Working Paper

November 27, 2023

Adaptation, Loss and Damage: A Global
Climate Impact Fund for Climate JusticeJeffrey D. Sachs1, Isabella Massa2, Simona Marinescu3, Samory Toure4, Leslie
Bermont Díaz5, Guillaume Lafortune6

1

President, UN Sustainable Development Solutions Network, United States
Senior Economist, UN Sustainable Development Solutions Network (SDSN), France
3
Senior Advisor for Small Island Developing States, UNOPS, and former UN Resident Coordinator in Samoa,
Cook Islands, Niue, and Tokelau; United States
4
Program Associate, UN Sustainable Development Solutions Network (SDSN), France
5
Lead Economist GCS Index, UN Sustainable Development Solutions Network (SDSN), France
6
Vice President and Head of the Paris Office at the SDSN and member of the Grenoble Center for Economic
Research (CREG), France
2

1

Abstract
This paper discusses climate justice in the context of increasing climate costs triggered byanthropogenic climate change. It stresses the urgent need of supporting countries mostaffected by climate change, including Small Island Developing States (SIDS), by helping themto cover climate impact costs such as adaptation and loss and damage (L&D). Following on theprevious paper by Sachs et al. (2022), and building on the literature, this paper provides ataxonomy of climate-induced costs to identify their different types, sources and interactions.
The paper also proposes a pilot integrated conceptual and methodological framework toquantify and assess responsibilities across countries for adaptation and L&D costs, using
indicator frameworks and methodologies from the existing attribution and contribution
studies. It then makes an initial attempt to frame a dedicated Global Climate Impact Fund
(GCIF) to share fairly and globally the burden of financing for adaptation and L&D costs fromanthropogenic activities, among responsible countries. We argue that increased funding foradaptation and loss and damages must come hand-in-hand with the development of longterm resilience and sustainable development pathways, including medium term investment
frameworks, in highly vulnerable countries and other countries.

About the SDSN
The UN Sustainable Development Solutions Network (SDSN) mobilizes scientific and technicalexpertise from academia, civil society, and the private sector to support practical problem
solving for sustainable development at local, national, and global scales. The SDSN has been
operating since 2012 under the auspices of the UN Secretary-General. The SDSN is building
national and regional networks of knowledge institutions, solution-focused thematic
networks, and the SDG Academy, an online university for sustainable development.

The results and opinions presented in this paper are those of the authors alone. They do notreflect the views of the SDSN or any organization, agency, or program of the United Nations.

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Acknowledgements
This paper benefited from extensive consultations with practitioners and scientists, conducted
between November 2022 and June 2023. We want to thank all contributors and in particular,David C. Smith (University of the West Indies, Director of the Centre for Environmental
Management, Coordinator of the Institute for Sustainable Development, and Chair of SDSN
Caribbean), Emily Wilkinson (ODI, Senior Research Fellow in the Global Risk and Resilience
Programme and Director of the Resilient and Sustainable Islands Initiative (RESI)), Prof.
Saleemul Huq (Independent University, Bangladesh (IUB) and Director of the International
Centre for Climate Change and Development (ICCCAD))7, Prof. Mizan Khan (Independent
University, Bangladesh (IUB), Deputy Director of the ICCCAD, and Technical Lead of the LDC
Universities Consortium on Climate Change (LUCCC)), and Jane Stanley (Eastern Regional
Organisation of Planning and Human Settlements (EAROPH), member of the Steering
Committee for the Pacific Urban Partnership), for their inputs at various stages. At SDSN weare grateful for research support provided by Cecil Haverkamp, Morvarid Bagherzadeh andSalma Dahir.

7

We hereby want to thank Prof. Saleemul Huq for his lifelong commitment to the issue of climate justice,adaptation and loss and damages.
3

Table of contents
List of Figures and Tables ......................................................................................................... 4
List of abbreviations ................................................................................................................. 5
1.

Background .................................................................................................................... 62.

A taxonomy of climate costs ......................................................................................... 93.
A pilot conceptual and methodological framework to assess adaptation and L&D
costs and countries’ financial responsibility ......................................................................... 12
4.

A Global Climate Impact Fund for adaptation and L&D costs ................................... 20

5.

Conclusion ................................................................................................................... 22

Annexes .................................................................................................................................. 24
Annex 1. Proposed adaptation indicator framework ................................................................ 24
Annex 2. Proposed L&D cost indicator framework .................................................................... 26
Annex 3. Suggestions of potential useful data sources to assess L&D costs .......................... 31
References .............................................................................................................................. 38

List of Figures and Tables
Figure 1. Cumulative production-based CO2 emissions from fossil fuels, by income group .... 7
Figure 2. The three pillars of climate-induced costs ................................................................. 9
Figure 3. From natural and anthropogenic climate change to climate-induced costs ........... 12
Figure 4. Pilot integrated framework to assess climate impact costs and third countries’
contribution to adaptation and L&D costs in affected countries ........................................... 14
Table 1. Adaptation and L&D cost indicator frameworks ....................................................... 17

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List of abbreviations
CO2

Carbon dioxide

COP

Conference of the parties

CVF

Climate Vulnerable Forum

EM-DAT

Emergency Events Database

FAO

Food and Agriculture Organization

GCIF

Global Climate Impact FundGDP

Gross Domestic Product

GFDRR

Global Facility for Disaster Reduction and Recovery

GHG

Greenhouse gases

HIC

High-income countries

IDMC

Internal Displacement Monitoring Centre

IDPs

Internally Displaced People

IMF

International Monetary Fund

IPCC

Intergovernmental Panel on Climate Change

IRDR

Integrated Research on Disaster Risk

L&D

Loss and Damage

LIC

Low-income countries

LMIC

Lower-middle-income countries

MDB

Multilateral Development BankOECD

Organisation for Economic Co-operation and Development

SDSN

Sustainable Development Solutions Network

SIDS

Small Islands Developing States

TC

Transitional Committee

UMIC

Upper-middle-income countries

UN

United Nations

UNDP

United Nations Development Program

UNEP

United Nations Environment Program

UNESCAP

United Nations Economic and Social Commission for Asia and the Pacific

UNFCCC

United Nations Framework Convention on Climate Change

WHO

World Health Organization5

1.

BackgroundThe climate system is currently undergoing changes of an unprecedented scale that will havelong lasting effects on people’s lives and the planet. There is an undeniable rise in thefrequency and magnitude of extreme weather events such as floods, droughts and heatwaves,and a growing number of countries are affected by slow-onset processes, including increased
surface temperatures and rising sea levels (IPCC, 2022; OECD, 2021). These have major human,economic and social consequences in highly vulnerable countries (Massa et al., 2023). In small
island developing states (SIDS), over the last two decades the frequency of natural hazards
has doubled (Akiwumi, 2022), and several small islands have experienced land-loss and coastal
erosion due to increased sea levels (Martyr-Koller et al., 2021). A recent scientific study showsthat global warming may exceed 1.5°C this decade (Hansen et al, 2023). Other scenarios evenconsider that global warming could exceed 2°C and even reach 3°C; even in the most optimistic
scenario, global warming is projected to be in the range of 1.8°C to 2.5°C (UNEP, 2023).
Scientists, with the validation of the Intergovernmental Panel on Climate Change (IPCC), agree
that a large share of the changes in climate is directly due to human activities (IPCC, 2022)8.
Among all human actions affecting the climate, greenhouse gas emissions (GHG) are thelargest source of anthropogenic climate change. GHG emissions are the main cause for theincrease in air temperatures (they are responsible for a rise by 1°C to 2°C), the intensificationof heavy precipitations across the world, the ice loss in the Arctic Sea, and the rise in observed
global sea levels. The scientific community also shows that other human drivers such as
aerosol emissions contributed to cooling temperatures, while the impact of natural drivers(between -0.1°C and 0.1°C) and internal variability (between -0.2°C and 0.2°C) were weaker.
Therefore, the observed warming in global climate is mainly driven by GHG emissions fromhuman activities, which are partly dissimulated by aerosol emissions (IPCC, 2021). GHG
emissions, together with changes in land use, are also recognized as the principal drivers ofclimate-related loss and damage (L&D) (James et al., 2019).
Among GHG emissions, the largest contributor to anthropogenic climate change is theincrease in atmospheric concentrations of carbon dioxide (CO2) (IPCC, 2021). CO2 emissions
are principally resulting from the combustion of fossil fuels (i.e. coal, oil, natural gas) and land
use changes (e.g. deforestation). Since 1850, humans released more than 2,500 Gt of CO2 in
the atmosphere (Evans, 2021). The IPCC (2021) warns that the atmospheric concentrations of
CO2 are continuously increasing, and that in 2019, they reached their highest levels in morethan two million years. While fossil fuel CO2 emissions decreased in 2020 during the first wavesof the COVID-19 pandemic, they have been climbing again, mainly due to a bump in economic
activity in China and India (Friedlingstein et al., 2022).
Since the beginning of the industrial revolution, high-income countries (HICs) generated on
average the largest share of global CO2 emissions, while the contribution to global CO2
8

The causal link between anthropogenic polluting emissions and the probability and intensity of extreme
weather events (e.g. hot extremes) has been proven by many studies (Herring et al., 2018; Tett et al., 2018;
Peterson et al., 2012; van Oldenborgh, 2007).
6

emissions of poorer countries heavily affected by climate change remains marginal (Figure1). In the 19th century, most CO2 emissions were originating from land use changes due to the
expansion of agriculture in HICs such as the United States. Near the end of the 19th century,with the worldwide takeoff of the industrial revolution, emissions from fossil fuels started to
become the main components of global CO2 emissions (Evans, 2021), and European countries
such as the United Kingdom, France and Germany began to be at the top of CO2 emitters. In
the second half of the 20th century, and then during the 21st century, emerging economies
such as China, Brazil and India significantly increased their share in global emissions. Highincome countries are also the countries emitting the largest quantity of CO2 in per capita
terms. Indeed, over the 1850-2020 period, HICs have been responsible for more than 75% of
all CO2 emissions per capita (Sachs et al., 2022).
Figure 1. Cumulative production-based CO2 emissions from fossil fuels, by income group(% of global emissions)
100%

75%

50%

25%

0%
1850

1870

1890

1910

1930

1950

1970

1990

High-income countries

Upper-middle-income countries

Lower-middle-income countries

Low-income countries

2010

Note: Production-based CO2 emissions only.
Sources: Authors’ elaboration based on The Global Carbon Project (2020) and Our World in Data (2021).

However, as of today, the financial burden of responding to climate impacts throughadaptation and other initiatives to address L&D, still almost entirely falls on affected nations
and not on countries that have been most responsible for climate change. In 1992, with thecreation of the UNFCCC, all countries agreed on the principle of “common but differentiatedresponsibilities” and “distributive fairness”. Yet, little has been done on the international
scene. At COP27 in Egypt in 2022, countries called for funding arrangements including theestablishment of a L&D Fund, but the consensus on the proposal for its operationalization isstill fragile (Gabbatiss & Dunne, 2023). The Transitional Committee (TC) recommended in its
co-chair’s proposal voluntary contributions to the L&D Fund and full consistency among
various components of the Financial Mechanism (TC, 2023b). However, up to now very few
7developed countries (e.g. Austria, Belgium, Canada, Denmark, Ireland, New Zealand, andScotland) pledged or allocated funds to cover L&D in countries affected by anthropogenic
climate change. Likewise, funding for adaptation in vulnerable countries remains dramaticallyinsufficient and this will ultimately lead to more loss and damage. Despite the increase of 53%
on average annually between 2017 and 2020, funding dedicated to adaptation programs only
represent 7% of total climate finance (Buchner et al., 2021), which still mainly targets
mitigation and green transitioning actions. Moreover, out of the USD 100 billion annual
funding pledged to developing countries for climate adaptation and mitigation in 2009, only
USD 28.6 billion have been mobilized for developing countries, and SIDS only had access to
USD 1.5 billion (Akiwumi, 2022). Most of the finance went also to mitigation interventions
rather than to adaptation projects. According to OECD preliminary data, it is only in 2022, twoyears after the deadline, that the commitment seems to have been fulfilled (Harvey, 2023).
The under-financing for L&D and adaptation in poor or vulnerable countries often leads themto subscribe to new loans from the International Monetary Fund (IMF) to recover from thedisasters they are inflicted with. Such new loans increase countries’ burden of debt, reduce
their capacity to recover, and have severe long-term consequences on their development andeconomic growth.
Poor and vulnerable countries, which are victims of climate-related disasters for which othernations are responsible, call for climate justice and ask for reparations. The concept ofclimate justice and the search for reparations are intrinsically linked with historicalresponsibility in GHG emissions and climate change. The UN General Assembly adopted theresolution A/RES/76/300 recognizing the right to a clean, healthy, and sustainable
environment as a human right, and therefore indivisible from the right to life, mental health,adequate food, housing, water, and cultural life, among others (UN, 2021). Climate justiceentails ensuring protection of this right for all and that the burden of climate change should
not be born by the least responsible countries who tend to be the most vulnerable ones.
Countries that are historically more responsible for climate change should bear a fair shareof global climate costs. While specific funding flows should be identified to share fairly theburden of the costs induced by climate change, increased global financing for the SDGs at large
may offer great opportunities to strengthen resilience in vulnerable countries by investing
massively in health systems, education, digital infrastructure, clean energy and other key
services and infrastructure. This must of course be accompanied by further efforts in receiving
countries to define long-term resilience and SDG pathways.
The support of the international community is also essential to pay the costs from both
adaptation and L&D imposed by anthropogenic climate change in poor and vulnerable
countries. Foreign public and private resources are more necessary than ever to build climate
resilience in countries such as SIDS, where already small tax bases and domestic resources
have shrunken due to the COVID-19 pandemic (Massa et al., 2023). Despite increasing climate
finance provided by developed countries reaching USD 89.6 billion in 2021, and aside
discussions for the operationalization of the L&D fund, efforts should be multiplied tostrengthen adaptation finance in developing countries notably through scaling up themobilization of private finance (OECD, 2023a; OECD, 2023b). In the effort of enhancing
8financial resources to build climate resilience, it is important to promote coherence andcomplementarity across new financing mechanisms such as the L&D Fund and existing funding
arrangements, including climate finance with a focus on adaptation, disaster risk reduction(DRR), overseas development assistance (ODA), and humanitarian aid (Anisimov, 2023).
This paper stresses the need for climate justice at the international level and proposes anintegrated framework to advance the discussions on how to quantify adaptation and L&D
costs, and build a global fund to finance adaptation and L&D costs attributed to humaninduced climate change. Section 2 provides a taxonomy of climate costs, clarifying theirnature and highlighting key differences across them. Section 3 presents a pilot integrated
conceptual and methodological framework to assess adaptation and L&D costs and individual
countries’ financial responsability. Section 4 makes an initial attempt to frame a new
dedicated Global Climate Impact Fund to share fairly and globally the burden of financing foradaptation and L&D costs from human-induced climate change, among responsible countries.
Section 5 concludes.

2.

A taxonomy of climate costs

There are three types of investments needed for climate safety: mitigation, adaptation andL&D (Figure 2).
Figure 2. The three pillars of climate-induced costs

Source: Authors’ elaboration.

For the purposes of this paper, we define Climate-induced Costs as the sum of Mitigation
Costs and Climate Impact Costs, with the latter category including both the Adaptation andL&D costs. In this typology, we regard reparative (or restitutive) justice as applying to Climate
Impact Costs faced by each country as the result of the global historical greenhouse gases(GHG) emissions. The Mitigation Costs are instead costs borne by countries for cutting
emissions in order to avoid damaging the global climate.
9Mitigation costs are related to “human intervention to reduce the sources or enhance thesinks of greenhouse gases” (IPCC, 2001). They are borne to address the root causes ofanthropogenic climate change. Some examples are the costs for replacing greenhouse gasemitting fossil fuels (e.g. coal, oil, and natural gas) with clean and renewable energies (e.g.
solar, wind, and geothermal), making old buildings more energy-efficient, replacing traditional
internal-combustion vehicles with electric options, decarbonizing food production or forplanting trees and preserving forests to absorb and store more carbon dioxide from theatmosphere.
Adaptation costs are related to the “adjustment in natural or human systems in response toactual or expected climatic stimuli or their effects” (IPCC, 2001). Therefore, they address theimpacts of climate change. Adaptation costs include expenses for redesigning housing,building sea walls, elevating infrastructure, and promoting drought-tolerant crops, among
many others. UNEP (2022) estimates that annual adaptation needs inflation adjusted are in
the range of USD 160-340 billion by 2030 and USD 315-565 billion by 2050.
In addition to their different objectives, mitigation and adaptation costs also differ in termsof spatial and time scale as well as concerned economic sectors (Tol, 2005). On one hand,mitigation costs are borne to respond to an international issue in the long-term, targeting
sectors such as energy, transportation, industry, and waste management. On the other hand,
adaptation costs are costs paid in response to local issues to get benefits in the short/longterm, mainly in the water and health sectors, as well as in coastal and low-lying areas.
Although a common definition of L&D still does not exist, L&D costs can be defined as costs
related to the residual impacts of climate change which are not prevented or avoided by
optimum adaptation and mitigation efforts9. The concept of L&D costs appeared for the firsttime in 1991, but only in 2015 L&D costs were recognized as a separate category frommitigation and adaptation (Sachs et al., 2022). L&D costs are incremental costs incurredbecause of climate-related disasters that can be reduced (but not eliminated) throughadaptation and that persist even after optimal mitigation and adaptation (Shawoo et al., 2021;
Jensen & Jabczyńska, 2022). L&D costs can be the consequence of severe weather events (e.g.
cyclones, hurricanes, tornadoes, droughts, or heatwaves) or slow-onset processes (e.g. sea
level rise, ocean acidification and salinization, land degradation, droughts, desertification, or
glacial retreat).
When discussing L&D, a distinction is made between economic and non-economic costs(Thomas et al., 2018). Economic L&D costs are direct physical costs due to the negative impactsof climate change on resources, goods and services traded in markets. They can be easily
quantified. Examples are costs related to damages to infrastructure, decreases in agriculture
production or in services such as tourism, disruption of economic activities, etc. Non-economic
L&D costs, instead, are indirect costs which are difficult to quantify and monetise, and referto climate-induced impacts such as loss of life, biodiversity, social cohesion, and cultural
9

Throughout the paper, this definition of L&D is preferred over the broader one stating that L&D refer to all
negative consequences of climate change.
10identity, or displacement and migration of communities10. Beside their direct and permanentconsequences on people’s lives, well-being, and health, non-economic L&D also havesubstantial impacts on economic growth and development.
Adaptation and L&D are costs induced by both natural variability and anthropogenic
activities. However, only the financing of human-induced adaptation and L&D costs should
be shared among polluting countries according to their historical responsibility in climatechange (Figure 3). Adapting to climate change helps reduce the amount of damages inflicted
but represents substantial investment costs that add to the climate impact costs. As explained
further in the paper, in addition to what is stated in the Paris Agreement (i.e. developed
countries should provide financial, technical, and capacity building support to less developed
and vulnerable countries), a climate justice regime would share the costs of incremental
adaptation (i.e. additional adaptation spending due to anthropogenic activities despite an
optimal level of adaptation to the share of climate events caused by natural variability) andL&D, but would require that each country is responsible for cleaning its energy system byfunding its own mitigation measures. Each country should pay restorative justice funds inproportion to the share of its historical GHG emissions, relative to their population size andconsidering the effect of past emissions on current GHG concentrations based on climate
modeling.

10

Out of the total 59.1 million internally displaced people (IDPs) accounted at the end of 2021, around 38 million
were displaced in the sole year 2021, and 23.7 million of them were displaced because of natural hazards (IDMC,
2022). More than half of the internally displaced people by disasters in 2021 were in the East Asia and the Pacificregion (Ibid). At an estimated average cost of USD 390 per displaced individual annually, the total cost of theclimate IDPs amounts to close to USD 10 billion per year. This estimated cost mainly refers to housing andschooling expenses and does not include the economic impact of displacement on host communities or IDPs in
the process of returning, nor does it account for investments made by governments or development stakeholders
to address the longer-term economic consequences of displacement such as unemployment and mental healthissues.
11

Figure 3. From natural and anthropogenic climate change to climate-induced costs

Source: Authors’ elaboration.

3.

A pilot conceptual and methodological framework to assess
adaptation and L&D costs and countries’ financial
responsibility

Determining who should pay for human-induced climate change adaptation and L&D is a
key but complex exercise since several methodological issues arise. For example, should westart counting responsibility from the beginning of the industrialization period, or from thecreation of the UNFCCC that marks the moment when countries and stakeholders started tobe conscious about the consequences of their pollution patterns? While polluting countries
should pay for both adaptation and L&D costs originating from anthropogenic climate change,
should natural-induced costs be shared equally among all countries – including the lesspolluting ones – to ensure enough incentive to plan for greater resilience to climate change inaffected regions or countries?
Moreover, the attribution of climate events to human-induced climate change requires firstto correctly measure or predict adaptation and L&D costs and then to understand whichshare of climate impact costs is due to anthropogenic activities rather than to natural
weather variability. Quantifying both adaptation and L&D costs is difficult due to the12multidimensional nature of these costs – economic and non-economic, short- and long-term,
historic and prospective – that challenges both the collection and the analysis of data.
Moreover, nothwistanding the progress in the attribution literature, when a natural weatherrelated disaster hits, it is still difficult to assert that the given shock was due to long-term
human-induced climate change rather than to natural weather variability. This challenge iseven bigger for slow-onset processes that have more dispersed impacts.
Up to now, these challenges have been investigated by different streams of the literature.
Existing studies can be categorized into three different group: (i) quantification studies trying
to correctly quantify the total amount of adaptation and L&D costs that need to be covered
for allowing countries and communities to recover from climate-related disasters; (ii)attribution studies identifying the respective role of human actions and natural variability inclimate change, in the probability of climate disasters, and in the resulting adaptation and L&D
costs; (iii) contribution studies identifying who (e.g. countries and/or economic sectors)
contributed to human-induced climate change and resulting costs. Nevertheless, all thesestudies present numerous limitations due, for example, to the scarce availability of granularand timely data, and to the challenge of integrating non-economic variables into the usedmodels (Sachs et al, 2022).
Although the existing literature provides useful guidelines, a harmonized and commonly
accepted methodology to first quantify adaptation and L&D costs (including non-economic
costs), and then to fairly split the burden of the costs among countries does not exist yet. As
a consequence, estimates of climate impact costs provided by different entities are very
diverse, although projected numbers are all considerable. For example, Baarsch et al. (2015)
estimate economic L&D costs to be USD 400 billion in 2030 and USD 1–1.8 trillion by 2050,
while Markandya and González-Eguino (2018) project L&D costs of at least USD 290-580
billion by 2030 for developing countries alone. More recent estimates obtained using data
provided by the Climate Watch data platform of the World Resources Institute are likewise
substantial, with identified total economic costs for current and future L&D of about USD 810
billion. The lack of precise estimates of climate impact costs and of a widely recognized criteria
on how the burden of adaptation and L&D costs should be shared across countries leads toinsufficient financial support for climate impact costs, especially in vulnerable countries such
as SIDS (Massa et al., 2023), which risk to disappear under water if adequate resources are
not allocated to support adaptation programs and cover L&D costs. Schäfer et al. (2021)
highlight that existing financial support to address L&D is insufficient, with most climate
finance addressing mitigation costs rather than adaptation and L&D costs (Buchner et al.,2021). Across the different types of L&D costs, financial support is particularly scarce for L&D
costs caused by slow-onset processes as well as non-economic L&D costs11.
This section proposes a pilot integrated framework that could be used by the international
community to assess climate impact costs and allocate responsibilities for those costs
11

See Künzel and Schäfer (2021) for evidence on Climate Vulnerable Forum (CVF) countries. CVF countries consistof 48 countries (including 17 SIDS) from the African, Asian-Pacific and Latin American and Caribbean region facing
severe threats due to climate impacts.
13

(Figure 4). The framework also integrates the topic of adequate financing mechanisms andsources to deal with climate impact costs, that will be further discussed in Section 4. Partly
building on OECD (2021), the pilot framework suggests that five steps are needed for assessing
adaptation and L&D costs, and identifying specific countries’ contributions to climate change
and related climate impact costs. These steps are summarized by Figure 4 and describedfurther below.
Figure 4. Pilot integrated framework to assess climate impact costs and third countries’
contribution to adaptation and L&D costs in affected countries

Source: Authors’ elaboration.

14

The first step of the framework is to assess adaptation and L&D costs. Indicator frameworks
are useful tools readily applicable by policymakers to assess them. Although several
institutions have been proposing series of indicators to measure L&D and adaptation, there iscurrently no harmonized and comprehensive indicator framework to support adaptation andL&D costing at the national level. Our proposed adaptation and L&D cost indicator frameworks
aim to fill this gap. Although different in nature, the indicators selected in the frameworks forcosting adaptation and L&D were identified according to the following criteria: indicators
should be policy-relevant, measurable, globally relevant, no time or event-specific, andmutually exclusive.
The adaptation framework suggests a list of adaptation measures to be costed, paired withindicators useful for the costs assessment (Table 1 and Annex 1). Adaptation measures andindicators are classified around eight main sectors, including institutions and innovation, andcover both hard (e.g. infrastructure) and soft (e.g. laws and regulation) adaptation measures.
Although not exhaustive, this list is comprehensive and useful to start an adaptation costassessment. While some countries may need to consider all adaptation measures, others may
prefer to focus on some of them, depending on their specific needs and available resources toadapt to climate change. Adaptation costing is usually ex-ante, and requires context-specificanalysis and methods. Studies assessing adaptation costs are typically represented by partial
equilibrium analyses based on sector-specific indicators (Soós et al., 2022; Parry et al., 2009;
Bosch & Pásztor, 2012), or integrated assessments models (de Lucena et al., 2010). In bothcases, some preliminary steps are essential to follow. This is why we complement theadaptation framework with a checklist which may provide guidelines to policymakers forcosting their adaptation needs (Box 1).
The L&D framework reports impact and cost indicators, which are usually measured ex-post,
using historical observable data, and not specific to a particular context (Table 1 and Annex2). The framework accounts for L&D costs caused by either extreme weather events (e.g.
floods) or slow-onset processes (e.g. sea level rise). Although a large share of losses anddamages experienced in developing and vulnerable countries is caused by slow-onset climatechange events, up to now most of the L&D costing methods that exist in the literature havefocused on L&D from extreme weather events. In our framework, L&D that are typicallyoccurring after a sudden shock and are easily measurable between two points close in time(e.g. loss in crops after a flood) are associated with “Extreme weather event”, while L&D
occurring on a more long-term basis (e.g. coral bleaching) are associated with “Slow-onset
event”. In some cases, both types of climate change events are associated with one impact or
cost indicator (e.g. rise in food insecurity). The proposed L&D cost indicator framework relates
to most of the Monitoring Sendai Framework targets and indicators, with the added value of
integrating indicators capturing L&D due to slow-onset processes. Suggestions of usefulpotential data sources (global sources with open or restricted access, as well as local
initiatives) to assess L&D costs at the country level are presented in Annex 3.
Both economic and non-economic L&D costs are considered in our framework. Taking noneconomic costs into account is essential, especially in the context of climate justice andreparations. Non-economic costs are often neglected since they are more difficult to quantify
15

as they refer to intangible climate-induced impacts on individuals, society and theenvironment (e.g. loss of life, loss of cultural identity), but they represent a significant andimportant part of L&D total costs. Economic costs are, on the other hand, easier to estimate,and can be decomposed between direct costs (i.e. damage to physical infrastructure or assets)
and indirect costs (i.e. related losses in production).
A number of databases may enable L&D costs assessment at the global level. While these
databases are useful to estimate global L&D costs, data availability and coverage at the local
level remain limited and increased gathering of hazard and impact data is necessary. Some
databases are managed by international organizations and their data are freely available (e.g.
EM-DAT, DesInventar), while others are managed by private institutions such as insurancecompanies and their access is restricted (e.g. Sigma, NetCatService). Some initiatives such as
the UNDP Bangkok Regional Hub, an effort to develop a taxonomy of L&D in 19 countries in
the Asia Pacific Region, 9 countries in Africa, 3 Arab States and 4 countries in Europe and acrossthe Commonwealth of Independent States, could lead to the creation of important data
sources on L&D. Other examples of local L&D data collection efforts are cited in Bhuiyan et al.
(2022). Digital technology could also be exploited more to gather data on L&D. In theassessment of adaptation costs, local data next to data already provided by various
international organizations, such as FAO, WHO, the World Bank, and IMF, among others, areneeded.
Following the costs assessments, a second step consists in estimating to what extent theanthropogenic drivers are responsible for climate change intensification. Climate events(slow-onset processes or extreme weather events) are due to both natural variability andhuman-induced drivers. Using the methodologies of attribution studies, the goal at this stageof the framework is to disentengle the influence of humans’ activities on climate change fromthat of natural drivers. To do this, attribution studies usually use a combination of climate
models comparing observed trends in specific climate variables (e.g. temperatures) withsimulated trends of the same variables in alternative scenarios where anthropogenic drivers(e.g. GHG or aerosol emissions) are excluded. The next steps of the framework focus on thepart of climate events due to anthropogenic drivers.
Measuring countries’ individual contributions to climate events suffered by other countries
is the third step of the framework. As CO2 emissions have been identified as the largest
source of anthropogenic climate change and are the best tracked over time, they can beused as a proxy to measure countries’ individual contributions to human induced climatechange. The aim is to estimate countries’ historical responsibility in human-induced climatechange. Relying on climate models and statistical methods, contribution methodologies
enable to quantify the share of each countries in global GHG and aerosols emissions, andestimate their respective consequences on climate variables (e.g. global temperatures) andthe probability of climate events.
After assessing the total adaptation and L&D costs, and estimating the share of climate change
due to anthropogenic drivers as well as countries’ individual responsibility in CO2 emissions,as a fourth step, the share of total adaptation and L&D costs that each country should bear16

as reparations to affected countries based on its contributions to human-induced climatechange is identified.
Table 1. Adaptation and L&D cost indicator frameworks
Selected examples of adaptation indicators useful to cost
adaptation*

L&D Indicators to be costed

Agriculture, fisheries, and forestry
Share of agricultural production issued from climate-resilient
Volume of crops, livestock and timber lost after natural
seeds (%)
hazards
Degree of crop and livestock diversification

Area of productive land and forest damaged by natural
hazards or affected by salinization

Fishes caught that are then discarded (%)
Number and levels of fishing quotas

Number of fish farms lost after natural hazards

Area affected by salinization (% arable land)
Irrigated area (% arable land)

Agriculture, aquaculture and forestry machinery, equipments
and systems destroyed or damaged by natural hazards

Average rainwater harvesting tanks capacity (liters)
Freshwater withdrawal (% of available freshwater resources)

Number of jobs in the agriculture, fisheries and forestry
sectors lost after natural hazards

Environment
Share of coastal area at risk of flooding protected with sea
Change in tree cover, species abundance, coral bleachingwalls or advanced shoreline (%)
Forest area treated with prescribed fire (%)

Area affected by desertification

Human and cultural
Share of the population living in climate-related risk areas that Number of people who died, went missing or were injured or
has been relocated (%)
affected by natural hazards
Household cash transfers (% GDP)
Share of food surpluses that is stored (%)

Number of people suffering from mental health issues after
natural hazards (per 100 000 population)

Surviving infants who received 2 WHO-recommended
vaccines (%)

Number of persons whose house was destroyed or severely
damaged by natural hazards

Share of patients admitted in trauma services who died within Number of people who fell below the national poverty line or
24 hours after hospitalization (%)
into food insecurity
Number of adaptation initiatives sourced from indigenous
knowledge

Loss of tangible and intangible cultural heritage (including
historical assets and cultural identity)

Climate change is included in primary and secondary
education national curricula (Yes: 1; No: 0)
Industry
Share of private companies issuing sustainability reports (%)
Number of raw materials, machinery and equipment
destroyed or damaged by natural hazards
Number of jobs lost after natural hazards
Loss of production after natural hazards
Infrastructure
Number or length of houses, ports, airports, schools,
healthcare facilities, government buildings, industrial facilities,
and electrical power plants destroyed or damaged by natural
hazards
Share of municipal waste collected and safely treated (%)
Number of schools, medical and industrial material andExpenditure on road maintenance (% GDP)equipment destroyed or damaged by natural hazards
Share of buildings resilient to climate hazards (%) (e.g. seismic
risk, floating housing, etc.)

Length of roads, railways, electrical distribution lines, WASH
grids and telecommunications grids destroyed or damaged bynatural hazards

Source: Authors’ elaboration. Notes: * Additional indicators useful to cost adaptation (including those in the Financing, Innovation andInstitutions categories) are reported in Annex 1.

17

Box 1. A checklist for adaptation costs assessments
1- Identify the event for which adaptation is needed.
It is essential to be precise in the description of the climate change event to which we want to adapt.
For instance, instead of referring to “sea level rise”, it is better to specify “flood caused by a surge fromthe sea”, “erosion of beaches and cliffs” or “saltwater intrusion”, since the adaptation tools to addressthese effects are different. While hard adaptation is required for inundations, sediment or beachnourishment, soft adaptation is more adapted in case of erosion (Parry et al., 2009).
2- Assess the probability of occurrence of the event and quantify its current scope.
Determine the frequency and probability of occurrence of the event (e.g. floods are estimated tohappen X times during the following N years). If the event is directly measurable, quantify its effects(e.g. measuring the volume of plastic waste entering in the ocean per year) and differentiate them bysource (e.g. plastic generated inland, plastic generated on coastal areas) (see examples in Soós et al.,2022).
3- Identify the relevant and specific adaptation measure, especially distinguishing between
hard and soft measures.
Describe the different elements of the measure, its performance characteristics, its expected
implementation time horizon, and potential side effects. At this stage, several adaptation options canbe listed. Costs of each option must be calculated separately and compared afterwards.
4- Identify the status of the current infrastructure, technological, and political environment.
This step is essential to understand context-specific variables (e.g. countries with high and low existing
infrastructure do not face the same adaptation needs nor have the same adaptative capacity).
5- Identify the exhaustive list of costs’ components of the selected measure and compute thetotal cost to implement the measure.
The adaptation indicators of our proposed framework can guide the quantification of the different
costs’ components (e.g. measuring the share of coastal area needing flood protection is necessary toestimate the total costs of flood protection measures).
Decompose the adaptation measure costs between: capital costs (i.e. initial investment and capital
recovery), and operating and maintenance costs (i.e. costs of labor, energy, material, services).
Depending on the data required, global databases can be used, or local data collection can be required.
The following formula approximates the calculation of adaptation investment costs:!

𝑎𝑑𝑎𝑝𝑡 𝑐𝑜𝑠𝑡𝑠 =* (𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 + 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 & 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡𝑠) ∗ 𝑝𝑟𝑜𝑏𝑎"#$%&'

where the aggregated investment costs (𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 and 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔&𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡𝑠),
adjusted for inflation, are multiplied by the probability of the event. The total is then summed over thenumber of years by the time horizon of the adaptation measure.

18

This is the starting point to design a new dedicated Global Climate Impact Fund (GCIF) to
share fairly on the global scale the financing of adaptation and L&D costs generated byhuman-induced climate change. Up to now, traditional and innovative financing mechanisms
as well as a number of national and international financing sources have been identified toaddress L&D costs, but they are still insufficient and in some cases difficult to apply (Sachs etal, 2022). For example, risk retention financing instruments, which refer to contingency
finance such as disaster relief funds, have the advantage to be rapidly disbursed. However,they can also divert funds from other key spending needs, can not be used for slow-onset
processes and may even worsen a country’s fiscal burden. Other innovative financing
instruments, such as debt for loss and damage swaps, can provide debt relief following a
climate disaster, but they can also disincentivize to reduce risks and can not provide additional
resources to respond to immediate needs stemming from the impacts of natural hazards(Sachs et al, 2022). To align with the concept of climate justice, we propose to complement
the existing financing mechanisms and sources with a dedicated GCIF. The GCIF would be atargeted instrument to finance incremental adaptation and L&D costs caused by humaninduced climate change, and would prevent countries from contracting new loans (e.g. with
the IMF) that increase their burden of debt and jeopardize their capacity to recover and build
resilience to climate change (Walsh & Ormond-Skeaping, 2022).
Nevertheless, there are multiple methodological obstacles to achieving each step of the
proposed pilot integrated framework. Starting with cost assessments (step 1), the main
limitations are: the lack of robust, granular, and timely data on both current and historicclimatic variables; the imperfect collection of data on economic impacts; and the lack ofmeasurements of non-economic impacts. Weak data governance and underdeveloped digitaltechnology are also key obstacles for data gathering and sharing at the national, regional, andglobal level. Data is difficult and costly to collect, and when it is collected it is often either on
paper (not digitalized) and therefore difficult to share, or it is not freely and openly available.
Initiatives such as PARIS21’s Climate Change Data Ecosystem (CCDE), SDSN’s Thematic
Research Network on Data and Statistics (TReNDS) and the Global Partnership for Sustainable
Development Data, are key examples of efforts that need to be multiplied and scaled-up to
close the existing data gaps. The attribution of climate events to natural variability andanthropogenic activities (step 2) also suffers from similar limitations, notably in developing
and vulnerable countries, where there are limited availability of long-term and disaggregateddata. In addition to data related obstacles, the assessment of the contribution of each country
to GHG emissions as well as the identification of the proportion of adaptation and L&D costs
caused by specific countries (steps 3 and 4) also suffer from sensitivity to emissions data andmethodological choices (Sachs et al, 2022). For example, there is no consensus on the starting
date that should be used to measure countries’ contribution to anthropogenic climate change,
notably through CO2 emissions. Therefore, it can be challenging to identify countries’
individual contribution to human-induced climate change and subsequently assess theirfinancial responsibility in other countries’ adaptation and L&D costs.

19

4.

A Global Climate Impact Fund for adaptation and L&D costs

COP27 marked a turning point in climate justice, as countries agreed on the creation of a
specific Loss and Damage Fund. The Fund aims at providing financial resources to nations thatare affected by the consequences of climate change, based on the principle of cooperationand facilitation, and not of liability or compensation (TC, 2023b). While the adoption of the
Loss and Damage Fund represents a historic decision in favor of climate justice, countries mustensure that its implementation is fair and rapidly effective. The Loss and Damage Fund can bea pivotal innovative mechanism to increase global liquidity and leverage resources forsustainable development in the context of the call for a global SDG Stimulus (United Nations,2023). In 2023, the Transitional Committee (TC)12 responsible for the design andoperationalization of the Fund, held multiple meetings to define its nature, institutionalarrangements, as well as the rules defining countries’ contribution and resources allocation13.
The TC co-chair’s proposal to COP28 suggested the Fund would be managed by a Board (withlegal personality and capacity) and hosted for an interim period of four years by the WorldBank as a Financial Intermediary Fund (FIF) (TC, 2023b). However, despite this agreed
proposal, the consensus seems to be still fragile (Gabbatiss & Dunne, 2023). Besides, although
L&D costs are incremental costs whose magnitude also depends on adaptation measures(Shawoo et al., 2021; Jensen & Jabczyńska, 2022), the current Fund model does not integrate
adaptation costs.
The framework presented in this paper provides additional perspectives on how the Fundshould operate.
To align with the concept of reparative justice, the Global Climate Impact Fund (GCIF) should
cover both incremental adaptation and L&D costs that are caused by human-induced
climate change. Costs that emerge both after sudden extreme weather events and slow-onset
processes should be considered. While total climate-related costs linked to anthropogenic
activities (i.e. incremental adaptation and human-induced L&D) should be shared among
polluting countries only, proportionally to their contribution to GHG emissions and theconsequent change in climate, the remaining costs caused by natural variability andinadequate adaptation should be covered individually by all countries, including those non
responsible but affected by climate change. This could provide the right incentive to affected
countries to continue investing in their efforts to adapt to climate change. In other words,
adaptation expenditure necessary to cover for climate change caused by natural variability

12

The Transitional Committee is composed of 14 members from developing countries and 10 representatives
from developed countries. The geographical representation among developing countries is: 3 countries fromAfrica, 3 countries from Asia and the Pacific, 3 countries from Latin America and the Caribbean, 2 SIDS, 2 countries
from the Least Developed Countries group, 1 developing country not belonging to any of the previous group(UNFCCC, 2022).
13
In its third meeting, the TC identified different sources of financing for the Loss and Damage Fund including
contributions from multiple stakeholders and innovative sources such as the voluntary carbon market orinternational pricing mechanisms (TC, 2023a).
20

should not be covered by the GCIF. The Fund should not finance L&D that are caused bynatural variability in climate and inadequate adaptation measures in affected countries.
The TC's final proposal ahead of COP28 “urges developed country Parties to continue toprovide support and encourage other Parties to provide, or continue to provide support, on
a voluntary basis, for activities to address loss and damage” (TC, 2023b).
Our proposal is that contributions to the GCIF could be based on the principle of historicalresponsibility in climate change and could cover all types of climate impact costs (adaptationand L&D). Half of the amount covered by polluting countries should be based on their current
GHG emissions and the other half should be based on their cumulative historical GHG
emissions. A price could be assigned to each ton of CO2 emitted based on total outlays needed
and the volume of emissions. On the long-run, other financial inputs from private sector andpotential innovative instruments could be considered to diversify the sources of funding.
Nevertheless, rather than being a post-disaster recovery scheme based on a disaster-bydisaster compensation approach, the GCIF should work as a global insurance mechanism,
where the premia would be paid according to polluting countries’ historical and current
responsibilities in CO2 emissions. A post disaster fund would leave aside most L&D costs
inflicted by slow-onset processes, to which countries such as SIDS are disproportionately
vulnerable (Massa et al., 2023), and would introduce a moral hazard issue as it reduces the
incentive for adaptation measures that could reduce loss and damages. Countries would
indeed engage very low preventive measures and expect a post-disaster fund to cover all theircosts in the wake of climate disasters. In the case of the proposed global insurance model, inorder to reduce the moral hazard issues typically associated with insurance schemes, the GCIF
should ensure that beneficiary countries do take adaptation and prevention measures, forexample by including a precondition to enter the insurance scheme based on countries’ efforts
to prevent damages (e.g. to prohibit new constructions in floodplains). The fact that the shareof L&D caused by natural processes is planned to be shared among all countries (not only
polluting countries) is an additional guarantee to reduce the moral hazard issue.
Allocations from the GCIF should be in the form of restorative payments equal to the costsof human-induced adaptation and L&D a country is bearing, net of the costs for which they
are responsible towards other countries. Every country is considered as a contributor and arecipient of the Fund. If a country faces as many costs as it is responsible for vis-à-vis the restof the world, then it should receive no net payments from the GCIF. If its responsibility inclimate adaptation and L&D costs is greater than the costs it faces (as will be true for mostHICs), then the country will pay more to the GCIF than it will receive from the GCIF. If its shareof priced historical emissions is lower than its adaptation and L&D costs (as most of the SIDS,
Low-Income Countries (LICs) and Lower-Middle-Income Countries (LMICs)), then it will receive
more from the GCIF than it will pay to the GCIF. A double track could be envisioned to apply
for funding from the GCIF. On one hand, countries could apply for funding their incremental
adaptation costs, based on their long-term resilience plans and SDG pathways. The GCIF would
be therefore more than an insurance scheme, incentivizing planning and ensuring quality

21

spending. On the other hand, countries affected by human-induced L&D from both extreme
weather events and slow-onset processes could receive funding to compensate for the costs.
The GCIF should operate as a new, independent, stand-alone institution, as stressed in the
TC’s final institutional arrangement proposal (TC, 2023b), to avoid tipping the balance ofpower toward developed countries parties. Hence, the GCIF will decide on its own forcontribution, eligibility, and allocation criteria, among others. Access to the funds should be
simplified and countries should be able to use their existing reporting and accounting systems
to prevent delays and additional costs to adapt to new settings. Major multilateraldevelopment banks (MDBs) (e.g. the World Bank, the IMF, or regional MDBs) would be incharge of the disbursements of funds allocated by the GCIF to affected countries, without
adding any form of conditionality. The MDBs could also oversee the spending of the funds andmake sure beneficiary countries comply with the requirements on adaptation spending to
avoid moral hazard issues associated with insurance payments. The Global Fund to fight
HIV/AIDS, tuberculosis and malaria, and its eight design principles14, stand as a model ofinspiration for designing the GCIF. The Global Fund has indeed proved that pooledinternational financing could accelerate progress in addressing global challenges (Sachs &Schmidt-Traub, 2017).

5.

Conclusion

Anthropogenic activities have been the main driver for climate change since the industrial
revolution. GHG emissions, notably carbon dioxide (CO2), are the largest contributors tohuman-induced climate change. The consequences of climate change are extreme weather
events and slow-onset processes threatening the universal human right to a clean, healthy,
and sustainable environment. While high-income countries historically produce the mostsignificant proportion of GHG emissions, climate change is disproportionally impacting
vulnerable countries whose contribution to anthropogenic climate change has been marginal.
The financial burden of climate responses through adaptation and initiatives addressing L&D
largely falls on affected nations, raising calls for reparations and climate justice.
Measuring costs related to adaptation and L&D, and determining who should pay for their
human-induced share, is a key but complex exercise. Currently, there is no universally
accepted framework to quantify these costs and determine fair sharing mechanisms among
nations. As a starting point, building on the literature, this paper provides a taxonomy defining
climate-induced costs as the sum of mitigation costs and climate impact costs, with the latter
category including adaptation and L&D costs. While mitigation costs are defined as costs for

14

The Global Fund has eight design principles: (1) country-led, (2) multistakeholder, (3) independent,
transparent, technical review and evaluation; (4) political independence; (5) needs-based pooled financing; (6)funding for programs integrated in broader health systems; (7) performance-based funding; (8) financing only
(Sachs & Schmidt-Traub, 2017).
22

cutting emissions to diminish the anthropogenic pressures over climate change, the taxonomy
highlights that the concept of reparative justice should apply to adaptation and L&D costs.
Using useful guidelines from the existing literature, the paper presents then a pilot integrated
methodological and conceptual framework to quantify and fairly split the burden of climate
impact costs from anthropogenic climate change, among responsible countries. As a first step,
the framework proposes to assess total adaptation and L&D costs using harmonized indicator
frameworks that could be readily used by policymakers. The second step is to identify theshare of climate impact costs due to anthropogenic activities versus natural weather
variability. The third step consists of measuring countries’ individual contribution to global
GHG emissions. The fourth and final step builds on previous steps, and estimates the share ofadaptation and L&D costs for which countries are individually responsible toward affected
countries, given their contribution to global anthropogenic climate change.
The framework leads to the proposal of a Global Climate Impact Fund (GCIF) seeking to restore
climate justice by relying on countries’ individual contributions linked to their historical andcurrent responsibility in GHG emissions. The Fund should be established as an independent
institution aiming to cover adaptation and L&D costs derived from human-induced climatechange. It should also offer access to countries on the basis of standardized criteria, and bysimplifying processes and averting additional costs. The GCIF should work in the form of a
global insurance mechanism where countries should receive restorative payments equivalent
to the costs of human-induced adaptation and L&D costs net of the share of global costs they
are responsible toward the rest of the world. Beneficiaries would in turn be responsible forpreparing science-based and credible long-term resilience and SDG pathways as well as
detailed project proposals to show precisely how the additional funds will be invested for longterm adaptation.
Areas for future research and policy action include applying the proposed cost indicator
frameworks in country-case studies with the aim of further selecting, prioritizing, and refining
the indicators presented in the paper. In this regard, the issue of data availability is ofparamount importance. There is an urgent need for robust, granular, and timely data to obtainreliable estimates of adaptation and L&D costs. Efforts are needed to measure non-economic
costs as well as L&D costs due to slow-onset processes that tend to be underestimated.
Moreover, as we propose to build the Fund on the principle of historical and current
responsibility in global GHG emissions, further research should lead to a commonly agreed
starting date to account for countries’ emissions. The price per ton that would be defined isanother area for future research, as one could argue that the price per ton of CO2 emissions
could be higher for richer countries. Finally, the current mechanisms of the Loss and Damage
Fund proposed by the Transitional Committee, or the GCIF proposed in this paper, may not be
sufficient to cover for the financing gaps in addressing adaptation and L&D costs. Therefore,
future research and policy action should look at the role of private sector and innovative
financing in the overall financing scheme for climate impact costs.

23

Annexes
Annex 1. Proposed adaptation indicator framework
Sector

Adaptation measures to be costed

Examples of adaptation indicators useful to cost adaptation

Adopt climate-smart agriculture
techniques

Share of agricultural production issued from climate-resilient seeds(%) (e.g. salt-tolerant seeds)

Increase crop and livestock
diversification

Degree of crop and livestock diversification

Reduce overfishing
Agriculture,
Fisheries and Improve soil management (e.g.
Forestry
desalinization methods, efficientirrigation)
Improve water management (e.g.
rainwater harvesting)Fishes caught that are then discarded (%)
Number and levels of fishing quotas
Area affected by salinization (% arable land)
Irrigated area (% arable land)
Average rainwater harvesting tanks capacity (liters)
Freshwater withdrawal (% of available freshwater resources)

Improved storage and processing to
reduce post-harvest losses

Penetration rate of metal silos technology among small farmers(%)

Improve flood protection

Share of coastal area at risk of flooding protected with sea walls or
advanced shoreline (%)

Improve wildfire protection

Forest area treated with prescribed fire (%)

Environment Reforestation

Tree density
Change in tree cover (%)

Invest in protected areas

Mean area that is protected in terrestrial and freshwater sites
important to biodiversity (%)

Promote green areas in cities

Share of the urban population with access to green area within 15
minutes’ walking distance (%)

Enhance international financing foradaptation

Amount of ODA and private financing received and allocated toclimate change adaptation (USD per capita)
Amount of financing received from catastrophe-bonds anddisaster-contingency funds (USD per capita)
Number of financial incentives for climate adaptation including
taxes and subsidies

Financing

Public expenditure allocated to climate change adaptation (% GDP)
Enhance internal financing foradaptation

Public expenditure allocated to resilient infrastructure (% GDP)
Share of credit to households using micro-finance schemes (e.g. tosupport resilient housing) (%)
Share of key assets protected by insurance in the energy, WASH,
and agriculture sectors (%)

Human andculturalRelocate people living in climaterelated risk areas (e.g. low-lying
areas)
Enhance social safety nets and social
protection (e.g. food banks)
Invest in public health services (e.g.
vaccination programs, emergency
services)
Promote sharing of local andtraditional knowledge
Improve education on adaptationand climate change (e.g. school
programs, media communications)Share of the population living in climate-related risk areas that has
been relocated (%)
Household cash transfers (% GDP)
Share of food surpluses that is stored (%)
Surviving infants who received 2 WHO-recommended vaccines (%)
Share of patients admitted in trauma services who died within 24
hours after hospitalization (%)
Number of adaptation initiatives sourced from indigenous
knowledge
Climate change is included in primary and secondary education
national curricula (Yes: 1; No: 0)24Industry

Increase due diligence

Share of private companies issuing sustainability reports (%)

Transport and road infrastructure
Expenditure on road maintenance (% GDP)
adaptation
Adjusting power plants
infrastructure and electricity grids to Share of underground electricity cabling (%)
climate events
Infrastructure Enhance waste management
Ensure climate-resistant housing
infrastructure

Innovation

Contribute to the diffusion ofclimate change adaptation
technologies
Invest in the development of early
warning systems (e.g. hazard
mapping and monitoring technology,
remote sensing)
Improve hazard data collection (e.g.
integrate indigenous climate
observations)
Development and adoption of easyto-use risk assessment tools
Creation and implementation ofnational, regional, subnational andlocal adaptation plans

Institutions

Creation and implementation of citylevel, district-level and sectoral
adaptation plans
Include indigenous and traditional
knowledge in the design ofadaptation plans at all levels
Expand laws and regulations
promoting adaptation (e.g. building
standards, protected areas, land
zoning)Share of municipal waste collected and safely treated (%)
Share of buildings resilient to climate hazards (%) (e.g. sismic risk,
floating housing, etc.)
Share of buildings with improved insulation (%)
Triadic patent families filed in the field of climate protection (permillion population)
Use of early warning systems for short-, medium- and long-term
decisions in the agricultural sector
Statistical Performance Index (worst 0-100 best)
Risk assessment processes are built on a multi-governanceapproach (Yes: 1; No: 0)
The national government has issued an adaptation plan (Yes: 1;
No: 0)
Share of regional and subnational authorities having issued local
adaptation plans (%)
Number of local authorities having issued sectoral adaptationplans (%)
Share of indigenous people members of subnational public
administrations (%)
Share of new laws and regulations that were adopted during theyear in relation to adaptation to climate change (%)

Sources: Authors’ elaboration based on Noble et al. (2014); OECD (2022); Mycoo et al. (2022); Government of Fiji (2017);
Goonesekera and Olazabal (2022).

25

Annex 2. Proposed L&D cost indicator framework
SectorLoss & Damage Indicator
Volume of crops already produced and expected to be
sold that were destroyed by natural hazards, by type ofcrop (tons)

Area of productive land damaged by natural hazards(hectares)

Land surface affected by soil salinisation (hectares)

Number of irrigation or other drainage system items
destoyed by natural hazards
Agriculture, fisheries
and forestry
Number of livestock lost after natural hazards, by type of
livestock

Number of fish farms lost after natural hazards

Volume of timber loss (tons)

Area of productive forest damaged by natural hazards(hectares)
Number of agriculture, aquaculture and forestry
machinery and equipments destroyed or damaged bynatural hazards

Cost indicator
Value of crops destroyed (USD per capita): multiply the volume of each crop c by its
market value and aggregate the total value; divide by the population size
loss_crop = (SUM(vol_c * price_c)) / pop
Loss of production (USD per capita): for each type of crop, multiply the area of land
destroyed by the crop yield per hectare and the crop price ; aggregate the total value ;divide by the population size

Type of cost

Climate event

Economic
DirectExtremeweather event

Economic
Indirect

Extremeweather event

loss_land = (SUM(area_c * yield_c * price_c))/pop
Estimated value of production loss (USD per capita): multiply the area of land affected bysalinisation by the crop yield loss in tons per hectare and the market price of each crop c ;Economic
divide by the population size
Indirectloss_salin = (SUM(area_c * yield_c * price_c)) / pop
Replacement cost of irrigation or other drainage systems (USD per capita): multiply the
number of items destroyed (resp. damaged) by their price (resp. repair cost) andaggregate the total value; divide by the population size
repl_irrig = ((n_dest * price) + (n_dam * repair_cost)) / pop
Value of livestock (USD per capita): multiply the number of each livestock ls by its market
value and aggregate the total value; divide by the population size
loss_livestock = (SUM( n_ls * price_ls)) / pop
Loss of fish production (USD per capita): Multiply the average number of fishes in fish
farms by their market value ; divide by the population size

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
Indirect

Extremeweather event

loss_fish = (nfish * price) / pop
Value of timber destroyed (USD per capita): multiply the volume timber lost by its market
value ; divide by the population size
Economic
Indirectloss_timber = (vol_timber * price) / pop
Forest restoration cost (USD per hectare): Multiply the seeds cost per kg by the seeds
needed per hectare of land
Economic
Directforest_restore = cost_seeds * n_seeds
Replacement cost of machinery and equipments (USD per capita): multiply the number of
Economic
items destroyed (resp. damaged) by their price (resp. repair cost) and aggregate the total
Directvalue; divide by the population size

26

Slow-onset
process

Extremeweather event

Extremeweather event
Extremeweather eventrepl_agri_machine = ((n_dest * price) + (n_dam * repair_cost)) / pop
Number of jobs in the agriculture, fisheries and forestry
sectors lost after natural hazards

Change in tree cover (unexploited forest) (%)

Change in species abundance and occupancy
Environment
Area affected by desertification (hectares)

Coral reef area affected by coral bleaching (hectares)

Number of people who died or went missing after
natural hazards (per 100 000 population)

Number of people who were injured during natural
hazards (per 100 000 population)
Human and cultural
Number of people who fell below the national poverty
line (per 100 000 population)

Number of persons whose house was destroyed orseveraly damaged by natural hazards

Loss of income in the agriculture, fisheries and forestry sectors (USD per capita): Multiply
the number of jobs lost by the average individual income in each sector s ; divided by the
population size
loss_inc = (SUM(njobs_s * avincome_s)) / pop
Proxy - Forest restoration cost (USD per hectare): Multiply the seeds cost per kg by theseeds needed per hectare of land
forest_restore = cost_seeds * n_seeds
Cost of conserving endangered species (IUCN Red List) (USD per capita): the Conservation
Opportunity Index measures the costs of achieving conservation of species in theirnatural habitat and in zoos
(See methdology source)
Cost of land rehabilitation (USD per hectare): by type of land (e.g. irrigated land, rainfed,
rangeland), multiply the land affected by desertification with the average rehabilitation
cost per hectare. The average rehabilitation cost per hectare varies according to the local
availability of trained technical workers, equipment, transportation, among otherelements
Cost of coral bleaching (USD per capita) proxied by the economic loss in the fisherysector, tourism sector, and loss of flood protection(See methodology source)
Cost of fatalities and missing population (USD per capita): Multiply the number offatalities or missed persons by the Value of Statistical Life ; divide by population size
cost_fatalities = (n_fatalities + n_missing) * VSL / pop
Cost of injuries (USD per capita): Multiply the number of injured people by the averagehospital cost for injuries per capita ; divide by population
cost_injuries = n_injured * av_hospcost / pop
Social safety cost (USD per capita): multiply the number of people who fell into poverty
by the average public expenditure on social protection per capita ; divide by the
population size
cost_poverty = n_pov * av_expsocial / pop
Relocation cost (USD per capita): Multiply the number of people whose house wasdestroyed by the average relocation cost per capita in the area ; divide by the population
size
cost_relocation = n_houses * av_reloccost

27

Economic
IndirectNon-economic

Non-economic

Extremeweather event
Extremeweather event
Slow-onset
process
Extremeweather event
Slow-onset
process

Non-economic

Slow-onset
process

Economic&
Non-economic

Slow-onset
process

Non-economic

Extremeweather event

Non-economic

Extremeweather event

Economic
Indirect

Extremeweather event
Slow-onset
process

Non-economic

Extremeweather event
Slow-onset
processNumber of people suffering from mental health issues
after natural hazards (per 100 000 population)

Number of people who fell into food insecurity (per 100
000 population)

Number of tangible cultural heritage (incl. Cultural or
historical assets, monuments or sites destroyed ordamaged by natural hazards) (per 100 000 population)
Loss of intangible cultural heritage (incl. Cultural identity,
cultural practices and traditions, sense of place)

Number of raw material, machinery and equipments
destroyed or damaged by natural hazards

Industry
Number of jobs lost after natural hazards

Mental health costs (USD per capita): Multiply the number of people affected by natural
hazards by the average public mental healthcare expenditure per capita ; divide by the
population size
cost_mental = n_affected * av_mentalexp / pop
Cost of humanitarian assistance (USD per capita): Multiply the number of people who fell
into food insecurity by the average humanitarian assistance received per capita in thearea ; divide by the population size
fis = (n_fis * av_humassist) / pop
Value of cultural heritage lost (USD per capita) estimated through the repair costs
aggregated to the estimated economic impact of the loss of cultural sites (e.g. degree ofdamage of its historical or touristic function)(See methodology source)
Assess through local surveys the population willingness-to-pay for the preservation of aset of cultural traditions(See methodology source)
Replacement cost of industrial machinery and equipment (USD per capita): Multiply the
number of items destroyed (resp. damaged) by their price (resp. repair cost) andaggregate the total value; divide by the population size

Non-economic

Extremeweather event
Slow-onset
process

Economic
Indirect

Extremeweather event
Slow-onset
process

Economic(indirect)&
Non-economic
Non-economic

Extremeweather event
Slow-onset
process
Extremeweather event
Slow-onset
process

Economic
DirectExtremeweather event

Economic
Indirect

Extremeweather event

Change in GDP per capita in each productive sector (%) (e.g. manufacture, tourism, trade, Economic
services)
Indirect

Extremeweather event

repl_ind_machine = ((n_dest * price) + (n_dam * repair_cost)) / pop
Loss of income in industry (USD per capita): For each industry "ind", multiply the numberof jobs lost by the average individual income; aggregate the total value and divide by the
population size
loss_inc = (SUM(njobs_ind * avincome_ind)) / pop

Loss of production after natural hazards

Number of houses destroyed or damaged by natural
hazards (per 100 000 population)

Infrastructure
Length of roads and railways destroyed or damaged bynatural hazards (km)Housing replacement cost (USD per capita): Multiply the number of houses destroyed(resp. damaged) by the cost to build new houses (resp. to repair houses); aggregate thetotal value and divide by the population

Economic
DirectExtremeweather event

repl_housing = ((n_dest * cost_build) + (n_dam * cost_repair)) / pop
Roads and railways replacement cost (USD per capita): Multiply the number of kilometersof roads (resp. railways and bridges) destroyed by the cost to build one kilometer of road(resp. railways and bridges); aggregate the total value; divide by the population size. The
Economicdifferent types of roads can also be taken into account (e.g. highway, secondary road)
DirectExtremeweather event

repl_roadsrails = ((km_roads * cost_kmroad) + (km_rails * cost_kmrail) + (km_bridge
cost_kmbridge)) / pop

28

Number of airports and ports destroyed or damaged
after natural hazards

Number of schools destroyed or damaged by natural
hazards

Number of school material and equipments destroyed ordamaged by natural hazards

Number of healthcare facilities destroyed or damaged bynatural hazards

Airports and ports replacement cost (USD per capita): Multiply the number of airports
and ports destroyed (resp. damaged) by the average cost of reconstruction (resp. cost of
reparation); divide by the population size
repl_transp = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
Schools replacement cost (USD per capita): Multiply the number of schools destroyed(resp. damaged) by the average cost of reconstruction (resp. cost of reparation) in thearea; divide by the population size
repl_schools = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
Scool material replacement cost (USD per capita): Multiply the number of schoolingmaterial and equipment "me" destroyed (resp. damaged) by their respective price (resp.
cost of reparation) in the area; divide by the population size
repl_material = ((n_dest_me * price) + (n_dam_me * repair_cost)) / pop
Healthcare facilities replacement cost (USD per capita): Multiply the number ofhealthcare facilities destroyed (resp. damaged) by the average cost of reconstruction(resp. cost of reparation) in the area; divide by the population size

repl_hosp = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
Medical equipment replacement cost (USD per capita): Multiply the number ofhealthcare material and equipment destroyed (resp. damaged) by their price (resp. cost
Number of medical material and equipment destroyed orof reparation) in the area; divide by the population size
damaged by natural hazards

Number of government buildings destroyed or damagedby natural hazards

Number of industrial facilities destroyed or damaged bynatural hazards

Number of industrial material and equipment destroyed
or damaged by natural hazards

Length of electrical distribution grids and lines destroyed
or damaged by natural hazards (km)repl_material = ((n_dest_me * price) + (n_dam_me * repair_cost)) / pop
Government buildings replacement cost (USD per capita): Multiply the number ofgovernment buildings destroyed (resp. damaged) by the average cost of reconstruction(resp. cost of reparation) in the area; divide by the population size
repl_govbuild = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
Industrial facilities replacement cost (USD per capita): Multiply the number of industrial
destroyed (resp. damaged) by the average cost of reconstruction (resp. cost of
reparation) in the area; divide by the population size
repl_indfaci = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
Industrial material replacement cost (USD per capita): Multiply the number of industrial
material and equipment destroyed (resp. damaged) by their price (resp. cost of
reparation) in the area; divide by the population size
repl_material = ((n_dest_me * price) + (n_dam_me * repair_cost)) / pop
Electrical grid replacement cost (USD per capita): Multiply the number of kilometers ofelectrical grids destroyed (resp. damaged) by the cost to build (resp. repair) one km of
grid; aggregate the total value; divide by the population size

29

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather eventrepl_electgrid = ((km_dest * av_construct) + (km_dam * av_repair)) / pop
Electrical plants replacement cost (USD per capita): Multiply the number of electrical
power plants destroyed (resp. damaged) by their construction cost (resp. reparation cost)
Number of electrical power plants destroyed or damaged
Economic
in the area; divide by the population sizeby natural hazards
DirectLength of water, sanitation and hygiene (WASH) grid
destroyed or damaged by natural hazards (km)Length of telecommunication infrastructure destroyed ordamaged by natural hazards (km)repl_elecplant = ((n_dest * av_construct) + (n_dam * av_repair)) / pop
WASH grid replacement cost (USD per capita): Multiply the number of kilometers of
WASH grids destroyed (resp. damaged) by the cost to build (resp. repair) one km of thegrid; aggregate the total value; divide by the population size
repl_wash = ((km_dest * av_construct) + (km_dam * av_repair)) / pop
Telecommunication grid replacement cost (USD per capita): Multiply the number ofkilometers of telecommunication grids destroyed (resp. damaged) by the cost to build(resp. repair) one km of the grid; aggregate the total value; divide by the population size

Extremeweather event

Economic
DirectExtremeweather event

Economic
DirectExtremeweather event

repl_wash = ((km_dest * av_construct) + (km_dam * av_repair)) / pop

Sources: Authors’ elaboration based on De Groeve et al.(2015); UNESCAP (2017); Youth Innovation Lab (2020); Jovel and Mudahar (2010); Serdeczny et al. (2016); IRDR (2015); Bahinipati and
Gupta (2022).

30

Annex 3. Suggestions of potential useful data sources to assess L&D costs
SectorCost indicator

Potential useful data
source (global, open
access)Potential useful data
source (global,
restricted access)Value of crops destroyed (USD per capita): multiply the volume
EM-DATof each crop c by its market value and aggregate the total
DesInventar
value; divide by the population size
FAO
UN Stats
loss_crop = (SUM(vol_c * price_c)) / pop
Loss of production (USD per capita): for each type of crop,
multiply the area of land destroyed by the crop yield per
DesInventar
hectare and the crop price ; aggregate the total value ; divide
FAO
by the population size
UN Stats

Potential usefuldata source (local
initiatives)

Data download link

Notes on the cost indicator

https://northsearegion.
eu/media/14789/chap2-economic-analysis-ofsalinization.pdf

Other (economic and noneconomic) costs associated withsoil salinisation exist, including:environmental costs, land value,
investment, employment, foodsupply chains, etc.

Local survey

Local survey

loss_land = (SUM(area_c * yield_c * price_c))/pop
Estimated value of production loss (USD per capita): multiply
the area of land affected by salinisation by the crop yield loss
in tons per hectare and the market price of each crop c ; divideby the population size

Agriculture,
fisheries andloss_salin = (SUM(area_c * yield_c * price_c)) / pop
forestry

Replacement cost of irrigation or other drainage systems (USD
per capita): multiply the number of items destroyed (resp.
damaged) by their price (resp. repair cost) and aggregate thetotal value; divide by the population size
repl_irrig = ((n_dest * price) + (n_dam * repair_cost)) / pop
Value of livestock (USD per capita): multiply the number of
each livestock ls by its market value and aggregate the total
value; divide by the population size
loss_livestock = (SUM( n_ls * price_ls)) / pop
Loss of fish production (USD per capita): Multiply the average
number of fishes in fish farms by their market value ; divide by
the population size

Ruto et al. (2021),"Economic Impact ofSoil Salinization and thePotential for Saline
Agriculture", In: Futureof Sustainable
Agriculture in Saline
Environments

Local survey

DesInventar
FAO
UN Stats

Local survey

DesInventar
FAO
UN Stats

Local survey

FAO
UN Stats

Local survey

31

loss_fish = (nfish * price) / pop
Value of timber destroyed (USD per capita): multiply thevolume timber lost by its market value ; divide by the
population size
loss_timber = (vol_timber * price) / pop
Forest restoration cost (USD per hectare): Multiply the seeds
cost per kg by the seeds needed per hectare of land
forest_restore = cost_seeds * n_seeds
Replacement cost of machinery and equipments (USD per
capita): multiply the number of items destroyed (resp.
damaged) by their price (resp. repair cost) and aggregate thetotal value; divide by the population size
repl_agri_machine = ((n_dest * price) + (n_dam * repair_cost))/ pop
Loss of income in the agriculture, fisheries and forestry sectors(USD per capita): Multiply the number of jobs lost by theaverage individual income in each sector s ; divided by the
population size

DesInventar
FAO
UN Stats

Local survey

DesInventar
FAO
UN Stats

Local survey

DesInventar
EM-DAT
FAO
UN Stats

Local survey

DesInventar
World Bank

Local survey
National statistical
officeloss_inc = (SUM(njobs_s * avincome_s)) / pop

Proxy - Forest restoration cost (USD per hectare): Multiply theseeds cost per kg by the seeds needed per hectare of land
forest_restore = cost_seeds * n_seeds

Example:http://mneguidelines.o
ecd.org/draft-oecd-faohandbook-ondeforestation-forestdegradation-and-duediligence-inagricultural-supplychains.pdf
https://www.cell.com/currentbiology/fulltext/S09609822(15)000809?_returnURL=https%3
A%2F%2Flinkinghub.els
evier.com%2Fretrieve%
2Fpii%2FS09609822150
00809%3Fshowall%3Dt
rue

OECD
FAO
World Bank

Environment
Cost of conserving endangered species (IUCN Red List) (USD
per capita): the Conservation Opportunity Index measures thecosts of achieving conservation of species in their natural
habitat and in zoos
(See methdology source)

Conde et al. (2015),"Opportunities andcosts for preventing
vertebrate extinctions",
Current Biology
Correspondance, Vol.
25, Issue 6

32

Other (economic and noneconomic) costs associated withthe loss of tree cover exist,
including: the health-related costsof tree loss in urban environment,
the costs of tree loss on species
survival and biodiversity, the costs
for growing trees rather than costs
for planting trees, etc.

Cost of land rehabilitation (USD per hectare): by type of land(e.g. irrigated land, rainfed, rangeland), multiply the land
affected by desertification with the average rehabilitation cost
per hectare. The average rehabilitation cost per hectare varies
according to the local availability of trained technical workers,
equipment, transportation, among other elements

Cost of coral bleaching (USD per capita) proxied by the
economic loss in the fishery sector, tourism sector, and loss offlood protection(See methodology source)

Cost of fatalities and missing population (USD per capita):
Multiply the number of fatalities or missed persons by theValue of Statistical Life ; divide by population size
cost_fatalities = (n_fatalities + n_missing) * VSL / pop
Cost of injuries (USD per capita): Multiply the number of
injured people by the average hospital cost for injuries per
capita ; divide by population

Human andculturalExample:http://www.ciesin.org/
docs/002-186/002186.html

FAO
World Bank

Cesar et al. (2003), "Theeconomics ofworldwide coral reef
degradation", Cesar
Environmental
Economics Consulting

https://www.wwf.or.jp/activities/lib/pdf_mari
ne/coralreef/cesardegradationr
eport100203.pdf

Beck et al. (2018), "Theglobal flood protectionsavings provided bycoral reefs", Nature
Communications, Vol.
9, n°2186

https://www.nature.co
m/articles/s41467-01804568-z

EM-DAT
DesInventar

SIGMA
NetCatSer

Local survey

EM-DAT
DesInventar

SIGMA
NetCatSer

Local survey

cost_injuries = n_injured * av_hospcost / pop
Social safety cost (USD per capita): multiply the number ofpeople who fell into poverty by the average public expenditureon social protection per capita ; divide by the population size
World Bank

Local survey

cost_poverty = n_pov * av_expsocial / pop
Relocation cost (USD per capita): Multiply the number ofpeople whose house was destroyed by the average relocation
cost per capita in the area ; divide by the population size

Local survey

EM-DAT

cost_relocation = n_houses * av_reloccost
IMF COFOG
Additional expenses for justice and security (USD per capita)

Gallup World Poll

https://ga.gallup.com/
https://dataunodc.un.org/content/homicidecountry-data

UNDOC
IMF COFOG

33

Mental health costs (USD per capita): Multiply the number ofpeople affected by natural hazards by the average public
mental healthcare expenditure per capita ; divide by the
population size

WHO (proxy: mental
hospital admissions)

Local survey

https://www.who.int/d
ata/gho/data/indicators/indicators-index

cost_mental = n_affected * av_mentalexp / pop
Cost of humanitarian assistance (USD per capita): Multiply the
number of people who fell into food insecurity by the average
humanitarian assistance received per capita in the area ; divide
WHO, WFP, DAC, OCHA
by the population size

Other (economic and noneconomic) costs associated withfood insecurity exist, including:decreased educational
attainment, decreased incomes on
the long-term, increased social
protection expenditure, decreased
well-being, higher expenses spent
on food imports, etc.

Local survey

fis = (n_fis * av_humassist) / pop

Value of cultural heritage lost (USD per capita) estimatedthrough the repair costs aggregated to the estimatedeconomic impact of the loss of cultural sites (e.g. degree ofdamage of its historical or touristic function)(See methodology source)

Assess through local surveys the population willingness-to-pay
for the preservation of a set of cultural traditions(See methodology source)

See costing
methodology by Romão
and Paupério (2018),"An indicator for the
economic loss in valueof damaged cultural
heritage properties",
8th ICBR - International
Conference on Building
Resilience
Example:Vondolia et al. (2022),"Valuing Intangible
Cultural Heritage in
Developing Countries",
Sustainability, Vol. 14,
Issue 8, p. 4484

Local survey

https://repositorioaberto.up.pt/handle/10
216/119545

Local survey

https://doi.org/10.3390/su14084484

Replacement cost of industrial machinery and equipment (USD
per capita): Multiply the number of items destroyed (resp.
damaged) by their price (resp. repair cost) and aggregate thetotal value; divide by the population size
Industry

repl_ind_machine = ((n_dest * price) + (n_dam * repair_cost))/ pop
Loss of income in industry (USD per capita): For each industry"ind", multiply the number of jobs lost by the average
individual income; aggregate the total value and divide by the
population size

Local survey

34

loss_inc = (SUM(njobs_ind * avincome_ind)) / pop
Change in GDP per capita in each productive sector (%) (e.g.
manufacture, tourism, trade, services)
Housing replacement cost (USD per capita): Multiply the
number of houses destroyed (resp. damaged) by the cost tobuild new houses (resp. to repair houses); aggregate the total
value and divide by the population

EM-DAT
DesInventar

NetCatSer

repl_housing = ((n_dest * cost_build) + (n_dam * cost_repair))/ pop
Roads and railways replacement cost (USD per capita):
Multiply the number of kilometers of roads (resp. railways andbridges) destroyed by the cost to build one kilometer of road(resp. railways and bridges); aggregate the total value; divideby the population size. The different types of roads can also betaken into account (e.g. highway, secondary road)

Local survey

repl_roadsrails = ((km_roads * cost_kmroad) + (km_rails *cost_kmrail) + (km_bridge cost_kmbridge)) / pop
Airports and ports replacement cost (USD per capita): Multiply
the number of airports and ports destroyed (resp. damaged)
by the average cost of reconstruction (resp. cost of
EM-DAT
Infrastructur reparation); divide by the population size
DesInventarerepl_transp = ((n_dest * av_construct) + (n_dam * av_repair)) /pop
Schools replacement cost (USD per capita): Multiply the
number of schools destroyed (resp. damaged) by the average
cost of reconstruction (resp. cost of reparation) in the area;
EM-DAT
divide by the population size
DesInventar
repl_schools = ((n_dest * av_construct) + (n_dam * av_repair))/ pop
Scool material replacement cost (USD per capita): Multiply the
number of schooling material and equipment "me" destroyed(resp. damaged) by their respective price (resp. cost of
reparation) in the area; divide by the population size
repl_material = ((n_dest_me * price) + (n_dam_me *repair_cost)) / pop
Healthcare facilities replacement cost (USD per capita):
Multiply the number of healthcare facilities destroyed (resp.
damaged) by the average cost of reconstruction (resp. cost of

Local survey

NetCatSer

Local survey

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

35

reparation) in the area; divide by the population size
repl_hosp = ((n_dest * av_construct) + (n_dam * av_repair)) /pop
Medical equipment replacement cost (USD per capita):
Multiply the number of healthcare material and equipment
destroyed (resp. damaged) by their price (resp. cost of
reparation) in the area; divide by the population size
repl_material = ((n_dest_me * price) + (n_dam_me *repair_cost)) / pop
Government buildings replacement cost (USD per capita):
Multiply the number of government buildings destroyed (resp.
damaged) by the average cost of reconstruction (resp. cost of
reparation) in the area; divide by the population size
repl_govbuild = ((n_dest * av_construct) + (n_dam *av_repair)) / pop
Industrial facilities replacement cost (USD per capita): Multiply
the number of industrial destroyed (resp. damaged) by theaverage cost of reconstruction (resp. cost of reparation) in thearea; divide by the population size
repl_indfaci = ((n_dest * av_construct) + (n_dam * av_repair))/ pop
Industrial material replacement cost (USD per capita): Multiply
the number of industrial material and equipment destroyed(resp. damaged) by their price (resp. cost of reparation) in thearea; divide by the population size
repl_material = ((n_dest_me * price) + (n_dam_me *repair_cost)) / pop
Electrical grid replacement cost (USD per capita): Multiply the
number of kilometers of electrical grids destroyed (resp.
damaged) by the cost to build (resp. repair) one km of grid;aggregate the total value; divide by the population size
repl_electgrid = ((km_dest * av_construct) + (km_dam *av_repair)) / pop
Electrical plants replacement cost (USD per capita): Multiply
the number of electrical power plants destroyed (resp.
damaged) by their construction cost (resp. reparation cost) in
the area; divide by the population size

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

EM-DAT
DesInventar

NetCatSer

Local survey

repl_elecplant = ((n_dest * av_construct) + (n_dam *av_repair)) / pop

36

WASH grid replacement cost (USD per capita): Multiply the
number of kilometers of WASH grids destroyed (resp.
damaged) by the cost to build (resp. repair) one km of the grid;
EM-DATaggregate the total value; divide by the population size
DesInventar
repl_wash = ((km_dest * av_construct) + (km_dam *av_repair)) / pop
Telecommunication grid replacement cost (USD per capita):
Multiply the number of kilometers of telecommunication grids
destroyed (resp. damaged) by the cost to build (resp. repair)one km of the grid; aggregate the total value; divide by the
population size

EM-DAT
DesInventar

NetCatSer

Local survey

NetCatSer

Local survey

repl_wash = ((km_dest * av_construct) + (km_dam *av_repair)) / pop

Sources: Authors’ elaboration based on EU (2015); UNESCAP (2017); Youth Innovation Lab (2020); Jovel and Mudahar (2010); Serdeczny et al. (2016); IRDR (2015); Bahinipati and Gupta (2022).

37

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