevant actors.] [E-13 – Number of people who received/require relief aid or assistance due to a hazardous event.] [Footnote: Relief aid or assistance could include, inter alia, food, medicine, medical care and shelter.]

5. Applicable Definitions and Terminology Target E of the Sendai Framework specifically requires National and Local DRR Strategies to be produced. Therefore for the purposes of this methodology, and unless stated otherwise, key terms are those defined in a) the Sendai Framework for Disaster Risk Reduction 2015-2030, and b) the “Working Text on Terminology” based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016. National DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 20152030: national disaster risk reduction strategies and plans, across different timescales with targets, indicators and time frames, aimed at preventing the creation of risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience (Sendai Framework, para 27(b)). Note: the Sendai Framework advocates the link between DRR and climate change. It also emphasises that DRR strategies need to be based on risk information and assessments. Local DRR Strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030: Local disaster risk reduction strategies and plans, across different timescales with targets, indicators and time frames, aimed at preventing the creation of risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience (Sendai Framework, para27 (b)). Cross-sectoral bodies/forum: coordinating mechanisms that operate within and across sectors and with relevant stakeholders across public and private stakeholders and at all levels, with the full engagement of all State institutions at national and local levels (based on the Principles of the Sendai Framework, Para 19 (e)). Disaster risk governance: The system of institutions, mechanisms, policy and legal frameworks and other arrangements to guide, coordinate and oversee disaster risk reduction and related areas of policy. Disaster risk management: Disaster risk management is the application of disaster risk reduction policies, processes and actions to prevent new risk, reduce existing disaster risk and manage residual risk contributing to the strengthening of resilience. 140

Disaster risk reduction: Disaster risk reduction is the policy objective aimed at preventing new and reducing existing disaster risk and managing residual risk, all of which contributes to strengthening resilience. [Disaster risk [reduction / management] plan: A document prepared by an authority, sector, organization or enterprise that sets out goals and specific objectives for reducing disaster risks together with related actions to accomplish these objectives. Local Government: Form of public administration at the lowest tier of administration [charged with the responsibility for disaster risk reduction] within a given state, which generally acts within powers delegated to them by legislation or directives of the higher level of government. (City managing, a new profession. The Independent, 1914)

6. Notes from the Secretariat on comments and proposals from Member States In respect of E-1, the OEIWG discussed adding “aligned with enabling legislation and regulation”. While the Secretariat is in favour of encouraging an enabling environment for DRR strategy development, it is concerned that in the absence of an agreed definition of, and sufficient data for “enabling legislation and regulation”, such an indicator would be at risk of distortions of subjectivity. [E-2 alt. - Number of countries and local governments that adopt and implement local and sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] [E-2 alt-bis. - Percentage of local governments with DRR strategy for implementation of national strategy for the Sendai Framework for DRR.] [E-2a – Percentage/number of local governments that have adopted or committed to the new 10 essentials defined in the UNISDR global campaign “Making Cities Resilient.”] Members of both the OEIWG and the IAEG-SDGs have emphasised that indicators that simply count the number of countries are not recommended. Instead, indicators to measure global and national progress have been promoted. This argument can also be applied to the local level, for which indicators monitoring gradual progress are considered more useful. Therefore, the Secretariat supports E-2 as originally proposed because the inclusion of population addressed by local strategies and plans is an important facet of gauging coverage. [E-3 alt. - Number of countries that have integrated DRR and climate change into their national development plan.] This is a rewording of the originally proposed indicator E-3, and is consistent with the desire to promote coherence with the 2030 Agenda and the Paris Agreement. Although Members may wish to examine how integration of DRR will be captured and reported, the measurement of this indicator is recommended at the national level. The mechanisms that countries in developing and 141

implementing national and local strategies and plans for DRR can vary and may be, by law, independent of their development plans. [E-4 – Number of countries that adopt and implement critical infrastructure protection plan.] Protection of existing and future critical infrastructure is one of the measures a comprehensive strategy must contain. As critical infrastructure protection plans contribute to efforts to both reduce existing risk and prevent the creation of risks, measuring such plans independently would entail a double counting of these measures. The Secretariat therefore recommends that countries interested in tracking progress in specific aspects of national and local strategies such as this, should develop this as a national indicator. [E-4 bis. - Number of countries with resilience programmes or strategies for health and education facilities in the framework of the DRR plans.] This indicator is contained in (or is a subset of) indicator E-4. See the note for this indicator. [E-4 ter. - Number of sector/hazard specific DRR strategy/plan developed in a country.] The Secretariat encourages countries and local authorities to undertake comprehensive risk assessments in the process of formulating national and local DRR strategies. In so doing, countries and local authorities may identify critical sectors on which to focus, or indeed develop a strategy, develop outcomes, priorities and targets that are sector-specific. As such, Members may find a measure of the number of sectors with dedicated DRR strategies and plans useful in assessing progress. However, it may be that a more qualitative measurement of improvement of such sector-specific strategies is more useful than merely counting strategies and plans. The Secretariat recognises that indicators E-4, E-4bis and E-4ter directly support the measurement of progress of Target D, and indirectly contribute to reduction of affected people (Target B) and economic loss (Target C). Indicator E-4 ter. is closely related to E-8. The number of hazards a country may be exposed to, and the sectors most at risk, vary widely. The Secretariat is therefore recommending that this Indicator is measured only at the national level, where significant hazards can be enumerated, and critical sectors identified. [E-5 - Number of countries with cross-sectoral bodies/forums, with clear roles and responsibilities identified across state institutions, civil society, private sector and international actors, in the implementation and review of DRR measures.] This indicator seeks confirmation of the assignation of roles and responsibilities in implementing DRR measures. If the coordinating mechanisms established are to positively affect decisionmaking and investment behaviour, it is important that this is verified in the review. This indicator was proposed by the Expert Group; the Secretariat considers that this component could be measured as part of DRR strategies, and as such should be included in national level indicator system. [E-5 alt. - Number of countries that adopt and implement specific DRR strategies in line with the Sendai Framework for DRR, including through cross-sectoral bodies/forums with identified roles and responsibilities, as appropriate, for relevant actors.] 142

The Secretariat is in favour of metrics of measurement that encourage the identification of roles and responsibility for implementation of DRR outcomes, priorities and targets, and seek to cultivate a culture of follow-up and delivery. If the coordinating mechanisms established are to positively affect decision-making and investment behaviour, it is important that this is verified in the review. In investigating the existence of cross-sectoral engagement in DRR, the indicator supports the principle that disaster risk can only be effectively reduced, prevented and resilience strengthened if an all of government, all of society approach is adopted; however, to be useful the indicator will need to move beyond merely a binary Yes/No consideration. This may be most usefully addressed in countries’ monitoring of national targets. [E-6 - Number of countries accounting for future risk in public and private balance sheets, setting financial targets to inform investment strategies for reducing risk and enhancing future prosperity.] Current and future disaster risk rarely features as a liability in public or private financial statements. If these liabilities are not recorded, the incentive to assume the costs of investment required to mitigate the costs incurred by these losses is much diminished. This indicator seeks to measure the degree to which such liabilities are estimated and incorporated in financial planning and investment so as to overcome impediments to future prosperity. This indicator was proposed by the Expert Group however given the timeframe for delivery of Target E of 2020, effectively monitoring global progress of this indicator might be unrealistic. This indicator measures one of many measures that can be taken to reduce risk, prevent the creation of risks and increase resilience and as such, should be included in national level indicator system. [E-7 - Number of countries and local governments conducting (independent) periodic outcome reviews of the implementation of national and local DRR strategies.] In calling for the predictable assessment of the impact that the implementation of such strategies has on trends in disaster risk and the corollary losses incurred, this indicator places the emphasis on both the implementation of national and local DRR strategies (and not simply their formulation), as well as their relevance. This indicator was proposed by the Expert Group and contributes to measuring achievement of element 1 of Paragraph 27b of the Framework and as such is already included in Indicators E-1 and E-2. Members should examine if this indicator (review) falls within the scope of Target E. This indicator might be included at national level. [E-8 - Number of countries that adopt and implement sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] This indicator is contained in E-4 Ter., which considers sector and hazard specific strategies. While the ideal would be for national or local strategies to be designed by sector and by hazard, a solid sector-specific strategy also denotes progress. The identification of those sectors considered critical varies significantly between country sectors varies; sectors exposed to disasters and considered critical by one country may not be the case elsewhere. Consequently the Secretariat recommends this indicator to be measured at country level, where specific sectors can be targeted and followed up. [E-9 – Number of countries that have national financing mechanisms for DRR.]

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This indicator can be considered a generic equivalent to indicator E-6, as it requests that countries and local governments explicitly design financing strategies for disaster risk reduction, including but not restricted to risk retention and transfer mechanisms, accounting for future losses and ensuring liabilities are estimated and incorporated in financial planning and investment so as to overcome impediments to future prosperity. This indicator measures one of many measures that can be taken to reduce risk, prevent the creation of risks and increase resilience and as such, should be included in national level indicator system. [E-10 – Number of countries that have spatial and land use planning mechanisms for DRR.] One of the most important instruments of a DRR Strategy is the establishment and enforcement of regulations that aim to prevent the creation of risk, employing measures such as spatial and land use planning, building codes and standards. This indicator measures one of many measures that can be taken to reduce risk, prevent the creation of risks and increase resilience and as such, should be included in national level indicator system. [E-11 - Number of countries that mainstream DRR into national development planning.] DRR strategies cannot be constructed in isolation of the development plans of countries. Risk Reduction is fundamentally a problem of development, and the links between DRR and the Sustainable Development Agenda are a reflection of this fact. This Indicator, which is a rephrasing of indicator E-3, would measure how tightly this integration is achieved. The Secretariat recommends that the original indicator E-3 be retained, and measured at national level where, inter alia, a favourable legislative context allows countries to integrate these instruments. [E-12 – Number of people protected by evacuation, improved infrastructure and other relevant measures that reduce the possible impact of disasters on people.] [Footnote: Disaster mitigation and protection measures could include, as appropriate, a wide range of activities before, during and after disasters by relevant actors.] Evacuation, Preparedness plans, structural retrofitting, Early Warning Systems (also discussed in Targets B and G) are all examples of such relevant measures (or outputs of a strategy) that this indicator seeks to capture. They aim to increase the resilience of communities and to reduce the stock of existing risk. This indicator measures one of many measures that can be taken to reduce risk, prevent the creation of risks and increase resilience and as such, should be included in national level indicator system. [E-13 – Number of people who received/require relief aid or assistance due to a hazardous event.] [Footnote: Relief aid or assistance could include, inter alia, food, medicine, medical care and shelter.] National and Local Strategies which include pre-disaster planning exercises, including contingency planning, enables governments to react in a timely and effective manner to the impacts of hazardous events by providing the required support to the affected population; and in so doing, strengthens economic, social, health and environmental resilience of communities. This indicator measures one of many measures that can be taken to reduce risk, prevent the creation of risks and increase resilience and as such, should be included in national level indicator system. The proposal by the OEIWG under Target G to include “Number of countries that have national 144

multi-hazard risk assessment providing the necessary information for National DRR strategies” may be an element that could be more usefully incorporated in an indicator for Target E as a component of quality / definition of a DRR strategy that is aligned with the Sendai Framework.

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7. Computation Methodology In identifying the core elements of a strategy, Members of the OEIWG identified that countries can monitor the improvement in quality of national DRR strategies or individual components over time, using quantitative indicators to measure the level of progress in alignment with the Sendai Framework, and not simply the change in the number of strategies. The two indicators, E-1 (National Strategies) and E-2 (Local Strategies), are also currently examined in the SDG indicator discussion of the IAEG. Instead of simply counting the number of countries, indicators monitoring gradual progress should be introduced for global and national as well as local levels. Members also recommended that the population coverage of such a plan would be important to ensure a people-centred approach. Further to the deliberations of the OEIWG as well as the IAEG, UNISDR proposed the following computation methodologies to allow the monitoring of improvement in national and local DRR strategies over time. These range from a simple quantitative assessment of the number of these strategies to a qualitative measure of alignment with the Sendai Framework, as well as population coverage for local strategies. To further simplify measurement of this target at the local level, the Secretariat also suggests that the measurement of progress in developing and implementing local strategies for DRR prioritise hazard prone areas – these areas should be defined by each country. A significant update to this note, is the proposal for a compound indicator E-0 (National and Local Strategies) that is a composite of E-1 and E-2 to provide a single headline indicator for the Target: E-0: Percentage of countries that adopt and implement national and local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030. E-0 would be calculated combining both indicators E-1 and E-2, giving a certain weight (importance) to each of them. Member States should consider the most appropriate weighting to be accorded; it may be that Members wish to accord equal importance to both the local and national strategies, or give more weight to national strategies as the provider of overall direction and to which local strategies should be aligned. Subject to the Option selected by countries to calculate E-1,the computation methodology for E-0 will vary The two variants of the formula are provided below.

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Indicator E-0: Percentage of countries that adopt and implement national and local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030. For indicator E-1, the Secretariat proposes two options: Option 1 a simple counting of the number of countries, and Option 2, percentage compliance. Consequently, the methodology to be employed to calculate E-0 will differ depending on which Option countries decide to use for E-1. It is therefore proposed that to be able to calculate E-0, if countries wish to use Option 1, the indicator E-1 is converted into a percentage. So that: E-1% = (E-1) / (Total number of Member States) * 100.

(if Option 1 is selected )

The result of Option 2 is already in the form of a percentage, and so the result can be used directly in the calculation of E-0. Consolidating E-1 and E-2 is most simply computed by taking the average the two: E-0 = (E-1% + E-2) / 2 Where E-1% is the percentage form of E-1 described above.

However, if Members wish to accord different weighting to E-1 and E-2, indicator E-0 can be calculated as follows:

E-0 =

(𝐄−𝟏% ∗ 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞𝟏 + 𝐄−𝟐 ∗ 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞𝟐 ) (𝑰𝒎𝒑𝒐𝒓𝒕𝒂𝒏𝒄𝒆𝟏 +𝑰𝒎𝒑𝒐𝒓𝒕𝒂𝒏𝒄𝒆𝟐 )

Where Importance1 and Importance2 are the weights assigned to each sub-indicator.

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Indicator E-1: Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 Two options are suggested below. Option 1 is the minimum requirement to measure Target E. Option 2 aims at measuring the degree to which that the DRR Strategy is in alignment with the Sendai Framework for Disaster Risk Reduction. The process for setting targets and indicators is expected to take time, and so the introduction of a Progress Index will allow for the monitoring of continuing and gradual improvement in strategy development. Option 2 is a more complete assessment than Option 1. In all cases, countries report status information and UNISDR calculates the global figure. Option 1 (MINIMUM REQUIREMENT): Simply count the number of countries which reported on the adoption and implementation of the national DRR strategy in line with the Sendai Framework for Disaster Risk Reduction 20152030. Definition of the National DRR Strategy can be taken from the paragraph 27 (b) of the Sendai Framework. National DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030: national disaster risk reduction strategies and plans, across different timescales with targets, indicators and time frames, aimed at preventing the creation of risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience (Sendai Framework, para 27(b)). Additional concern is that coherence among DRR, climate change adaptation and sustainable development is strongly advocated in the Sendai Framework. The DRR strategies should be based on risk information and assessments. It is suggested that countries evaluate if their national DRR strategies satisfy the minimum requirement outlined in the Sendai Framework paragraph 27 (b).

Option 2. – National DRR Strategy Index (conservative): Create an index (temporarily called National DRR Strategy Index) to reflect progress in more detail by using quantitative indicators that can measure how comprehensive a national DRR strategy is, instead of measuring only its existence. Option 2.1. proposes the use of minimum criteria defined below as elements of the Progress Index. The criteria include elements of Paragraph 27 (b) of the Sendai Framework.

National DRR Strategy Index = Progress index1 + Progress Index2+…+ Progress Index n

148

Where n =number of countries reporting the progress on national DRR strategy The score of non-reporting countries is assumed as zero (not having national DRR strategy) and therefore not included in the formula. The Progress Index checks the degree to which the national DRR strategy satisfies the four elements defined in the Sendai Framework paragraph 27 (b). The four elements are: a) setting time frames and targets and indicators, b) setting objectives and measures aiming at preventing the creation of risk, c) setting objectives and measures aiming at the reduction of existing risk, and d) setting objectives and measures aiming at the strengthening of economic, social, health and environmental resilience. Each element is assigned 0.25 (25%). If a country has a DRR strategy satisfying the four elements, it is evaluated as 1. If a country reports the lack of DRR strategy, it is evaluated as 0. If a country has a contingency or preparedness plan which has objectives and measures aiming at the strengthening of economic, social, health and environmental resilience, but not addressing the prevention of risk creation and reduction of existing risk and also not having targets and indicators, then it is evaluated as 0.25. The score of National DRR Strategy index will increase when the number of countries reporting on the adoption and implementation of national DRR strategy increases and/or the quality of national DRR strategy improves to satisfy the definition of the DRR strategy outlined in the Sendai Framework. The four elements would have variation. For example, Members may choose to add a new element, for example being informed by risk assessment and information. However, it is not recommended to significantly change the four elements to be consistent with the Sendai Framework.

Options 2 is slightly more complicated than Option 1. However, without putting significant additional burden, it is possible to monitor improvement in quality of a national DRR strategy. Given that target and indicator setting is usually a process taking time, instead of evaluating a strategy which does not satisfy the requirements of the Sendai Framework as zero in Option 1, Option 2 provides the opportunity to assess gradual or partial progress.

149

Indicator E-2: Percentage of local governments that adopt and implement local DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 Four options are suggested below. Option 1 is the minimum requirement to measure Target E. Option 2 can measure the degree to which the DRR Strategy is in alignment with the Sendai Framework for Disaster Risk Reduction. The process for setting targets and indicators is expected to take time. The introduction of the Progress Index will allow monitoring such continuing and gradual improvement of the strategy development. The variations in size of local governments might need attention on population coverage of local strategies. In a country where the majority of small local governments adopt and implement local DRR strategies but one big local government does not have a local DRR strategy, a significant percentage of the national population will not be covered by local DRR strategies. From a humancentred perspective, option 3 is proposed to take population coverage into consideration. Option 4 is the combination of the Options 2 and 3 (Table 1). Table 1 Four options suggested

Option 1 Option 2 Option 3 Option 4

Progress (quality improvement) X X

Population coverage x x

The required information from countries for each option is as follows (Table 2). In all cases, countries report status information and UNISDR calculates the global average percentage. Table 2 Required information from countries for each option Option1 Option2 DRR strategy adoption and implementation X x (Required for all options) Progress Index (explained below) x (Required for options 2 and 4) Population Share Index (explained below) (Required for options 3 and 4)

Option3 x

Option4 X

-

x

x

x

Definition： It is currently proposed to define local government as a form of public administration at the lowest tier of administration [charged with the responsibility for disaster risk reduction] within a given state, which generally acts within powers delegated to them by legislation or directives of the higher level of government. It is proposed as the lowest possible tier with responsibilities for disaster risk reduction because there is significant variance between national level and municipality or village (lowest) levels, and in terms of sub-national governments between countries (e.g. state, prefecture, department, province, canton). Additionally, lowest tier governments are the closest to the citizens and serve as the base of resilience building. 150

Caution should be applied in using this indicator as administrative reform in a country will influence the percentage in each option by changing the total number of local governments. However, no solution can be found to this issue. In options 1 and 3, Local DRR Strategy is defined as local disaster risk reduction strategies and plans, across different timescales with targets, indicators and time frames, aimed at preventing the creation of risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience (Sendai Framework, para 27(b)). In Option 2 and 4, Local DRR strategy does not have clear definition in itself but the minimum criteria are defined as elements of the Progress Index. The criteria include the elements of Paragraph 27 (b) of the Sendai Framework. Additional concern in both cases is that coherence among DRR and climate change adaptation and sustainable development is strongly advocated in the Sendai Framework. The DRR strategies should be based on risk information and assessments. Option 1 (Minimum requirement): Calculate global average: Add each country’s percentage of local government adopting and implementing local DRR strategy and divide the value by the number of countries reported. Global average = (Percentage of Country1 + Percentage of Country2 +…+Percentage of Country n)

/ Number of countries reported Where n =number of countries reported National average = Number of local government adopting and implementing local DRR strategy /total number of local government in a country Definition of the Local DRR Strategy will be taken from paragraph 27 (b) of the Sendai Framework. Countries are suggested to evaluate if their local DRR strategies satisfy the minimum requirement outlined in the Sendai Framework paragraph 27 (b). Non-reporting local governments are assumed not to have a local DRR strategy.

Option 2. – Local DRR Strategy Index (conservative): Create an index (temporarily called Local DRR Progress Index) to reflect the national progress in local DRR Strategy quality, add each country’s Index and divide the value by the number of countries reported to calculate the global average. Global Average = (Local DRR Progress Index1+ Local DRR Progress Index2 +…+Local DRR Progress Index n) / Number of countries reported

151

Where n =number of countries reported Local DRR Progress Index in a country= Progress Index1 + Progress Index2+…+ Progress Index n / Total number of local government in a country Where n =total number of local governments in a country The score of non-reporting local governments is assumed as zero in terms of the Progress Index (not having DRR strategy). The Progress Index checks the degree to which the local DRR strategy satisfies four elements defined in the Sendai Framework paragraph 27 (b). The four elements are: a) setting time frames and targets and setting indicators, b) setting objectives and measures aiming at preventing the creation of risk, c) setting objectives and measures aiming at the reduction of existing risk, and d) setting objectives and measures aiming at the strengthening of economic, social, health and environmental resilience. Each element is assigned 0.25 (25%). If a local government has a DRR strategy satisfying the four elements, it is evaluated as 1. If a local government reports the lack of DRR strategy, it is evaluated as 0. If a local government has a contingency or preparedness plan which has objectives and measures aiming at the strengthening of economic, social, health and environmental resilience but not addressing the prevention of risk creation and reduction of existing risk and also not having targets and indicators, then it is evaluated as 0.25 (25%). The score of the Global Average will increase when more local governments report on the adoption and implementation of local DRR strategy and/or the quality of local DRR strategy improves to satisfy the definition of the DRR strategy outlined in the Sendai Framework. Options 2 is more complicated than Option 1, gathering accurate information on the specificities of local DRR strategies for aggregation at the national level will entail a marginally more significant reporting burden. However, in so doing, it is possible to monitor improvement in quality of a national DRR strategy. Given that target and indicator setting is usually a process taking time, instead of evaluating a strategy which does not satisfy the requirements of the Sendai Framework as zero in Option 1, Option 2 provides the opportunity to assess gradual progress.

152

Option 3: Create an index (temporarily called Local DRR Population Share Index) to reflect the population coverage of local DRR Strategies at national level, add each country’s Index and divide the value by the number of countries reported to calculate global average.

Global average= (Local DRR Population Share Index1+ Local DRR Population Share Index2 +…+ Local DRR Population Share Index n) / Number of countries reported Where n = number of countries reported Local DRR Population Share Index in a country= Local government (LG)1 * Population Share Index1 + LG2*Population Share Index2+…+LG n * Population Share Index n / Total number of local government in a country Where Local Government (LG): Binary value 1 or 0 is given. Local governments reporting the adoption/implementation of local DRR strategy in line with the Sendai Framework is given 1 while local governments reporting the lack of such a plan is given 0. Non-reporting local government is assumed as zero (not having DRR strategy). n = total number of local governments reported Population Share Index: the local government’s population share (%) in national population The score of the Global Average will increase when the number of local governments reporting the adoption and implementation of a local DRR strategy in line with the Sendai Framework increases and/or the population share of local governments adopting/implementing DRR strategy increases. This option is more complicated than option 1. However, without putting significant additional burden, it is possible to monitor the population coverage of local DRR strategy.

Option 4: Create an index (temporarily called Local DRR Progress Population Index) to reflect the quality improvement and population coverage of local DRR Strategies at national level, add each country’s Indexes and divide the value by the number of countries reported to calculate global average. Global average = (Local DRR Progress Population Index1+ Local DRR Progress Population Index2 +…+ Local DRR Progress Population Index n) / Number of countries reported n =number of countries reported 153

Local DRR Progress Population Index in a country= Local government (LG)1 * Progress Population Index1 + LG2*Progress Population Index2+…+LG n * Progress Population Index n/ Total number of local governments in a country Where Local Government (LG): Binary value 1 or 0 is given. Local governments reporting the adoption/implementation is given 1 while local governments reporting lack of the adoption/implementation is given 0. Non-reporting local government is assumed as zero (not having DRR strategy). n = total number of local governments in a country Progress Population Index: the Progress Index * Population Share Index The score of Global average will increase when (a) the number of local governments reporting the adoption and implementation of local DRR strategy increases, (b) the quality of local DRR strategy improves to satisfy the definition of the DRR strategy outlined in the Sendai Framework, and/or (c) the population share of local governments adopting/implementing DRR strategy increases. This option is the most complicated. Caution in interpretation will be required given the new elements that have been introduced. Examples: A. National Level Let us suppose an imaginary country having 4 local governments. The information reported from these 4 local governments for each option is as follows. Local 1 Local 2 Local 3 Local 4 DRR strategy adoption and Yes Yes Yes No implementation Progress Index (a) 100% 50% 75% 0 Population Coverage Index (b) 40% 30% 15% 15% Progress Population Index ((a)*(b)) 40% 15% 11.5% 0 Note: The table shows only Local government 1 satisfies definition of para 27 (b) of the Sendai Framework (Progress Index is 100%). Option 1 Percentage is 25% (1 out of 4 local governments having local DRR strategies satisfying 4 elements in paragraph 27 (b) of the Sendai Framework. Local governments 2 and 3 might have local DRR strategy but the strategies are not satisfying the 4 elements judging from the Progress Index. Therefore, they are evaluated as not satisfying the definition of local DRR strategy in alignment with the Sendai 154

Framework and therefore are scored as zero). Option 2 (1*100% + 1*50% + 1*75% + 0)/4=225/4 =56.25% Currently remaining 40% is attributed to no plan in local 4 and lacking elements in local 2 and 3. If the local 4 adopts and implements the plan satisfying all elements, then (1*100% + 1*50% + 1*75% + 1*100%)/4=325%/4 =81.25%. Remaining 18.75% can be remedied by quality improvement of plans in local 2 and 3. Option 3 (1*40% + 0% + 0% + 0%)/4 = 40%/4 = 10% (Only Local 1 satisfies the definition of local DRR strategy and is counted as 1, for 40% of population) Option 4 (1*40% + 1*15% + 1*11.5% + 0)*/4 = 66.5%/4 =16.63%

B. Global Level Let us suppose an imaginary world consisting of 3 countries. The information reported from these 3 countries for each option is as follows.

Option 1: DRR strategy adoption and implementation Option 2: Local DRR Progress Index Option 3: Local DRR Population Share Index Option 4: Local DRR Progress Population Index

Country 1 25% 60% 10% 18%

Country 2 50% 80% 50% 48%

Country 3 40% 50% 75% 60%

Option 1: (25% + 50% +40%)/ 3 = 38% Option2: (60% + 80% +50%)/ 3 = 63% Option3: (10% + 50% +75%)/ 3 = 45% Option4: (18% + 48% +60%)/ 3 = 42%

155

8. Critical issues, sources, data collection Source and data collection Summation of data from National Progress Reports of the Sendai Monitor reported by countries and local governments. Disaggregation  By country  By city (applying sub-national administrative units)  By sector

Comments and limitations Political commitment and leadership: The development or revision of the national/local DRR strategy may be led by a government institution or institutions with a cross-sectoral coordination mandate, strong convening power and able to take full ownership of the process. In order to ensure political support at the highest possible level, the planning exercise will ideally be backedup by an Executive Order, Ministerial Decree or similar instrument and be aligned with national/local legislation and regulations. Multi-sectoral consultation: The process of developing the initial draft of the national or local DRR strategy should engage relevant sectors (economy and finance, planning, housing and urban development, environment, infrastructure, education, health, agriculture, trade, tourism, energy, etc.) so as to define sector-specific activities and requirements, identify priority areas of intervention and promote integrated risk reduction within sectoral development plans and programmes. It may also entail consultation with local and regional governments, civil society, academic and private sector organizations as well as communities. Once the initial draft is available, it should be shared with all the stakeholders for revision and validation. Once validated, it should be presented for endorsement of Cabinet members through an executive order for the strategy’s implementation. Formal endorsement and approval: The national/local DRR strategy can then be submitted for approval by the national entity responsible for its development and later publication in the national register. The document will go through a final step of political endorsement and approval, following legislation and endorsement procedures, the national/local DRR strategy can be formally incorporated into development policies, identified as a national/local priority and be allocated financial resources. Baseline: 140+ countries undertook voluntary self-assessment of progress in implementing the Hyogo Framework for Action during the four reporting cycles to 2015 using the HFA Monitor, generating the world’s largest repository of information on national DRR policy inter alia. Its successor, provisionally named the Sendai Monitor, is under development. A baseline is expected to be created in 2016-2017 that will facilitate reporting on progress in achieving the relevant targets of both the Sendai Framework and the SDGs.

156

ANNEX I: Summary table of indicators and sub-indicators by Category Target E Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

E-0

E-1* E-1 alt.* E-2*

E-2 alt.*

E-2 alt-bis.*

E-2a*

E-3* E-3 alt.* E-4* E-4 bis.* E-4 ter.*

E-5*

E-5 alt.*

E-6*

Indicator

Number of countries that adopt and implement national and local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030. Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework for Disaster Risk Reduction 20152030. Number of countries with national DRR strategies for implementation of the Sendai Framework for DRR [Percentage of local governments that adopt and implement local DRR strategies in line with the [Sendai Framework for Disaster Risk Reduction 2015-2030 / national disaster risk reduction strategy]. [Number of countries and local governments that adopt and implement local and sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] [Percentage of local governments with DRR strategy for implementation of national strategy for the Sendai Framework for DRR.] [Percentage/number of local governments that have adopted or committed to the new 10 essentials defined in the UNISDR global campaign “Making Cities Resilient.”] [Number of countries that [integrate / integrated] [climate and disaster risk / climate change / adaptation] into [development planning / development plan].] [Number of countries that have integrated DRR and climate change into their national development plan.] [Number of countries that adopt and implement critical infrastructure protection plan.] [Number of countries with resilience programmes or strategies for health and education facilities in the framework of the DRR plans.] [Number of sector/hazard specific DRR strategy/plan developed in a country.] [Number of countries with cross-sectoral bodies/forums, with clear roles and responsibilities identified across state institutions, civil society, private sector and international actors, in the implementation and review of DRR measures.] [Number of countries that adopt and implement specific DRR strategies in line with the Sendai Framework for DRR, including through cross-sectoral bodies/forums with identified roles and responsibilities, as appropriate, for relevant actors.] [Number of countries accounting for future risk in public and private balance sheets, setting financial targets to inform investment strategies

Methodology

Data

Y

N

Y

Y

E-1

0

Y

N

E-2 / E-4 ter.

0

E-2

0

E-2

0

N

N

E-3

0

Y

N

E-4

0

E-8

0

Y

N

E-5

0

N

N

157

for reducing risk and enhancing future prosperity.] E-7*

E-8* E-9* E-10* E-11* E-12* E-13*

[Number of countries and local governments conducting (independent) periodic outcome reviews of the implementation of national and local DRR strategies.] [Number of countries that adopt and implement sector specific DRR strategies in line with the Sendai Framework for Disaster Risk Reduction.] [Number of countries that have national financing mechanisms for DRR.] [Number of countries that have spatial and land use planning mechanisms for DRR.] [Number of countries that mainstream DRR into national development planning.] [Number of people protected by evacuation, improved infrastructure and other relevant measures that reduce the possible impact of disasters on people.] [Number of people who received/require relief aid or assistance due to a hazardous event.]

Y

Y

Y

N

Y

N

N

N

E-3

0

N

N

Y

N

* Indicators marked with an asterisk (*) are extracted from the Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. All other indicators listed in this Annex and throughout this document are Secretariat proposals.

158

REFERENCES Comisión Nacional de Prevención de Riesgos y Atención de Emergencias (CNE). (2015). “Política Nacional de Gestión del Riesgo 2016-2030”. CR. San José, CNE. 2015. Democratic Socialist Republic of Sri Lanka (2010) National Policy on Disaster Management. Sri Lanka, National Council for Disaster Management. Disaster Risk Management Centre. Dr. Fadi H Hamdan (2012) Towards a national strategy for disaster risk management in the republic of Lebanon incorporating results of the initial national consultation process. Lebanon. Government of Malawi (2015) National Disaster Risk Management Policy. Malawi. Government of Nepal (2009) National Strategy for Disaster Risk Management. Nepal, Ministry for Home Affairs. Government of Pakistan (2013) National Disaster Risk Reduction Policy. Pakistan, Ministry of Climate Change, National Disaster Management Authority. Ministry of Agriculture (2013?) Disaster Risk Management Strategic Programme and Investment Framework. Ethiopia, Disaster Risk Management and Food Security Sector. ONEMI Ministry of the Interior and Public Safety (2014) National Policy for the Management of Risk of Disasters. Chile. Presidencia del Consejo de Ministros PCM (2014) Plan Nacional de Gestión del Riesgo de Desastres PLANAGERD 2014-2012. Perú. Presidencia del Consejo de Ministros PCM (2012) Política Nacional de Gestión del Riesgo de Desastres. Peru. Republic of Bulgaria (2014) Disaster Risk Reduction Strategy. Bulgaria. Republic of Turkey (2012) National Earthquake Strategy and Action Plan. Turkey, Prime Ministry Disaster and Emergency Management Presidency. Royal Government of Bhutan National Disaster Risk Management Framework. Bhutan, Department of Local Governance, Ministry of Home and Cultural Affairs. The Egyptian Cabinet Egypt (2010) National Strategy for Crisis/Disaster Management and Disaster Risk Reduction. Information and Decision Support Center. The Federal Democratic Republic of Ethiopia (2013) National Policy and Strategy on Disaster Risk Management. Ethiopia. United Nations (2015) The Sendai Framework for Disaster Risk Reduction 2015-2030 159

United Nation office for Disaster Risk Reduction (2015) Support to National Implementation of the Sendai Framework 2015 -2013. Geneva. Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016. Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 United Nations Office for Disaster Risk Reduction. Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. United Nations Office for Disaster Risk Reduction. Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015.

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Concept note on Methodology to Estimate the Availability of and Access to Multi-Hazard Early Warning Systems and Disaster Risk Information and Assessments to Measure the Achievement of Target G of the Sendai Framework for Disaster Risk Reduction: A Technical Guidance

10 June 2016

The United Nations Office for Disaster Risk Reduction

161

1. Overview This document outlines a computation methodology to estimate the availability of, and access to, multi-hazard early warning systems (MHEWS) and disaster risk information and assessments. The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OEIWG) requested the UNISDR to propose a methodology at its Second Session, held in Geneva on 10-11 February 2016. This document complements the “Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction” produced by the Secretariat on 23 December 2015. The purpose of this document is to support discussion by Member States on the selection and design of indicators to monitor progress and achievement of the global Target G of the Sendai Framework for Disaster Risk Reduction 2015-2030: Target G: Substantially increase the availability of and access to multi-hazard early warning systems and disaster risk information and assessments to the people by 2030 The methodologies described here are based on previous experience of a number of governments, academic and research institutions, private organizations and work of the United Nations. It is informed, inter alia, by experts from the World Meteorological Organization (WMO) and the National Meteorological and Hydrological Services (NMHSs) of its Members. The note also draws from the work that underpins the Global Assessment Reports on Disaster Risk Reduction (GAR) (UNISDR, 2009a, 2011, 2013 and 2015), including experience gained in developing and employing a probabilistic global risk model. The elements which effective MHEWS comprise, and which give rise to accessible risk information and assessment, are many and complex. They involve, inter alia, aspects of systematic detection, monitoring and forecasting of hazards, vulnerability and exposure, detailed analysis of the risks involved, supported by appropriate and effective means of communicating and disseminating risk information, from accountable authorities to populations at risk at the local level, such that it prompts appropriate action, all of which accompanied by the capability to prepare and respond in a timely manner. Capturing this complexity in the measurement of a global target is challenging. In their deliberations, Members will need to consider a number of important challenges: ▫

▫

▫

As MHEWS vary considerably from country to country, instead of counting the number of the systems that fully meet the four components (see below), the Secretariat suggests a focus on functionality (e.g. the degree of achievement) to measure progress in achieving the Target. The selection of principal hazards to be included in a MHEWS remains a national determination, in measuring the global Target and recognising that hazardous events differ greatly - from large-scale, often low-frequency events such as earthquakes, cyclonic winds, and tsunamis, to small-scale, high-frequency hazardous events such as floods. MHEWS generally have a defined scope and coverage that is specific to a particular geography or population. Determining progress in developing coverage could be an indicator that could assist the measurement of progress in achieving the global Target. 162

▫ ▫

▫

▫

In calculating coverage, Members will need to deliberate on the denominator to be used in computation, notably with regard to population coverage. Ideally, it is the exposed population that would be used; however, determining the exposed population will be challenging and the use of a proxy is suggested, that could be for example, the total population in targeted sub-national administrative units. As more than one MHEWS can cover the same geography or population, the risk of double counting is high. Members should consider the consistency of information to be collected if this is to be avoided. Identifying the availability of, and populations’ access to, risk information and assessments will be challenging. Notwithstanding, defining whether this is reaching the most exposed or vulnerable populations. The Secretariat therefore suggests a number of possible proxy indicators in this note. However, determining the number of exposed persons is extremely challenging, especially for small and medium-sized hazardous events and the fact that not everyone exposed is affected (see the note on Target B).

Given the challenges in assessing all key elements of available and accessible MHEWS, risk information and assessment, the Secretariat suggests that Members explore indicators that focus on evaluating the functional elements of each as a proxy for the Target. Such indicators can use widely available data that are consistent across countries as well as over time, and as such may be considered fit for purpose in measuring progress in achievement of Target G.

163

2. Summary The methodology proposes the use of simple and realistic indicators to measure the progress in increasing the availability of and access to multi-hazard early warning systems (MHEWS) and disaster risk information and assessments to the people by 2030. The methodology outlined here aims to produce an approximate value (a “proxy”) that provides a verifiable, consistent and homogeneously estimated measure of the number of people with access to MHEWS, risk information and assessments. Instead of eliciting binary responses on the existence or non-existence of an element of the Target, it may be possible to adopt quantitative indicators to measure quality; thereby measuring the level of progress. The proposal by several countries to address the degree of coverage of available risk information is supported by the Secretariat. However, Members will need to determine an appropriate denominator other than simply using data availability. Given the complexity and wide variation between countries in the elements and conditions that give rise to effective MHEWS and accessible risk information and assessment, the Secretariat suggests the following. With regards to MHEWS, the Secretariat suggests that the outcome of the Third International Conference on Early Warning 2006 be used as the basis for the development of such indicators. The Secretariat suggests using the four indispensable components of effective EWS as the basis for proposed global indicators, all of which need to be coordinated across many agencies at national to local levels: 1) risk knowledge, 2) monitoring and forecasting , 3) dissemination and communication, and 4) preparedness. The differing characteristics of MHEWS from country to country require a multi-faceted approach, simply counting the number of countries with MHEWS that fully meet the four components is not technically recommended, instead a focus on the degree of achievement to measure progress in achieving the Target is proposed. In this regard, the Secretariat proposes several options including increment measurements for achievement judged by the widely agreed and recognized MHEWS Checklist. The population or geographical coverage and combined with above method would be an another option, however, the exposure as a denominator is usually hard to be obtained, therefore the Secretariat does not recommend it as a global index due to its complexity. Each country should specify the major hazards to be included in a "multi-hazard" EWS, and indicators should be weighted accordingly when reporting, reflecting the extent of physical / economic damage accorded to each hazard. The Secretariat therefore proposes the use of a compound indicator G-1, which would be computed based on indicators G-2 through G-4 and G-6. Indicator G-1 is a headline and compound indicator of the sub-indicators for the four components of MHEWS. The Secretariat suggests a quantitative indicator with four levels of overall effectiveness of the MHEWS measuring the extent to which it meets the four components by according an equal weighting for each component. Another option is computed using an arithmetic average of the scores of the indicators G-2 through G-4 and G-6, and then computing the weighted average by hazard. 164

As regards measuring disaster risk information and assessments, again simply counting the number of countries with an assessment or risk information is not technically recommended, instead a multi-faceted approach is proposed. The Secretariat proposes a number of options beyond a simple binary consideration, that seek to measure the quality of the multi-hazard national disaster risk information and assessments by appraising overall levels of effectiveness. Additional options are provided to measure coverage in addition to quality. It is suggested that in measuring quality, countries assess the extent to which the disaster risk information and assessments meet the five categories of Risk Knowledge as developed for the Third International Conference on Early Warning (ISDR 2006). The Secretariat therefore proposes the use of indicator G-5. The data required to report against the above indicators for both MHEWS and risk assessment / risk information are currently not widely available. In many instances, the hazards to be included in a MHEWS will need to be identified, and an initial assessment of the four components of a MHEWS will need to be undertaken. Likewise, where national disaster risk information and assessments are available, an initial appraisal of quality and potentially coverage may be required. Such efforts can be supported by technical groups and initiatives, including those supporting the development of MHEWS.

The following table summarises the recommendations by the Secretariat with regard to the indicators proposed by Member States and described in the Working Text on Indicators based on negotiations during the Second Session of the OEIWG. Indicators are grouped by: ▫

Recommended

– for measurement at the global level;

▫

Recommended

– for measurement at the national level; or

▫

Not Recommended

– for reasons of feasibility, duplication, lack of a globally applicable methodology, inter alia.

165

No.

Indicator

Methodology

Data

B-1

Recommended - for measurement at the global level

Y

Y

G-1

Number of countries that have multi-hazard early warning system.

Y

N

Y

N

Y

N

Y

N

Y

N

Y

N

G-2 G-3 G-4 G-5

G-6

Number of countries that have multi-hazard monitoring and forecasting system. Number of people who are covered by and have access to multi-hazard early warning system per 100,000 Number of local governments having a preparedness plan (including EWS) or evacuation plan with standard operating procedures. Number of countries that have multi-hazard national risk assessment / information, with results in an accessible, understandable and usable format for stakeholders and people. Number of local governments that have multi-hazard risk assessment / risk information, with results in an accessible, understandable and usable format for stakeholders and people.

Recommended - for measurement at the national level G-7alt

Number of countries with programmes for the disaster risk perception and understanding of the population.

N

N

B-1

Not Recommended

Y

Y

N

N

G-5 alt. G-5a G-7 G-8 G-9 G-10 G-11 G-12 G-13 G-14

[Multi-hazard risk information system capable of providing information in a simple and usable format to common people] [Number of countries with national risk assessment for G5 and mapping reports at national and local level.] [Percentage of population with understanding of the risk they are exposed to.] [Number of countries that have national plans with budget and timeline for development of multi-hazard EWS.] [Number of countries that have disaster loss databases publicly accessible.] [Number of countries that have open data policies and mechanisms to make hazard and risk data accessible and available to all users.] [How many countries provide basic weather, environmental and climate services, as defined by the World Meteorological Organization.] [Percentage of people in local communities able to use indigenous knowledge of the risk they are exposed to.] [Percentage of local communities trained in community based multi hazard early warning management system and response.] [Number of programmes to enhance awareness, disaster risk information and risk assessment.]

G-5 N

N

Target E Targets A-D N

N

N

N

N

N

G-4 G-7 alt.

0

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3. Technical Requirements for an indicator to measure “Availability of and Access to Multi-Hazard Early Warning Systems and Disaster Risk Information and Assessments” Indicators proposed to measure targets in this and other frameworks are numbers that give an indication of the nature of the aspect to be measured18, in this case they estimate the availability and access to MHEWS, risk information and assessment. It is important to emphasize that no indicator will provide an absolutely precise, accurate and exhaustive measure of access and availability. It would be impossible to remove a certain degree of uncertainty or inaccuracy from such estimations, for which the sourcing of data is subject to numerous country-specific variables. The indicators to measure Target G of the Sendai Framework aim to meet the following important criteria: Consistent over time: The Target requires the comparison of progress over the entire period of the Sendai Framework (2015-2030), nevertheless, monitoring should occur throughout the period of 25 years including the decade of the Hyogo Framework (2005-2015) in order to obtain a continuous view of the progress of implementation and achievements, and data must be recorded and reported in a consistent way for the cycle of measurement, and so avoid introducing biases. Consistent across countries: It must be a) applicable to any country in the world, to the maximum degree possible, allowing comparison among countries or regions, and b) feasible, such that data can be obtained regardless of the level of development or income of each country. SMART: Specific, Measurable, Achievable, Relevant, Time Bound. Reliable: Results can be trusted. Transparent: The methodology used is well known, with caveats declared, and for which weaknesses, limitations and strengths, including economic assessment biases, are identified. Verifiable: Data can be traced back to official sources. Feasible: Easy to collect data in a practical and realistic way, without imposing an extraordinary or even impossible burden to countries. Taking advantage of existing data: Many countries have already collected relevant data. Taking advantage of this fact is more practical than having everyone start from zero. Useful: Results can be used not only for measuring the achievement of targets but also for DRR strategy planning, awareness raising, risk assessments and the development of DRR and related policies.

18

http://www.oxforddictionaries.com/es/definicion/learner/indicator

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4. List of Proposed Indicators to Measure Target G The following indicators are taken from the Working Text on Indicators, based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 Proposed indicators are classified under the two categories: 1) MHEWS, and 2) disaster risk information and assessments. 1) Possible indicators for MHEWS: G-1 - Number of countries that have [coordinated] multi-hazard early warning system. (This index should be computed based on indicators G-2 through G-4 and G-6) [G-2 – Number of countries that have [coordinated] multi-hazard monitoring and forecasting system.] G-3 – [Number / percentage] of people who are covered by [and have access to] multi-hazard early warning system [per 100,000]. [G-4 – [Percentage / Number] of [local] [and national] governments having preparedness plan (including EWS response and evacuation components) or evacuation plan [tested on regular basis] [and standard operating procedures].] [G-6 – [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] * note that G-6 is considered as a part of both MHEWS and risk assessments. [G-8 - Number of countries that have national plans with budget and timeline for development of multi-hazard EWS.] [G-13 – Percentage of local communities trained in community based multi hazard early warning management system and response.] The table below organises sub-indicators by the four indispensable components for an effective functioning MHEWS that must be coordinated across sectors and multiple levels of governments.

risk knowledge and risk assessment detection, monitoring, analysis and forecasting of the hazards dissemination and communication of timely, accurate and actionable warnings & associated likelihood and impact information preparedness and local capabilities to respond to the warnings received

Proposed Indicator G-6* G-2 G-3 G-4, G-13

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2) Possible indicators for risk information and assessment: [G-5 - [Number / percentage] of countries that have [multi-hazard national risk assessment / risk information] with results in an accessible, [understandable and usable] format for stakeholders and people.] [G-5 alt. - Multi-hazard risk information system capable of providing information in a simple and usable format to common people] [G-5a – Number of countries with national risk assessment for G5 and mapping reports at national and local level.] [G-6 – [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] * note that G-6 is considered as a part of both MHEWS and risk assessments. [G-7 - Percentage of population with understanding of the risk they are exposed to.] [G-7 alt. Number of countries with programmes for the disaster risk perception and understanding of the population.] [G-9 - Number of countries that have disaster loss databases publicly accessible.] [G-10 - Number of countries that have open data policies and mechanisms to make hazard and risk data accessible and available to all users.] [G-11 – How many countries provide basic weather, environmental and climate services, as defined by the World Meteorological Organization.] [G-12 – Percentage of people in local communities able to use indigenous knowledge of the risk they are exposed to.] [G-14 – Number of programmes to enhance awareness, disaster risk information and risk assessment.]

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5. Applicable Definitions and Terminology Sendai Framework Target G specifically requires a substantial increase in “the availability of and access to multi-hazard early warning systems and disaster risk information and assessments to the people by 2030” to be estimated, therefore and for the purposes of this methodology, related key terms are defined as proposed by the Working Text on Terminology based on negotiations during the Second Session of the OEIWG held in Geneva, Switzerland from 10-11 February 2016, issued on 3 March 2016, reissued with factual corrections on 24 March 2016.

Contingency planning: A management process that analyses emerging disaster risks and establishes arrangements in advance to enable timely, effective and appropriate responses. Annotation: Contingency planning results in organized and coordinated courses of action with clearly identified institutional roles and resources, information processes, and operational arrangements for specific actors at times of need. Based on scenarios of possible emergency conditions or hazardous events, it allows key actors to envision, anticipate and solve problems that can arise during crises. Contingency planning is an important part of overall preparedness. Contingency plans need to be regularly updated and exercised. Early warning system: An [interrelated / integrated] set of hazard warning, risk assessment, [communication and preparedness activities] that enable individuals, communities, businesses and others to take timely action to reduce their risks. Annotations: Effective “end-to-end” and “people-centred” early warning system comprises four interrelated key elements: 1) risk knowledge and risk assessment; 2) detection, monitoring, analysis and forecasting of the hazards and possible consequences; 3) dissemination and communication of timely, accurate and actionable warnings and associated likelihood and impact information; and 4) preparedness and local capabilities to respond to the warnings received. The expressions “end-to-end” and “people-centred” early warning systems are also used to emphasize that early warning systems need to span all steps from hazard detection to user/sector- specific warning reaching a threatened community to take action. These four interrelated components need to be coordinated within and across sectors and multiple levels for the system to work effectively. Failure in one component or lack of coordination across them could lead to the failure of the whole system. Additional annotation from the Secretariat: Early warning systems can be developed for specific hazards and specific consequences or for multiple hazards and a range of impacts. The latter are termed multi-hazard early warning systems and are designed to be used in multi-hazard contexts where hazardous events may occur simultaneously or cumulatively over time, and taking into account the potential interrelated effects. A multi-hazard early warning system increases the efficiency and consistency of warnings through coordinated and compatible mechanisms and capacities, involving multiple disciplines for updated and accurate hazards identification and monitoring. [Alt. Early warning system (EWS): The set of capacities and processes needed to generate, disseminate and use timely, accurate, actionable and inclusive warning information to enable

170

individuals, communities and organizations to appropriately and in sufficient time prepare for and respond to a hazard in order to reduce the possibility of harm or loss.] Annotation: An early warning system necessarily comprises four interrelated key elements: a) risk knowledge; b) detection, monitoring and forecasting of the hazard(s) and respective risk assessments and generation of the warning message(s); c) dissemination and communication of timely, accurate, actionable and inclusive warnings and associated likelihood and impact information; and d) preparedness and response capabilities at all levels. The expressions “end-toend” and “people-centred” early warning systems are also used to emphasize that early warning systems need to span all steps from hazard detection to user-/sector-specific warning development through to community response. These four interrelated components need to be coordinated within and across sectors and multiple levels for the system to work effectively. Failure in one component or lack of coordination across them could lead to the failure of the whole system. Roles and responsibilities of the various public and private stakeholders for the implementation of an early warning system need to be clarified and reflected in the respective national to local institutional and legal frameworks and planning. Early warning systems may exist for specific hazards (e.g. flash floods) or for specific consequences (e.g. famine, disease). They may also involve international cooperation to address transboundary risks, such as floods, epidemics and the release of hazardous materials into the air or water." (WMO)]. Forecast: Definite statement or statistical estimate of the likely occurrence of a future hazardous event or conditions for a specific area. Annotation: In meteorology a forecast refers to a future condition, whereas a warning refers to a potential occurrence of a hazardous event. [Hazard: A process, potentially damaging physical event, phenomenon or human activity that may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation. Annotation: Hazards can include latent conditions that may represent future threats and can have different origins: natural (geological, hydro-meteorological and biological) or induced by human processes (environmental degradation and technological hazards). Hazards can be single, sequential or combined in their origin and effects. Each hazards is characterised by its location, intensity, frequency and probability.] Additional annotation from the Secretariat: Hazards may be anthropogenic, natural or socio-natural in origin. Man-made or anthropogenic hazards are induced entirely or predominantly by human activities and choices. The range of manmade hazards may include technological and biological hazards. They are distinguished from natural hazards and exclude conflict and terrorism. Natural hazards are predominantly associated with natural processes and phenomena and may be characterized by their magnitude or intensity, speed of onset, duration, and area of extent. For example, earthquakes have short durations and usually affect a relatively small region, whereas droughts are slow to develop and fade away and often affect large regions. Several hazards are socio-natural in that they are associated with a combination of natural and anthropogenic factors, including environmental degradation, climate change and others. Hazards include hydro-meteorological, geological, biological, technological and environmental processes and phenomena. 171

[Hazardous Event: The occurrence of a natural or human-induced phenomenon in a particular place during a particular period of time due to the existence of a hazard. Annotation: Severe hazardous event(s) could lead to a disaster as a result of the combination of hazard occurrence and risk factors.] [Alt. Hazardous Event: The occurrence of a natural, technological and biological phenomenon in a particular place during a particular period of time during the existence of a hazard.] Local Government: Form of public administration at the lowest tier of administration within a given state, which generally acts within powers delegated to them by legislation or directives of the higher level of government. Multi-hazard: addressing (1) selection of multiple major hazards that the country faces, and (2) specific contexts where hazardous events may occur simultaneously or cumulatively over time, and taking into account the potential interrelated effects. [Alt. Multi-hazard early warning system: A type of early warning system that provides and uses common capacities to prepare for and respond to several hazards, including those occurring simultaneously or cumulatively over time, and takes into account the potential interrelated effects. ] Annotation: As early warning systems for specific hazards have many common elements, a multihazard early warning system increases the efficiency and consistency of warnings. Since hazards are often interrelated, a multi-hazard early warning system also ensures that information about hazards which occur together or sequentially are addressed in a shared system using common procedures. It uses risk information from multiple sources and integrates technical, social and financial capacities through coordination mechanisms among multi-disciplinary stakeholders, including effective feedback mechanisms for continuous improvement." (WMO definition and annotation). [Preparedness: The knowledge and capacities developed by governments, professional response and recovery organizations, communities and individuals to effectively anticipate, respond to, and recover from, the impacts of likely, imminent or current disasters. Annotation: Preparedness action is carried out within the context of disaster risk management and aims to build the capacities needed to efficiently manage all types of emergencies and achieve orderly transitions from response through to sustained recovery. Preparedness is based on a sound analysis of disaster risks and good linkages with early warning systems, and includes such activities as contingency planning, stockpiling of equipment and supplies, the development of arrangements for coordination, evacuation and public information, and associated training and field exercises. These must be supported by formal institutional, legal and budgetary capacities. The related term “readiness” describes the ability to quickly and appropriately respond when required.] Preparedness plan: Plan that establishes arrangements in advance to enable timely, effective and appropriate responses to specific potential events or emerging situations that might threaten society or the environment. 172

[Risk assessment: An approach to determine the nature and extent of risk by analysing potential hazards and evaluating existing conditions of vulnerability that together could potentially harm exposed people, property, services, livelihoods and the environment on which they depend. Annotation: Risk assessments (and associated risk mapping) include: a review of the technical characteristics of hazards such as their location, intensity, frequency and probability; the analysis of exposure and vulnerability including the physical social, health, economic dimensions, [environmental impact assessment,] and the evaluation of the effectiveness of prevailing and alternative coping capacities in respect to likely risk scenarios. This series of activities is sometimes known as a risk analysis process. ISO [31000 / 73: 2009] defines risk assessment as a process made up of three processes: risk identification, risk analysis, and risk evaluation. ▫ Risk identification: process that is used to find, recognize, and describe the risks that could affect the achievement of objectives. ▫ Risk analysis: process that is used to understand the nature, sources, and causes of the risks that have been identified and to estimate the level of risk. It is also used to study impacts and consequences and to examine the controls that currently exist. ▫ Risk evaluation: process that is used to compare risk analysis results with risk criteria in order to determine whether or not a specified level of risk is acceptable or tolerable.] Risk information: Comprehensive information on all dimensions of risk including hazards, exposure, vulnerability and capacity related to persons, communities, organizations and countries and their assets. Annotation: Risk information includes all studies, information and mapping required to understand the risk drivers and underlying risk factors.

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6. Notes from the Secretariat on comments and proposals from Member States G-1 - Number of countries that have [coordinated] multi-hazard early warning system. [G-2 – Number of countries that have [coordinated] multi-hazard monitoring and forecasting system.] The Secretariat would suggest to omit the term “coordinated” as this is considered implicit to an EWS (see the Terminology Text). The indicator will need to be further defined to determine if it is inclusive of detection and observation systems / networks. G-3 – [Number / percentage] of people who are covered by [and have access to] multi-hazard early warning system [per 100,000]. It will be extremely challenging to measure the number/ percentage of people who “have access to” an EWS. In the event that measurement is possible, for example if a MHEWS covers a small area (e.g. small island), determining the percentage coverage of the population would be important. [G-4 – [Percentage / Number] of [local] [and national] governments having preparedness plan (including EWS response and evacuation components) or evacuation plan [tested on regular basis] [and standard operating procedures].] Members may conclude that the measurement of this indicator is facilitated by focusing only on local governments. The Secretariat recognises that it is desirable to be able to determine the degree to which preparedness and evacuation plans that include EWS components are underpinned by standard operating procedures. Determining how frequently preparedness and evacuation plans are tested is also desirable, but this entails a degree of detailed appraisal beyond that observed in other indicators, and Members should be aware of the additional reporting burden that this will entail. [G-5 - [Number / percentage] of countries that have [multi-hazard national risk assessment / risk information] with results in an accessible, [understandable and usable] format for stakeholders and people.] [G-6 – [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] The measurement of progress in access to risk information and assessment at both the national level (G-5) and the local level (G-6) is advised, as access, coverage and application can differ significantly at each level. The use of terms “understandable and usable” that risk subjective interpretation are not advised.

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[G-5 alt. - Multi-hazard risk information system capable of providing information in a simple and usable format to common people] [G-5a – Number of countries with national risk assessment for G5 and mapping reports at national and local level.] G-5alt includes subjective elements that will prove difficult to measure. The Secretariat considers indicator G-5a as part of G5. [G-7 - Percentage of population with understanding of the risk they are exposed to.] [G-7 alt. Number of countries with programmes for the disaster risk perception and understanding of the population.] G-7 would be extremely difficult to measure objectively at the global level. G-7alt implies a level of detail that will prove challenging to measure on a systematic basis, but could be considered in the context of national DRR strategies, under Target E, or for use in measuring nationally appropriate targets. [G-8 - Number of countries that have national plans with budget and timeline for development of multi-hazard EWS.] This indicator seeks to measure the resourcing and sustainability of established EWS, however such plans are rarely developed in isolation from other strategies and plans, and as such measurement may be problematic. The Secretariat suggests that this aspect could be included in guidelines for measuring G-1 or indeed the relevant aspect of national DRR strategies, under Target E. [G-9 - Number of countries that have disaster loss databases publicly accessible.] [G-10 - Number of countries that have open data policies and mechanisms to make hazard and risk data accessible and available to all users.] Both G-9 and G-10 are input indicators that would provide an indication of the degree to which data should be collected and provided, however neither would measure if data are actually made available and accessed, nor if data is provided in an understandable format that prompts an appropriate action. This indicator is implicitly measured by country reporting on Targets A through D. G-11 – How many countries provide basic weather, environmental and climate services, as defined by the World Meteorological Organization. This requires further definition to determine what it seeks to measure, and whether it responds to the requirements of the Target in terms of disaster risk and EWS. The Global Framework for Climate Services (GFCS) states that weather and hydrological services enable short term preparedness and response to hazard events, and describes climate information/services at the seasonal and decadal timescales as essential for long-term planning purposes. However, there is no official WMO definition for ‘basic weather, environmental and climate services’.

175

[G-12 – Percentage of people in local communities able to use indigenous knowledge of the risk they are exposed to.] [G-13 – Percentage of local communities trained in community based multi hazard early warning management system and response.] These indicators will prove extremely challenging to measure, and the reporting burden on countries could be significant. Furthermore, the definition of the term “local communities” and “community based” will be required. “Communities” may not manage the warning rather will manage the response – this is implicit within a MHEWS. These indicators can be considered within indicator G-4. [G-14 – Number of programmes to enhance awareness, disaster risk information and risk assessment.] This input indicator would provide an indication of the degree to which progress is being made in communication, dissemination and outreach to populations and therefore may be desirable. However, it does not allow determination of success in uptake, including by most exposed. Such a quantitative measure cannot measure the impact of such programmes, which may only be possible to measure at the national / local level and could be integrated in G-7alt.

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7. Computation Methodology Given the subjective nature of many of the suggested indicators, it will be important to strike the balance between precision and practicality; while recognising that no indicator will provide an absolutely precise, accurate and exhaustive measure of the Target. Simply counting the number of countries (with MHEWS or risk assessment) is not technically recommended; this is particularly the case for indicators such as those for the Target G, which require a multi-faceted approach. Instead, indicators to measure global and national progress should be proposed. Members are encouraged to discuss aspects of population coverage so as to ensure a people-cantered approach. Recognising that hazards vary significantly between countries (for example, in terms of both frequency and intensity), and that national and sub-national variables are also a reality, the Secretariat suggests that each country specify the major hazards to be included in "multi-hazard" when reporting. The use of a weighting that can reflect the extent of physical / economic damage accorded to each hazard by each country is suggested for use with all the following proposed methodologies; in so doing, a weighted average can be calculated for all multi-hazard indicators. For example, a country has two major hazards, earthquakes and floods, for which it determines a weighting of 70:30 respectively. In the case of earthquakes, the MHEWS indicator scores 0.50. In the case of floods, the MHEWS indicator scores 0.75. The weighted average for the country’s MHEWS indicator is then calculated by: (earthquakes) 0.70 x 0.50 + (floods) 0.3 x 0.75 = 0.575

A. Indicators for multi-hazard early warning system (MHEWS) G-1 - Number of countries that have [coordinated] multi-hazard early warning system. Five options are suggested below: Option 1 is the MINIMUM REQUIREMENT to measure Target G – count the number of countries which have complete and effective MHEWS, aligned with the Sendai Framework. Option 2 is the RECOMMENDED OPTION and measures the quality of the MHEWS. Since the process for implementation and development of MHEWS will take time, the introduction of such index will allow for monitoring continuing and gradual improvement. (a Progress Index) Option 3 considers population or geographical coverage of any MHEWS. This will allow for monitoring MHEWS development of the target population / area. (a Coverage Index). Option 4 reflects quality in more detail by combining Options 2 and 3 to reflect improvement and population/geographical coverage of MHEWS (a Progress Coverage Index). Option 5 is a compound indicator, composed of sub-indicators G-2, G-3, G-4 and G-6.

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Option 1: The option evaluates the existence of a complete and effective MHEWS on a Yes/No basis, and simply counts the number of countries which have MHEWS aligned with the Sendai Framework. A complete and effective MHEWS must meet all four essential components of MHEWS: 1) risk knowledge and risk assessment; 2) detection, monitoring, analysis and forecasting of the hazards and possible scenarios; 3) dissemination and communication of timely, accurate and actionable warnings and associated likelihood and impact information; and 4) preparedness and local capabilities to respond to the warnings received. If a country has MHEWS which satisfies all four components, it will be counted. Otherwise, the score will be 0. The global average is calculated by dividing the total number of countries by the number reporting. 𝑮𝒍𝒐𝒃𝒂𝒍 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 =

∑

𝑛

∑𝑚 𝑗=1 𝑊𝑖𝑗⁄ 𝑖=1 𝑛

Where: Wij: weighted average of hazard j (=1, .., m) in country i (=1, .., n) Only when hazard j of MHEWS meets full components, otherwise 0 m: number of hazards n: number of countries Option 2: This option suggests quantitative indicators to measure the quality of the MHEWS, rather than existence. The Secretariat proposes the following scales with 4 levels of overall effectiveness of the MHEWS, in addition to registering its absence. i. Comprehensive achievement; full score, 1.0 point, ii. Substantial achievement, additional progress required; 0.75 point, iii. Moderate achievement, neither comprehensive nor substantial; 0.50 point, iv. Limited achievement; 0.25 point, v. No / poorly functioning MHEWS; no score, 0 point. * These criteria are reflected by the Level of Progress in the HFA Monitor. In order to measure quality, it is proposed to measure the extent to which MHEWS meets the “four components” by according an equal weighting for each component. As mentioned above, if the MHEWS lacks a single component, the MHEWS will be dysfunctional. It is proposed that scoring follows the following protocol, so that when MHEWS meets: i. all four components fulfilled; 1.0 point, ii. three components fulfilled + one not; 0.75 point, iii. two components fulfilled + two not; 0.50 point, iv. one component fulfilled + three not; 0.25 point, v. no component; 0 point. 178

In other words, each component is assigned 0.25. A clearer definition of each level is provided below under each sub-indicator. This index is more complicated than Option 1, however, it enables monitoring the improvement in quality of MHEWS. The index can reflect progress as the score of a global average will increase when (a) the number of countries report the adoption and implementation of MHEWS, and (b) the quality of MHEWS improves to satisfy the definition of MHEWS outlined in the Sendai Framework. The global average can be calculated through summation of each country’s indices and dividing the value by the number of reporting countries.

𝑮𝒍𝒐𝒃𝒂𝒍 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 =

∑

𝑛

∑𝑚 𝑗=1 𝐺𝑖𝑗 × 𝑊𝑖𝑗⁄ 𝑖=1 𝑛

Where: Gij: the score of the indicator G-1 of hazard j(=1, .., m) in country i(=1, .., n) Wij: weighted average of hazard j in country i; ∑𝑚 𝑗=1 𝑊𝑖𝑗 = 1 m: number of hazards n: number of countries *Option 1 is a binominal case of Gij.

Option 3: This option reflects coverage of complete and effective MHEWS meeting the four components. There are several ways to address the degree of available coverage of MHEWS. In applying population coverage, the ideal denominator would be the number of exposed population. However, obtaining such data is difficult, so a proxy would be applied, e.g. the total population in the country or in targeted sub-national administrative units. The geographical area covered is another option. In this case the denominator would be the total area of the country or targeted sub-national administrative units.

𝑮𝒍𝒐𝒃𝒂𝒍 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 =

∑

𝑛 𝑖=1

∑𝑚 𝑗=1 𝐶𝑖𝑗 × 𝑊𝑖𝑗⁄ 𝑛

Where: Cij: coverage of hazard j (=1, .., m) in country i (=1, .., n) Only when hazard j of MHEWS meets full components, otherwise 0 Wij: weighted average of hazard j in country i; ∑𝑚 𝑗=1 𝑊𝑖𝑗 = 1 m: number of hazards n: number of countries

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Option 4: By combining Options 2 and Options 3, the improvement in quality and population / geographical coverage of any MHEWS can be measured. However, due to its complexity the Secretariat does not recommend it as a global index.

𝑮𝒍𝒐𝒃𝒂𝒍 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 =

∑

𝑛 𝑖=1

∑𝑚 𝑗=1 𝐺𝑖𝑗 × 𝐶𝑖𝑗 × 𝑊𝑖𝑗⁄ 𝑛

Where: Gij: the score of the indicator G-1 of hazard j(=1, .., m) in country i(=1, .., n) Wij: weighted average of hazard j in country i; ∑𝑚 𝑗=1 𝑊𝑖𝑗 = 1 Cij: coverage of hazard j (=1, .., m) in country i m: number of hazards n: number of countries *Option 3 is a binominal case of Gij.

Option 5: The Secretariat recommends that the headline indicator, G-1, should be computed based on indicators G-2 through G-4 and G-6. Members may wish to consider a compounding methodology that entails computing the arithmetic average of the scores of the indicators G-2 through G-4 and G-6, and then computing the weighted average by hazards j in the targeted country i, so that: 𝑛

(∑4𝑘=1 ∑𝑚 𝑗=1 𝐼𝑁𝐷𝑖𝑗𝑘 × 𝑊𝑖𝑗⁄ 4)⁄

∑ 𝑮𝒍𝒐𝒃𝒂𝒍 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 =

𝑖=1

𝑛

Where: INDijk: the score of the indicator G-k of hazard j(=1, .., m) in country i(=1, .., n) Wij: weighted average of hazard j in country i; ∑𝑚 𝑗=1 𝑊𝑖𝑗 = 1 k: 1 to 4 (G-k stands for G-2, G-3, G-4, and G-6) m: number of hazards n: number of countries

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[G-2 – Number of countries that have [coordinated] multi-hazard monitoring and forecasting system.] The methodology for each compound indicator can be that used for G-1, computing the indicator by calculating a weighted average of hazards determined by each country. Option 1: MINIMUM REQUIREMENT Simply count the number of countries with a multi-hazard monitoring and forecasting system.

Option 2: RECOMMENDED This option suggests quantitative indicators to measure the quality of the multi-hazard monitoring and forecasting system, beyond simply existence. The Secretariat proposes the following scales with 4 levels of overall effectiveness of the multihazard monitoring and forecasting system, in addition to registering its absence. i. Comprehensive achievement; full score, 1.0 point, ii. Substantial achievement, additional progress required; 0.75 point, iii. Moderate achievement, neither comprehensive nor substantial; 0.50 point, iv. Limited achievement; 0.25 point, v. No / poorly functioning multi-hazard monitoring and forecasting system; no score, 0 point. * These criteria are reflected by the Level of Progress in the HFA Monitor. In order to measure quality, it is proposed that countries measure the extent to which the multihazard monitoring and forecasting system meets the checklist developed as a contribution to the Third International Conference on Early Warning (ISDR 2006). The Checklist of Key Element 2: Monitoring and Warning Service provides 3 categories: 1) Institutional Mechanisms Established, 2) Monitoring Systems Developed, and 3) Forecasting and Warning Systems Established. In this way, the quality of a MHEWS can be assessed for inter alia the density of observation networks, the number of hazards covered, or spatial and temporal resolutions.

Option 3: Same approach as G-1.

Option 4: The indicator could be calculated by combining Options 2 and 3 to reflect the improvement in quality and population or geographical coverage of the multi-hazard monitoring and forecasting system. As with G-1, this is complex and thus the Secretariat does not recommend using it as a global index.

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G-3 – [Number / percentage] of people who are covered by [and have access to] multihazard early warning system [per 100,000]. The methodology of this indicator is similar to that of G-1; a weighted average of hazards determined by country. Option 1: MINIMUM REQUIREMENT Simply count the number of people who are covered by MHEWS, or count in the same way and divide by total population. Option 2: RECOMMENDED This option suggests quantitative indicators to measure the quality of communication of the MHEWS, rather than existence. The Secretariat proposes the following scales with 4 levels of overall effectiveness of the MHEWS, in addition to registering its absence. i. Comprehensive achievement; full score, 1.0 point, ii. Substantial achievement, additional progress required; 0.75 point, iii. Moderate achievement, neither comprehensive nor substantial; 0.50 point, iv. Limited achievement; 0.25 point, v. No / poorly functioning MHEWS; no score, 0 point. In order to measure quality, it is proposed that countries measure the extent to which the communication system meets the checklist developed as a contribution to the Third International Conference on Early Warning (ISDR 2006). The Checklist of Key Element 3: Monitoring and Warning Service Dissemination and Communication provides 3 categories: 1) Organizational and Decision-making Processes Institutionalized, 2) Effective Communication Systems and Equipment Installed, and 3) Warning Messages Recognized and Understood. Option 3: The option proposes measuring population coverage as represented by the number of local governments covered by the MHEWS at national level. The computation methodology entails the summation of each country’s index and dividing the value by the number of reporting countries. Option 4: The indicator could be calculated by combining Options 2 and 3 to reflect the improvement in quality and population or geographical coverage of the MHEWS. As with G-1, this is complex and thus the Secretariat does not recommend using it as a global index.

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G-4 – [Percentage / Number] of [local] [and national] governments having preparedness plan (including EWS response and evacuation components) or evacuation plan [tested on regular basis] [and standard operating procedures]. The methodology of this indicator is similar to that of G-3; a weighted average of hazards determined by country. Option 1: MINIMUM REQUIREMENT Simply count the number of local [and national] governments which have preparedness plans (including EWS, response and evacuation components) or evacuation plans; or count in the same way and divide by the total number of governments. Measuring this indicator at the national, as well as the local level, may present computation challenges; Members may wish to consider focusing on the number/quality of local preparedness and evacuation plans. While desirable, the introduction of ‘tested on a regular basis’ entails a degree of subjectivity and detail that will present significant reporting challenges. Option 2: RECOMMENDED This option suggests quantitative indicators to measure the quality of preparedness and response plans, rather than existence. The Secretariat proposes the following scales with 4 levels of overall effectiveness of preparedness and response plans, in addition to registering its absence. i. Comprehensive achievement; full score, 1.0 point, ii. Substantial achievement, additional progress required; 0.75 point, iii. Moderate achievement, neither comprehensive nor substantial; 0.50 point, iv. Limited achievement; 0.25 point, v. No / poorly functioning preparedness and response plans; no score, 0 point. In order to measure quality, it is proposed that countries measure the extent to which preparedness and response plans meet the checklist developed as a contribution to the Third International Conference on Early Warning (ISDR 2006). The Checklist of Key Element 4: Response Capacity provides 4 categories: 1) Warnings Respected, 2) Disaster Preparedness and Response Plans Established, 3) Community Response Capacity Assessed and Strengthened, and 4) Public Awareness and Education Enhanced. Option 3: The option proposes measuring population coverage as represented by the number of local governments covered by preparedness and response plans. The computation methodology entails the summation of each country’s or each local government’s index and dividing the value by the number of reporting countries or local governments.

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Option 4: The indicator could be calculated by combining Options 2 and 3 to reflect the improvement in quality and population or geographical coverage of preparedness and response plans. Again, this is complex and thus the Secretariat does not recommend using it as a global index.

[G-6 – [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] See section B. Indicators for disaster risk information and assessments.

B. Indicators for disaster risk information and assessments The same methodology as that proposed for the indicators to measure MHEWS should be used; wherein the indicator is calculated as a weighted average of hazards by country.

[G-5 - [Number / percentage] of countries that have [multi-hazard national risk assessment / risk information] with results in an accessible, [understandable and usable] format for stakeholders and people.] Option 1: MINIMUM REQUIREMENT Simply count the number of countries with a multi-hazard national risk assessment / risk information, with results in an accessible format for stakeholders and people.

Option 2: RECOMMENDED This option suggests quantitative indicators to measure the quality of the multi-hazard national risk assessment / risk information, beyond simply existence. The Secretariat proposes the following scales with 4 levels of overall effectiveness of the multihazard national risk assessment / risk information, in addition to registering its absence. i. Comprehensive achievement; full score, 1.0 point, ii. Substantial achievement, additional progress required; 0.75 point, iii. Moderate achievement, neither comprehensive nor substantial; 0.50 point, iv. Limited achievement; 0.25 point, v. No / poorly functioning multi-hazard national risk assessment / risk information; no score, 0 point.

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In order to measure quality, it is proposed that countries measure the extent to which the multihazard national risk assessment / risk information meets the checklist developed as a contribution to the Third International Conference on Early Warning (ISDR 2006). The Checklist of Key Element 1: Risk Knowledge provides 5 categories; 1) Organizational Arrangements Established, 2) Natural Hazards Identified, 3) Community Vulnerability Analyzed, 4) Risks Assessed, and 5) Information Stored and Accessible. See the following examples of the elements required for multi-hazard national risk assessment / risk information (each level builds on the former): i. Risk assessment uses a probabilistic approach and is shared and coordinated, and used by national institutions with clear responsibilities for decision making, planning, and storing data and information; 1.0 point. ii. Risk assessment calculating possible losses and physical damage, as well as underlying risk drivers, of identified hazards, through vulnerability analysis with established organizational commitment; 0.75 point. iii. Risk assessment calculating possible losses and physical damage of identified hazards through vulnerability analysis; 0.50 point. iv. Evaluation of potential damage and loss scenarios with identification of: the main hazards to which the country is exposed, and the main exposed elements in prone areas; 0.25 point, v. Risk assessment / information unavailable; 0 point.

Option 3: This option proposes to reflect the coverage of multi-hazard national risk assessment / risk information by population coverage, the ideal denominator of which would be the number of exposed population. Geographical area covered is another option. In this case the denominator could be the total area of the country or targeted sub-national administrative units.

Option 4: The indicator could be calculated by combining the Options 2 and the Options 3 to reflect the quality improvement and population or area coverage of the multi-hazard national risk assessment / risk information. Again, the Secretariat would not recommend it due to its complexity as a global index.

[G-6 – [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] Use the methodology proposed for G-5 and adjust for local governments in place of countries.

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8. Critical issues, sources, data collection and statistical processing: Source and data collection Main source: National Progress Reports of the Sendai Monitor, reported to UNISDR. Disaggregation Further to the recommendations of both the OEIWG and the IAEG-SDGs, the Secretariat recommends disaggregating data: 

By country, by event, by hazard type (e.g. using the IRDR classification, natural hazards can be disaggregated as climatological, hydrological, meteorological, geophysical, biological and extra-terrestrial)



By sub-national administrative unit similar to municipality.

Comments and limitations National Progress Reports: 140+ countries undertook voluntary self-assessment of progress in implementing the Hyogo Framework for Action during the four reporting cycles to 2015 using the HFA Monitor, generating the world’s largest repository of information on national DRR policy inter alia. Its successor, provisionally named the Sendai Monitor, is under development. As there is no specific data addressing this indicator at this moment, a baseline is expected to be created in 2016-2017 that will facilitate reporting on progress in achieving the relevant targets of both the Sendai Framework and the SDGs.

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ANNEX I: Summary table of indicators and sub-indicators by Category Target G Compound Indicator: This indicator sums sub-indicators and is the principle measure of the Target Category I: Indicators for which an established methodology exists and data are already widely available in a significant number of countries Category II: Indicators for which a methodology has been established but for which data are not easily available Category III: Indicators for which an internationally agreed methodology has not yet been developed nor is data easily available This indicator duplicates another, or is included in proposals for other targets

Code

G-1 G-2 G-3

G-4

G-5 G-5 alt. G-5a G-6 G-7 G-7 alt. G-8 G-9 G-10 G-11 G-12 G-13 G-14

Indicator

Number of countries that have [coordinated] multi-hazard early warning system. [Number of countries that have [coordinated] multi-hazard monitoring and forecasting system.] [Number / percentage] of people who are covered by [and have access to] multi-hazard early warning system [per 100,000]. [[Percentage / Number] of [local] [and national] governments having preparedness plan (including EWS response and evacuation components) or evacuation plan [tested on regular basis] [and standard operating procedures].] [[Number / percentage] of countries that have [multi-hazard national risk assessment / risk information] with results in an accessible, [understandable and usable] format for stakeholders and people.] [Multi-hazard risk information system capable of providing information in a simple and usable format to common people] [Number of countries with national risk assessment for G5 and mapping reports at national and local level.] [ [Percentage / Number] of local governments that have [multi-hazard risk assessment / risk information], with results in an accessible, [understandable and usable] format for stakeholders and people.] [Percentage of population with understanding of the risk they are exposed to.] [Number of countries with programmes for the disaster risk perception and understanding of the population.] [Number of countries that have national plans with budget and timeline for development of multi-hazard EWS.] [Number of countries that have disaster loss databases publicly accessible.] [Number of countries that have open data policies and mechanisms to make hazard and risk data accessible and available to all users.] [How many countries provide basic weather, environmental and climate services, as defined by the World Meteorological Organization.] [Percentage of people in local communities able to use indigenous knowledge of the risk they are exposed to.] [Percentage of local communities trained in community based multi hazard early warning management system and response.] [Number of programmes to enhance awareness, disaster risk information and risk assessment.]

Methodology

Data

Y

N

Y

N

Y

N

Y

N

Y

N

N

N

G-5 Y

N

N

N

N

N

Target E

N

Targets A-D N

N

N

N

N

N

G-4 G-7 alt.

0

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REFERENCES David Rogers and Vladimir Tsirkunov. 2011. Implementing Hazard Early Warning Systems, GFDRR WCIDS Report 11-03. http://www.preventionweb.net/files/24259_implementingearlywarningsystems1108.pdf United Nations. 2006. Global Survey of Early Warning Systems: Third International Conference on Early Warning (EWC III). 27-29 March 2006, Bonn, Germany. https://www.wmo.int/pages/prog/drr/events/EWSExpertmeeting/Documents/Global_Survey_E WS.pdf United Nations. 2006. Report of the First Experts’ Symposium on Multi-Hazard Early Warning Systems (MHEWS-I) (May 2006) https://www.wmo.int/pages/prog/drr/events/EWSExpertmeeting/Documents/EWSSymposium20 06OutcomeReport.pdf UNISDR (The United Nations Office for Disaster Risk Reduction). 2006. Developing Early Warning Systems, A Checklist: Third International Conference on Early Warning (EWC III). 27-29 March 2006, Bonn, Germany. http://www.unisdr.org/we/inform/publications/608 UNISDR (The United Nations Office for Disaster Risk Reduction). 2009. 2009 UNISDR Terminology on Disaster Risk Reduction. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2009a. GAR 2009: Global assessment report on disaster risk reduction: risk and poverty in a changing climate. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2011. GAR 2011: Global Assessment Report on disaster risk reduction: revealing risk, redefining development. United Nations International Strategy for Disaster Reduction, Geneva UNISDR (The United Nations Office for Disaster Risk Reduction). 2013. GAR 2013: Global Assessment Report on disaster risk reduction: from shared risk to shared value; the business case for disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva. This document can be accessed online in: http://www.preventionweb.net/english/hyogo/gar/ UNISDR (The United Nations Office for Disaster Risk Reduction). 2015. GAR 2015: Global Assessment Report on disaster risk reduction: Making development sustainable: The future of disaster risk management. United Nations International Strategy for Disaster Reduction, Geneva. http://www.preventionweb.net/english/hyogo/gar/ Working Text on Terminology. Based on negotiations during the Second Session of the Openended Intergovernmental Expert Working Group on Terminology and Indicators Relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016.

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Working Text on Indicators. Based on negotiations during the Second Session of the Open-ended Inter-governmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction held in Geneva, Switzerland from 10-11 February 2016. Issued on 3 March 2016. Reissued with factual corrections on 24 March 2016 United Nations Office for Disaster Risk Reduction. Information Note on Comments received on the Working Background Text on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. United Nations Office for Disaster Risk Reduction. Technical Collection of Issue Papers on Indicators for the Seven Global Targets of the Sendai Framework for Disaster Risk Reduction. 23 December 2015. WMO (World Meteorological Organization). 2008. Capacity Assessment of National Meteorological and Hydrological Services in Support of Disaster Risk Reduction: Analysis of the 2006 WMO Disaster Risk Reduction Country-level Survey, World Meteorological Organization, Geneva https://www.wmo.int/pages/prog/drr/natRegCap_en.html WMO 2012: Institutional Partnerships in Multi-Hazard Early Warning Systems: A Compilation of Seven National Good Practices and Guiding Principles http://library.wmo.int/opac/?lvl=notice_display&id=10659#.Vz2kT0ZQjdU WMO - Multi-Hazard Early Warning Systems (MHEWS) https://www.wmo.int/pages/prog/drr/projects/Thematic/MHEWS/MHEWS_en.html

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