Editing IPCC:AR6/WGII/Chapter-10 (section)

=== 10.5.4 Costs and Finance ===

<div id="h2-14-siblings" class="h2-siblings"></div>

<div id="10.5.4.1" class="h3-container"></div>

<span id="point-of-departure-2"></span>
==== 10.5.4.1 Point of Departure ====

<div id="h3-40-siblings" class="h3-siblings"></div>

Estimates of adaptation costs and financial needs have evolved significantly since the previous IPCC assessments. These developments are based on improvements in the understanding of how the hazard interacts with the physical and socioeconomic elements, and how to capture these interactions in systematic modelling frameworks. The developments are also clearly reported especially in the area of addressing the underestimates in adaptation costs that the previous studies suffered from as the previous studies tended to rely on data from wealthy economies ( [[#Hochrainer-Stigler--2014|Hochrainer-Stigler et al., 2014]] ; [[#Carleton--2020|Carleton et al., 2020]] ). The adaptation cost estimates have also improved since the previous IPCC reports due to constant improvements in capturing the loss and damages of disaster events ( [[#Hochrainer-Stigler--2014|Hochrainer-Stigler et al., 2014]] ). The reliance of earlier studies on correlations to derive adaptation costs was addressed to some extent by addressing the endogeneity of disaster measures ( [[#Kousky--2014|Kousky, 2014]] ), especially by relying upon the physical measures of disasters such as wind speed, although more work is needed in this area.

<div id="10.5.4.2" class="h3-container"></div>

<span id="findings-3"></span>
==== 10.5.4.2 Findings ====

<div id="h3-41-siblings" class="h3-siblings"></div>

Climate change can cause significant impacts and as a result can impose considerable adaptation costs on countries and people. Despite the importance, the research on adaptation costs is limited in Asia, especially on the economy-wide costs, while fragmented literature is available on sector-level adaptation costs. Most of the available literature on adaptation costs at the regional level originate from the work carried out by development finance institutions such as ADB.

Estimates suggest that climate-change impacts could result in a loss of 2% of the GDP of South Asian countries by 2050 and 9% by 2100 ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ). These impacts will be felt in major vulnerable sectors, including agriculture, water, coastal, marine, health and energy, and will have significant impact on the economic growth and poverty reduction in the region. Countries could differ widely in terms of the economic costs they face. In South Asia, the economic costs were projected to be 12.6% of the GDP for the Maldives, which is the highest among the South Asian countries, and 6.6% for Sri Lanka, the least among the South Asian countries. The resultant adaptation costs for countries were projected to range from 0.36% (Copenhagen Cancun Scenario for 2050) to 1.32% (business-as-usual scenario) of the GDP in various scenarios during 2010–2050 ( ''Medium agreement'' , ''limited evidence'' ) ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ).

[[#Arto--2019|Arto et al. (2019)]] have reported the adaptation costs of the Mahanadi Delta in India for agriculture, fisheries and infrastructure sectors ( [[#Arto--2019|Arto et al., 2019]] ). The cumulative adaptation costs for 2015–2016 were reported to be 276 million USD for agriculture and 0.163 million USD for fisheries. In comparison, the modelled cumulative agricultural GDP loss due to climate-change impacts was reported to be 5% up to 2050, and 8% for infrastructure. Adaptation interventions, such as embankments, were found to provide an avoided losses (adaptation benefits) to the tune of 2.2% of the delta’s GDP by 2050. Similarly, input subsidies in seeds, fertilisers and biofertilisers were found to buffer the shocks in agriculture by 10%, and buffer the GDP per capita by 3% ( [[#Arto--2019|Arto et al., 2019]] ).

[[#Markandya--2019|Markandya and González-Eguino (2019)]] have estimated the adaptation costs and residual adaptation costs accrued due to insufficient adaptation using integrated assessment models. Using the residual damages as a measure of loss and damage, the authors have estimated adaptation costs and residual costs under scenarios of high damages–low discount rate and low damages–high discount rate. The estimates suggested adaptation costs of 182 and 193 billion USD by 2050, and 737 and 783 billion USD by 2100 for South Asia and East Asia, respectively, under the scenario of high damages–low discount rate. The residual costs for the same scenario were 289 and 76% for 2050 and 238 and 62% for 2100 for South Asia and East Asia, respectively. Estimates for low damages–high discount rate were significantly lower adaptation costs and residual costs for both of these sub-regions of Asia.

The CCA efforts can be characterised as fragmented, incoherent and lacking perspective ( [[#Ahmed--2019a|Ahmed et al., 2019a]] ), and the picture on adaptation financing can be stated as similarly fragmented with very limited literature published in peer-reviewed journals. Adaptation financing is crucial for supporting vulnerable countries and enhances adaptation, as it is evident that the enhanced adaptation finance support has positively affected the pace of adaptation in low-income countries ( [[#Ford--2015|Ford et al., 2015]] ). At the organisational level, adaptation financing has provided multiple functions that include risk assessment functions, valuation functions and risk disclosure functions ( [[#Linnenluecke--2016|Linnenluecke et al., 2016]] ).

Of the total global public adaptation finance of 28 billion USD, East Asia and the Asia–Pacific attracted 46% of the total funding, while South Asian countries attracted only 9% of the total funding ( [[#UNEP--2016|UNEP, 2016]] ). These differences reflect the capacity of countries to attract adaptation finance. Some of the important adaptation-targeted climate funds are Pilot Programmes for Climate Resilience, Green Climate Fund, and Least-Developed Countries Fund, and South Asian countries have significantly benefited from these dedicated climate funds. Due to the disaster implications of climate change, there is a need to allocate adaptation finances for DRR. Estimates suggest that East Asia and the Asia–Pacific in general allocated 27% of the total adaptation funds to DRR, while South Asia allocated 25% ( [[#Caravani--2016|Caravani, 2016]] ). Low-income economies tend to allocate more adaptation funds to DRR (46%), while lower-middle-income economies allocated 22%.

The least developed countries lack the capacity to adapt to climate change and the Least Developed Country Fund (LDCF) has made significant contributions to adaptation in these countries ( ''High agreement, limited evidence'' ). Based on the interview-based field research in four least developed countries, Sovacool et al. (2017) opined that the LDCF projects are contributing to the adaptive capacity of these countries ( [[#Sovacool--2017|Sovacool et al., 2017]] ). They also found that these projects are taking a marginal approach, rather than a radical or transformational one, to adaptation.

[[#Kissinger--2019|Kissinger et al. (2019)]] have estimated the climate financing needs in the land sector under the Paris Agreement. The estimates suggested adaptation needs of 2.5 billion USD for Bangladesh, 40.5 million USD for Lao PDR and 31 million USD for Mongolia, for the forest sector alone ( ''Low agreement, limited evidence'' ).

Financing green growth and low-carbon development can provide resilience benefits ( ''high agreement'' , ''limited evidence'' ). Kameyama et al. (2016) have estimated the cost of low-carbon investments that can provide resilience benefits in Asia and reported that such low-carbon development will cost in the range of 125–149 billion USD annually. A combination of public, private, bilateral and multilateral funding sources, and carbon-market offsets, were suggested to achieve this level of funding. In terms of the total resources available, a combination of public, private and bi- and multi-lateral funding could help the region to raise as much as 222.3–412.5 billion USD annually, with a possibility to reach higher amounts depending on the future economic growth of countries in the region. Soil carbon sequestration in agricultural soils was found to be a win–win solution for both mitigation and adaptation as it can help improve soils while increasing farm yields and incomes of smallholders ( [[#Aryal--2020a|Aryal et al., 2020a]] ).

New adaptation financing sources have been emerging which could provide country-specific adaptation financing suiting local-level adaptation needs in Asia. The newly established Asia Infrastructure Investment Bank (AIDB), and newly emerging developing-country finance institutions, are known to provide an additional adaptation finance ( [[#Neufeldt--2018|Neufeldt et al., 2018]] ); however, despite these emerging financial sources, the region will fall short of the adaptation target in the Paris Agreement ( [[#Neufeldt--2018|Neufeldt et al., 2018]] ).

<div id="10.5.4.3" class="h3-container"></div>

<span id="knowledge-gaps-1"></span>
==== 10.5.4.3 Knowledge Gaps ====

<div id="h3-42-siblings" class="h3-siblings"></div>

Adaptation cost estimates can vary between various studies due to the differences in methodologies they adopt. Some studies have conducted cost assessments using a combination of stakeholder consultations and quantitative modelling of climate-change impacts and adaptation ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ), while others depended solely on the quantitative modelling. Studies also differ in the coverage of sectors too: they either have focused on the multiple vulnerable sectors ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ) or on a single sector ( [[#Hossain--2019|Hossain et al., 2019]] ). Studies have differed in their estimates depending on their ability to take into consideration the transition costs of sudden adaptation ( [[#Hossain--2019|Hossain et al., 2019]] ), the nature of social cost and/or damage functions employed ( [[#Arto--2019|Arto et al., 2019]] ), the discount rates applied ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ) and consideration for the effects of GHG mitigation on adaptation needs ( [[#Duan--2019a|Duan et al., 2019a]] ). In addition, the assumptions made on the pace of adaptation in estimating adaptation costs can make a difference in adaptation cost estimates. Adaptation at a slow or normal pace could require more adaptation finance, as large amounts of damage are not eliminated, than when adaptation is implemented at a faster rate ( [[#Markandya--2019|Markandya and González-Eguino, 2019]] ). Although there have been improvements in adaptation cost estimates, there is a need to address the issue of endogeneity ( [[#Kousky--2014|Kousky, 2014]] ; [[#Samuel--2019|Samuel et al., 2019]] ). The vast majority of studies that rely on databases, such as EM-DAT, tend to suffer from such endogeneity problems due to their inability to control the causality between GDP and damages ( [[#Kousky--2014|Kousky, 2014]] ). Costs attributable to non-economic losses and damages are the least reported and least quantified in the adaptation costs literature due to lack of sufficient, robust and accessible methodologies ( [[#Chiba--2017|Chiba et al., 2017]] ; [[#Chiba--2019|Chiba et al., 2019]] ; [[#Serdeczny--2019|Serdeczny, 2019]] ). This is a major limitation in assessing adaptation costs and financial needs, and it can lead to gross underestimation of adaptation costs. A detailed description of issues related to non-economic losses and damages, and its importance in strengthening adaptation, is provided in Box 10.6 and Table 10.5.

<div id="box-10.6" class="h2-container box-container"></div>

'''Box 10.6 | Loss and Damage Across Asia: Mapping the Evidence and Knowledge Gaps'''
<div id="h2-26-siblings" class="h2-siblings"></div>

Losses and damages are climate impacts after implementing adaptation and mitigation actions, signifying the presence of residual risks (Chapter 1; [[#Kugler--2016|Kugler and Sariego, 2016]] ; [[#Mechler--2019|Mechler et al., 2019]] ). These residual risks indicate that despite adaptation, there are soft and hard adaptation limits ( [[#Mechler--2019|Mechler et al., 2019]] ). This box reviews the adaptation literature across 51 countries in Asia on loss and damage (L&amp;D), and adaptation barriers and limits, and identifies knowledge and regional gaps. The key messages are that (a) climate-induced L&amp;D is already occurring across Asia ( ''medium evidence, high agreement'' ), (b) these L&amp;D are ''very likely'' to increase at higher warming levels ( ''medium evidence, high agreement'' ) and (c) measuring and attributing non-economic and intangible L&amp;D remains a challenge ( ''low evidence, high agreement'' ).

'''Findings on losses and damages in Asia:''' Evidence on climate-related L&amp;D highlights tangible or material losses and damages such as loss to life, property, infrastructure and livelihoods ( ''medium evidence, high agreement'' ); and intangible or non-material losses and damages such as increasing conflict and civil unrest, erosion of sociocultural practices and decreased well-being ( ''low evidence, high agreement'' ). The main constraint in assessing past and future L&amp;D is that this terminology is not used prominently or consistently in the disaster management and climate risk literature in Asia, which potentially leads to under-reporting. In contrast, there is ''robust evidence'' ( ''high agreement'' ) on adaptation constraints, notably on governance, informational and physical constraints, to adapting, but regional evidence is very uneven with gaps in Central, North and West Asia. Table 10.5 presents a summary of L&amp;D but draws on national and subnational studies.

The knowledge gaps are as follows:

* Attribution studies linking anthropogenic climate change and L&amp;D remain focused on rapid-onset extreme events, and evidence on L&amp;D from slow-onset events, such as drought and water scarcity, is low ( [[#Pereira--2019|Pereira et al., 2019]] ; [[#Singh--2021a|Singh et al., 2021a]] ).
* Regional evidence gaps in Central, North and West Asia; and ''low evidence'' of national-level projected L&amp;D ( [[#Uchiyama--2020|Uchiyama et al., 2020]] ; [[#Singh--2021a|Singh et al., 2021a]] ).
* Disproportionate emphasis on economic L&amp;D while intangible, non-economic L&amp;D are relatively less measured and reported ( [[#Chiba--2017|Chiba et al., 2017]] ; [[#Bahinipati--2020|Bahinipati, 2020]] ). Economic loss estimates are largely approximations and therefore suffer from various methodological, assumption and data-related uncertainties.
* Insufficient literature differentiating L&amp;D under future adaptation scenarios, which makes assessment of residual damages and future L&amp;D difficult. The L&amp;D projections are constrained by limited understanding on how vulnerabilities will evolve with economic and demographic changes. Most projected L&amp;D are based on the population and GDP projections. More future projections are based on the RCP scenarios, and the least number of studies were conducted on the combination of RCP and SSPs.
* Mitigation will have L&amp;D and adaptation co-benefits ( [[#Kugler--2016|Kugler and Sariego, 2016]] ; [[#Toussaint--2020|Toussaint, 2020]] ), especially at the lower temperature stabilisation 1.5°C ( [[#Nishiura--2020|Nishiura et al., 2020]] ), but the literature is currently insufficient to assess these L&amp;D co-benefits of mitigation efforts.
* Negligible regional evidence on limits to adaptation.

'''Way forward:''' Developing robust metrics and institutions for measuring and reporting L&amp;D at national and regional scales, especially non-economic damages and L&amp;D due to slow-onset events, is critical. In addition to vulnerability assessments, assessing L&amp;D and limits to adaptation can inform adaptation prioritisation and enhance adaptation effectiveness (e.g., [[#Craft--2016|Craft and Fisher, 2016]] ; [[#Leiter--2019|Leiter et al., 2019]] ). Lessons are available from biodiversity and ecosystem services monitoring frameworks that have well-developed metrics and processes (e.g., [[#Díaz--2020|Díaz et al., 2020]] ).

'''Table 10.5 |''' Tangible and intangible losses and damages across Asia a

{| class="wikitable"
|-
! rowspan="3"| Sub-region (no. of papers)

! rowspan="3"| Key risks reported in L&amp;D papers

! colspan="5"| Losses and damages

! colspan="8"| Adaptation constraints (bold ticks denote strong barrier)

! colspan="2"| Adaptation limits

|-
! colspan="4"| Tangible

! rowspan="2"| Intangible

! rowspan="2"| E

! rowspan="2"| S

! rowspan="2"| H

! rowspan="2"| G

! rowspan="2"| F

! rowspan="2"| I

! rowspan="2"| P

! rowspan="2"| B

! rowspan="2"| Soft

! rowspan="2"| Hard

|-
! Past

! RCP2.5

! RCP4.5

! RCP8.5

|-
| East Asia (32)

| Coastal flooding, heatwaves, SLR

| \***

| \*

| \**

| \**

| \*

| ✓

|
| ✓

| ✓

| ✓

| ✓

|
| NE

| NE

|-
| Southeast Asia (4)

| Coastal flooding, SLR

| \*

|
| \*

|
| ✓

| ✓

|
| ✓

|
| NE

| NE

|-
| South Asia (18)

| Coastal flooding, drought, SLR, heatwaves

| \***

| \*

| \**

| \**

| \**

| ✓

|
| ✓

| ✓

| ✓

|
| \*

| \**

|-
| Central Asia (3)

| Snowmelt, heatwaves, drought

| \*

|
| \*

| \*

|
| ✓

| ✓

|
| NE

| NE

|-
| North Asia (2)

| Permafrost thaw

|
| \*

| \*

| \*

|
| ✓

|
| NE

| NE

|-
| West Asia (9)

| Heatwaves, drought

| \**

|
| \*

| \*

| \*

|
| ✓

|
| \*

| \**

|-
| colspan="2"| Magnitude of losses and damages

| colspan="5"| Evidence

| colspan="10"| Adaptation constraints

|-
| rowspan="2"|
| rowspan="2"| High (&gt;50% sector/population affected relative to reported baseline)

| rowspan="2" colspan="2"| \***

| rowspan="2" colspan="3"| High (≥10 papers)

| colspan="4"| E

| colspan="6"| Economic

|-
| colspan="4"| S

| colspan="6"| Sociocultural

|-
| rowspan="2"|
| rowspan="2"| Medium (25–50% sector/population affected)

| rowspan="2" colspan="2"| \**

| rowspan="2" colspan="3"| Medium (5–9 papers)

| colspan="4"| H

| colspan="6"| Human capacity

|-
| colspan="4"| G

| colspan="6"| Governance

|-
| rowspan="2"|
| rowspan="2"| Low (&lt;25% sector/population affected)

| rowspan="2" colspan="2"| \*

| rowspan="2" colspan="3"| Low (≤4 papers)

| colspan="4"| F

| colspan="6"| Financial

|-
| colspan="4"| I

| colspan="6"| Informational/technological

|-
| rowspan="2"|
| rowspan="2"| Not assessed due to inadequate evidence

| rowspan="2" colspan="2"| NE

| rowspan="2" colspan="3"| No evidence

| colspan="4"| P

| colspan="6"| Physical

|-
| colspan="4"| B

| colspan="6"| Biological

|}

Notes:

'''East Asia:''' [[#Tezuka--2014|Tezuka et al. (2014)]] ; Elliott et al. (2015); [[#Lei--2015|Lei et al. (2015)]] ; [[#Li--2015a|Li et al. (2015a)]] ; [[#Li--2015b|Li et al. (2015b)]] ; [[#Kim--2016a|Kim et al. (2016a)]] ; [[#Lee--2016|Lee and Kim (2016)]] ; [[#Yu--2016|Yu (2016)]] ; [[#Zhao--2016b|Zhao et al. (2016b)]] ; Abadie et al. (2017); [[#Chen--2017a|Chen et al. (2017a)]] ; Chen et al. (2017b); [[#Chung--2017b|Chung et al. (2017b)]] ; [[#Lee--2017|Lee et al. (2017)]] ; [[#Feng--2018a|Feng et al. (2018a)]] ; [[#Lee--2018b|Lee et al. (2018b)]] ; Lee et al. (2018c); [[#Udo--2018|Udo and Takeda (2018)]] ; [[#Yu--2018a|Yu et al. (2018a)]] ; [[#Yu--2018c|Yu et al. (2018c)]] ; [[#Lee--2019|Lee et al. (2019)]] ; [[#Liu--2019c|Liu et al. (2019c)]] ; [[#Liu--2019d|Liu et al. (2019d)]] ; [[#Wang--2019b|Wang et al. (2019b)]] ; [[#Wu--2019d|Wu et al. (2019d)]] ; [[#Kim--2020|Kim and Lee (2020)]] ; [[#Liu--2020|Liu (2020)]] ; [[#Liu--2020|Liu and Chen (2020)]] ; [[#Yu--2020|Yu et al. (2020)]] . '''Southeast Asia:''' [[#Giuliani--2016|Giuliani et al. (2016)]] ; Dau et al. (2017); [[#Vu--2017|Vu and Ranzi (2017)]] ; [[#Mehvar--2018|Mehvar et al. (2018)]] . '''South Asia:''' Wijetunge (2014); [[#Ahmed--2016b|Ahmed et al. (2016b)]] ; [[#Jevrejeva--2016|Jevrejeva et al. (2016)]] ; [[#Patankar--2016|Patankar and Patwardhan (2016)]] ; Abadie et al. (2017); [[#Aslam--2017|Aslam et al. (2017)]] ; Chiba et al. (2017); [[#Mishra--2017|Mishra et al. (2017)]] ; [[#van%20der%20Geest--2017|van der Geest (2017)]] ; [[#Chhogyel--2018|Chhogyel and Kumar (2018)]] ; [[#Jevrejeva--2018|Jevrejeva et al. (2018)]] ; [[#Leng--2019|Leng and Hall (2019)]] ; [[#Bahinipati--2020|Bahinipati (2020)]] ; [[#Bahinipati--2020|Bahinipati and Patnaik (2020)]] ; [[#Khan--2020|Khan et al. (2020)]] ; Bhowmik et al. (2021). '''Central Asia:''' [[#Groll--2015|Groll et al. (2015)]] ; [[#Babagaliyeva--2017|Babagaliyeva et al. (2017)]] ; [[#Otto--2017|Otto et al. (2017)]] . '''North Asia:''' [[#Gleick--2014|Gleick (2014)]] ; [[#Hjort--2018|Hjort et al. (2018)]] ; [[#Tschakert--2019|Tschakert et al. (2019)]] . '''West Asia:''' [[#Mantyka-Pringle--2015|Mantyka-Pringle et al. (2015)]] ; [[#Pal--2016|Pal and Eltahir (2016)]] ; [[#Ghomian--2017|Ghomian and Yousefian (2017)]] ; Gohari et al. (2017); [[#Ashrafzadeh--2019b|Ashrafzadeh et al. (2019b)]] ; [[#Bierkens--2019|Bierkens and Wada (2019)]] ; [[#Houmsi--2019|Houmsi et al. (2019)]] ; Mosavi et al. (2020).

(a) For definitions on losses and damages and limits, see Cross-Chapter Box LOSS in Chapter 1.

<div id="10.5.5" class="h2-container"></div>

<span id="risk-insurance"></span>