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

==== 10.5.4.3 Knowledge Gaps ====

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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.

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'''Box 10.6 | Loss and Damage Across Asia: Mapping the Evidence and Knowledge Gaps'''
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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.

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