Ways and means of assessing socio-economic losses and ......Ways and means of assessing losses and damages from climate change Background document Workshop on Assessing socio-economic

Ways and means of assessing losses and damages from climate change

Background document

Workshop on Assessing socio-economic losses and damages from climate

change

13 January 2021, 14.00 – 19.00 CET (GMT +1)

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1. Introduction

1. Climate change due to human activity is happening and will cause increasingly severe impacts on

the economy, society, biodiversity and ecosystems. Some types of extreme weather events will become

more intense and more frequent; slow-onset changes, such as sea-level rise, will transform the conditions

under which humans live; while tipping points in the climate system, such as the collapse of ice-sheets,

could bring about large-scale irreversible changes to the planet. At risk are the lives and livelihoods of

hundreds of millions if not billions of people, development gains and economic prosperity. In several

regions, these changes may undermine political stability and social cohesion.

2. There have been tremendous efforts and important progress in the past decades in understanding

the risks and associated impacts of climate change. There is a good understanding of some of the broader

changes in climate under different emission scenarios in coming decades. At a smaller scale, for some

climate phenomena and for some regions, projections of climate impacts are subject to high levels of

uncertainty, even to the extent that the projected direction of change may differ across different climate

models. Estimates of the sensitivity of the climate response (i.e. the extent to which global surface

temperature will change) are also less well-constrained than we would wish for due to the inherent

complexity and internal variability of the climate system. In terms of understanding the implications of

climate change, these physical uncertainties are compounded by uncertainty over socio-economic factors,

as well as the poorly understood and non-linear responses of many human and natural systems to a

changing climate. The likelihood and timing at which tipping points may be triggered are even less well

understood. In order to limit and manage the risks of losses and damages from climate change, it is

important to have a better understanding not just of the projections themselves but also of the uncertainties

around these projections.

3. Effective regional, national and international action for sustainable development requires improved

understanding of the multifaceted ways that climate change affects society and welfare. Assessing the

likelihood and extent of the losses and damages from climate change is not an easy problem. This note

aims to provide an overview of a few well-established methodological approaches to assessing socio-

economic losses and damages, highlighting their strengths, weaknesses and comparability, serving as

backdrop for the workshop on 13 January 2021, “Assessing the socio-economic losses and damages from

climate change”. The workshop will focus in on a number of outstanding research questions, such as: the

possibility of assessing the socio-economic costs of crossing thresholds and triggering tipping points; the

comparability of the outcomes from different methods and therefore the possibility of providing aggregate

estimates; and the possibility of reflecting difficult-to-quantify impacts in the evaluation and decision making

process.

4. The rest of the note is structured as follows: section 2 introduces the methodologies assessing

quantitative impacts, starting with the econometric approaches, then proceeds to present integrated

assessment and bottom-up models. Section 3 discuss other, possibly complementary approaches, which

assess losses and damages that are typically more difficult to quantify, ethically and/or methodologically.

Section 4 outlines questions that will guide the discussion during the workshop on 13 January. The note

will be revised following the workshop to take into account the takeaway messages and key issues

emerging from the discussion as well as any comments that participants may have.

2. Methodologies assessing quantitative impacts

2.1 Econometric approaches

5. Econometric approaches use past data to estimate the effects of climate variability and change on

various quantifiable outcomes, such as GDP (Dell, Jones and Olken, 2012[1]; Burke, Hsiang and Miguel,

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2015[2]), mortality (Heutel, Nolan and Molitor, 2017[3]; Carleton et al., 2020[4]), and agricultural yields

(Mendelsohn, Nordhaus and Shaw, 1994[5]). Auffhammer (2018[6]) and Kolstad and Moore (2020[7]) provide

comprehensive overviews of climate econometrics literature, while Botzen, Dechenes and Sander (2019[8])

review the related literature on the effects of natural disasters.

6. Early models compared countries or regions with each other by regressing outcomes on long-

period averages of weather, as a proxy for climate (Mendelsohn, Nordhaus and Shaw, 1994[5]). The

advantage of these cross-sectional models is that they capture the effect of climate (as opposed to

weather) with the possible long-run adaptation opportunities. A drawback is that the cross-sectional

identification implicitly assumes that the same climate will have the same effect in different locations,

conditioned on control variables in the regression. It is unlikely that all relevant control variables are

included, thus estimates may suffer from omitted variable bias, particularly if the factor omitted is not

independent of the effect of climate on the outcome variable.. For example, Schlenker, Hanemann, and

Fisher (2005[9]) revisits the analysis of Mendelsohn, Nordhaus and Shaw (1994[5]), by excluding irrigated

counties to control for parts of the omitted variables about access to irrigation and varying prices, and gets

different results. From a theoretical perspective, the agents in a cross-sectional model do not expect

climate to change over time (climate is expected to be stationary). Otherwise, the estimate capture not

only the effect of climate, but also the expectations of climate, for example, preventive adaptation

measures. However, if agents are rational they are likely to treat climate as changing.

7. More recent models use panel data: the same cross-sectional units are observed over several

time periods. Usually such models control for the unobserved heterogeneity of cross-sectional units (e.g.

by taking differences or including fixed effects), to control for the omitted variable bias (Dell, Jones and

Olken, 2012[1]; Burke, Hsiang and Miguel, 2015[2]). Treating unobserved heterogeneity addresses the

omitted variables bias, but it introduces another issue. Studies cannot use long-period averages of

weather, but only shorter – typically annual - variations, which are closer to weather than to climate (Kolstad

and Moore, 2020[7]). The extent to which annual weather observations, which are quite variable, can

provide insights on the effects on climate is subject to scholarly debate. Hallegatte, Rentschler and

Rozenberg (2020[10]) argues that using annual weather fluctuations could underestimate the effects of

climate, because some impacts might not materialise for a change in weather, but will under the change

in climate. On the other hand, the approach could also overestimate the true effects of climate change,

because the adaptation will not be undertaken in the short run as a response to weather fluctuations, but

only in response to longer-term (anticipated) changes.

8. On the other hand, Deryugina and Hsiang (2017[11]) argue that the effect of annual weather

fluctuations and climate is the same if there are limited opportunities to adaptation or if adaptation can be

fine-tuned and is reversible (e.g. how much water to use for irrigation).1 Indeed a few papers have shown

that the mid-term effects on GDP are comparable to the short run effects; estimating with multiple year

differences in averages, instead of annual differences yields similar results (Dell, Jones and Olken, 2012[1];

Burke and Emerick, 2016[12]; Hsiang, 2016[13]). Thus, these studies argue that the benefits of adaptation

appears to be limited at best. Barecca et al (2016[14]) provides a counterexample and show that after 1960

households in the U.S. took adaptation measures, which starkly cha/nged the impacts of temperature on

mortality. Specifically, they installed air conditioning systems, which decreased the heat-mortality

relationship (the same temperature caused 80% less deaths during 1960-2014, than it did in 1931-1959).

9. Some approaches explicitly estimate the long run (climate) and short run (weather) effects. For

example, by including annual weather variations from an average and the average itself in a single

regression framework (Bento et al., 2020[15]). Kolstad and Moore (2020[7]) label this partitioning variation,

when both are included linearly in the regression. Another approach in this direction, is to first estimate the

1 This theoretical result only holds with optimised outcomes (e.g. profits or welfare), but not to different equilibrium

outcomes (e.g. mortality or ecosystems).

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effect of climate by regions, and then examine what influences the effect itself, for example cold days are

more damaging in hot places and vice versa. As with the cross-sectional models, these approaches need

a wide arrange of control variables to be credible, otherwise, the estimates will be biased by omitted

variables.

10. A fundamental limitation of econometric approaches is that, since all the data is about the past, it

is unclear how well do they perform out-of-sample. Thus, they can accurately capture small, marginal

changes in climate, but may be unreliable in estimating the effects of the larger shifts that are predicted by

the physical sciences.

2.2 Integrated assessment models

11. Another quantitative approach for assessing the socio-economic implication of climate change is

to provide a model, in which the drivers are explicitly described in an aggregate framework. These models

not only examine the effect of climate on the economy, but also explicitly model the effect of the economy

on climate. Since they integrate physical and economic models, the literature labelled them integrated

assessment models (IAMs, e.g. DICE model, (Nordhaus, 1992[16]); PAGE model, (Stern, 2006[17])) of global

climate change. Given assumptions on, among others, GDP growth, population growth policies and

technology, IAMs inform researchers and policy makers about economic and environmental outcomes as

well as energy pathways and land use based on simplified firm and household decisions. IAMs have a

representation of economic, energy, land and climate systems and are, thus, useful to see the

interdependencies and trade-offs between choices made in these systems. They have been mainly

designed to assess cost efficient mitigation pathways under remaining climate impacts.

12. The economic components of IAM can take multiple directions from relatively simple models (e.g.

Ramsey growth models) relying on intertemporal maximisation of representative agent to more complex

CGE models. Typically, CGE models rely on shocks (such as natural disasters or policy changes) not on

pathways or gradual effects of climate and compare equilibrium outcomes with or without such shocks.

Computable general equilibrium (CGE) models, which are large multi-sectoral and multi-regional models,

comprising (representative) households, firms in different sectors, and governments. Different regions are

connected through trade and capital flows. The granular representation of sectors and regions enables

CGE modellers to capture important international trade effects and repercussions across different sectors

of the economy. CGE models, however, rely on the assumption of perfect markets and are typically not

able to explain non-equilibrium transition path towards outcomes and distributional impacts.

13. The climate module of an IAM describes climate impacts, including regional sea level rise and

temperature increases, which will be translated into damages through a damage function that describes

the effect of these climate variables on the economy. The form of the damage function is subject to

scholarly debate, as it is impossible to say a priori what the exact damages are, or whether it is even

meaningful to capture all the economic damages through a scalar function (Pindyck, 2017[18]). Often

stylized, highly aggregated damage functions are used to link changes in global mean temperature to

output losses, so impact channels generally remain unknown (but see e.g. PAGE, (Stern, 2006[17])). Recent

IAMs tend to use estimated parameters from the econometric literature, referenced above, to specify their

damage functions (Moore and Diaz, 2015[19]). The problem of estimating out-of-sample changes thus

becomes a crucial issue here. The inclusion of non-economic damages (such as non-use values of

ecosystems) significantly increases the estimated total damage (increase the social cost of carbon by

about 500% in analysis by (Bastien-Olvera and Moore, 2020[20])), similarly if the substitution between

natural and other type of capitals is not perfect the costs increase (Sterner and Persson, 2008[21]; Drupp,

2018[22]). The possibility of catastrophic damages or uncertain tipping points can also considerably affect

the total costs (increase the social cost of carbon by about 60%-300%; (Lontzek et al., 2015[23]; Cai et al.,

2015[24])).

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14. The structure of integrated assessment models makes it extremely difficult to use standard

econometric techniques to assess their validity, which implies that the arguments about most of the central

parameters remain theoretical. Even the most detailed models cannot capture all the channels and

mechanisms, because of the high level of aggregation. Another common issue to most IAMs, is their coarse

temporal resolution (typically time steps of several years), which makes it difficult to capture the transitory

dynamics following extreme weather events.

15. In addition, due to the relatively long time horizon, results from IAMs tend to be very sensitive to

the choice of the discount rate, e.g. when calculating the so-called social cost of carbon, used in cost-

benefit analyses and policy appraisal (Smith and Braathen, 2015[25]). For example, Diaz and Keller

(2016[26]) includes the disintegration of the West Antarctic Ice Sheet due to climate change in a DICE model

and find that it does not change the costs significantly, in part because the additional costs are in the far

future. (Do note, that this is in the relatively simplistic and aggregated world of the DICE model.) The losses

to individual countries, especially Small Island Developing States (SIDS) could be enormous (UNEP,

2014[27]). The result of Diaz and Keller (2016[26]) complements and contrasts the Lontzek et al (2015[23])

results referenced in the paragraph above, who emphasise the uncertain and unknown nature and timing

of tipping points, which includes a wider range of damages than the narrow focus of Diaz and Keller

(2016[26]). Another difference is Lontzek et al (2015[23]) stochastically estimate the effects and find that

uncertain future outcomes are effectively discounted at a lower rate than deterministic outcomes.2

2.3 Bottom-up modelling approaches

16. Bottom-up models are motivated by the fact that the impacts of climate change are determined by

the socio-economic context as much as by climate conditions. In general, the methods are more data-

intensive, but they can uncover heterogeneities and mechanisms behind the aggregate impacts. For

example, instead of using single agents to represent whole sectors or industries, it is possible to include

multiple (possibly heterogeneous) agents within sectors, who can interact with each other and with the

environment in an agent-based model. Each agent has its own decision problems, and adapts to the

environment. Agents can be firms, households or individual consumers. The dynamics of the socio-

economic system arises from the interplay of the different agents.

17. Agent based models are complex tool with lots of parameters, which means that they are very

data intensive, and there are lots of degrees of freedom with regards to agents’ behavioural rules. For this

reason most models are relatively narrow and focus on a single issue in a single region (Hailegiorgis,

Crooks and Cioffi-Revilla, 2018[28]) or provide stylised simulation of a single issue (Haer et al., 2020[29];

Otto et al., 2017[30]). Another issue is that different assumptions can lead to similar emergent dynamics at

the macro-level. Since stylised facts on the macro-level are the mostly the ones that can be compared with

empirical observations(e.g. growth dynamics, recovery dynamics), it is difficult to verify the behavioural

rules of the individual agents.

18. The flexibility of agent-based models allows modellers to use data disaggregated at different

levels and compare how simulated agent interactions at the micro-level give rise to macro-level outcomes.

Partial equilibrium results from the econometric literature can be included individually (instead of a single

damage function of IAMs). Technological change also has a potential to be better modelled, because it

explicitly takes account of non-linearities involved in the innovation process (Farmer et al., 2015[31]). Agent-

based models can include not perfectly rational (boundedly rational) agents, but also learning of the agents

over time. Because of the complexity, the models could lead to multiple equilibria (or no equilibria at all),

which may provide a useful starting point to examine why certain equilibria arise.

2 Specifically, Lontzek et al (2015[23]) find that the additional carbon tax to delay uncertain outcomes grows at a slower

rate than the baseline carbon tax for deterministic outcomes (the present and future are considered to be closer).

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19. An alternative approach is to use a simpler model, which does not have such large data

requirements, and use household surveys to constrain the modelling with data and empirical results. Once

the effects of climate are estimated on household level, it is possible to simulate the micro-level

consequences of climate change using the physical impact scenarios. This approach can also model

multiple channels, and by using reweighing and resampling techniques it can account for socio-economic

uncertainties and provide statistical estimates of various climate change effects (Hallegatte and

Rozenberg, 2017[32]). The use of household surveys may better represent household sources of income,

especially for the poorest households, thus the method is suited to elicit distributional impacts. The main

weakness is that there are no interactions across sectors in a consistent framework (for instance, the

swings in agricultural output has no impact on the non-agricultural sector or ability to invest in other

activities). Relatedly, there is no underlying growth dynamics of the models, which might exclude some

important effects of climate. It may also underestimate some of the effects because compound effects of

multiple, interacting climate impacts are not modelled (Hallegatte, Rentschler and Rozenberg, 2020[10]).

20. Another tool, which does not need a theoretical grounding in economics, is a risk modelling

framework (e.g. CLIMADA, (Bresch and Aznar-Siguan, 2020[33])). This methodology tends to focus on a

single region, population or sector. Researchers first assess the most relevant hazards and analyse the

effects of historical events in the local area. Next, they use physical models to look at the possible impacts

of climate change and assess uncertainty by simulating multiple models. The method essentially matches

high spatial and temporal resolution biophysical models with high-resolution data on assets. Then the direct

impacts on assets can be estimated. An advantage of this approach is that relies on high resolution data,

thus can assess the impacts with accuracy. A disadvantage is that it is event-based in the sense that it

tries to model the direct impacts of individual events in high detail, but it cannot capture equilibrium and

indirect effects. Another drawback is while the method can assess the impacts on assets, the ultimate aim

of policies is often welfare, and it is not exactly clear how to match assets to welfare.

3. Other methodologies

3.1 Place-based approaches

21. Some impacts are difficult to elicit with aggregate data and require knowledge of the local socio-

economic and cultural contexts. Place-based approaches aim to assess these impacts, with researchers

often conducting fieldwork in the affected area. The data collection typically begins with interviews,

surveys, or focus group discussions. While surveys might be the cheapest and most comparable across

people, interviews and focus group discussion could avoid some of the data entry error that comes from

misunderstanding survey questions or instructions. There is trade-off between comparability and better

quality data through more details. All data collection methods need to be carefully designed and the sample

should reflect the population.

22. Participatory methods are also available to provide more detail and help to assess the data

gathered, such as role-playing games, brainstorming and conceptual maps (Voinov et al., 2018[34]). These

dcan access values unavailable to quantitative approaches. Another advantage of these methods, is that

they can provide access to people who are less accessible with traditional quantitative data (e.g.

indigenous people).

23. After collection, the data are coded and analysed qualitatively. This, in practice, means that

interview or survey responses are recorded on a computer and the responses are labelled according to

themes or keywords, to help abstraction from the individual responses. With qualitative analysis,

researchers look for patterns and connections between the codes. This could be take multiple directions

depending the aim of the researchers. For example how do themes structured (thematic analysis; (Nursey-

Bray et al., 2019[35])), relatedly how do concepts connect (content analysis; (Thomas, Moore and Edwards,

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2018[36])), or how discourses form and change (discourse analysis; (Hissen, Conway and Goulden,

2017[37])).

24. While place-based approaches are often associated with qualitative methods, quantitative

analyses are also often carried out. For example, answers to closed-ended questions (e.g. multiple choice

or yes/no questions) could also be analysed with descriptive quantitative methods such as chi-square tests

or Pearson’s correlation coefficients (Thomas, Moore and Edwards, 2018[36]). Content analysis could also

be carried out quantitatively with the methods of text mining or natural language processing (Kirilenko and

Stepchenkova, 2012[38]), though the data demands are typically higher than what is usually available by

place-based approaches. Another quantitative complement to the analysis, is usually some form of

geographical information system (GIS) analysis (Karlsson, van Oort and Romstad, 2015[39]). This makes

the approach a bit more tangible, and helps complement the responses of different respondents (for

example where a certain impact hit differently according to GIS).

25. In some cases, however, it is not always clear how can place-based approaches be

accommodated to the decision making process, mainly because they are locally focused. They also tend

to be atheoretical, thus they have difficulty in establishing causal relationships. Often they can inform and

add depth to quantitative studies and call attention to possible misperceptions or limitations (see footnote

3 on p. 9). There have been creative attempts to combine quantitative and qualitative approaches in a

place-based setting, for example, farmers were asked to participate in a board game-like role playing and

their attitudes and behaviour served as basis for the behavioural rules of an agent-based model (Voinov

et al., 2018[34]). Alternatively, quantitative approaches, like cost-benefit analyses should be considered only

one factor of the decision-making process, and not the final verdict (Chichilinisky, 1997[40]).

3.2 Ecosystems

26. Climate change puts several ecosystems at risk, which also affects socio-economic well-being

(van der Geest et al., 2019[41]). In particular, the IPCC Special Report on Global Warming of 1.5°C (2018[42])

identified significant avoided damages to ecosystems, when the global mean temperature rise was 1.5°C

or less, instead of 2°C. However, how changes in ecosystem respond to climate change and how such

changes subsequently affect socio-economic well-being is not always clear. The natural world responds in

dynamic and unpredictable ways, and the ecological effects, such as species redistribution, are not fully

understood or completely predictable. Moreover ecological changes affect the climate in complex ways,

for example, forest dieback potentially amplifies warming through carbon cycle feedbacks (Pecl et al.,

2017[43]).

27. In general, valuing a complex thing such as ecosystem services is not an easy task. Ecosystems

provide a variety of services, some of which are not traded on the market, thus not captured by national

accounts such as GDP. While many argue against putting a monetary value on ecosystems on principle

(Costanza et al., 2017[44]; Des Jardins, 2013[45]), the needs of policy makers to conduct cost-benefit

analyses of environmental policies prompted economist to devise methods of such valuation (Bateman

et al., 2002[46]; Perman et al., 2011[47]). Broadly speaking there are two kinds of empirical methods:

revealed preference methods and stated preference methods.

28. Revealed preference methods estimate the value of ecosystems by looking at how consumers

have chosen in actual decision situations. First, hedonic pricing techniques look at prices of goods that are

traded on the market (typically houses) and estimates how proximity to a local environmental amenity

affects the prices. It is quite data-intensive, because it needs to control for all variables that might confound

the effect of ecosystems. Since the housing market is assumed to be efficient, the prices will be a good

indication of value. Another revealed preference method is the travel cost method. Travellers to a certain

environmental amenity are surveyed about their personal characteristics and the details of their trip. The

individual, who paid the costs of the trip, is perceived to be willing to pay at least that amount for the visit.

Cost of trip includes train tickets, fuel costs, admission costs, but also the opportunity cost of travelling.

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There are a few limitations to this method: if people take trips for multiple reasons it is difficult to disentangle

the effect of ecosystems from the other reasons. Another possible problem is that people who value the

ecosystem highly may choose to live nearby it, which will not be captured by this method. In general,

revealed preference methods have the big advantage of examining people’s actual behaviour, but they

only estimate use values of local amenities and will not capture the non-use values (e.g. the existence

value of certain species).

29. Stated preference methods examine preferences using people’s statements, instead of their

behaviour. For this reason, they are more flexible and can capture the non-use values missed by revealed

preference methods. The most widely used and accepted is contingent valuation. Contingent valuation

relies on surveys to assess the value of ecosystems, with relatively easy implementation and flexibility, as

the survey can explicitly ask for the value of different ecosystem services and different types of values (e.g.

existence value, use value etc.). A limitation of this approach is that respondents are not accustomed to

make valuation about non-market goods, thus they might not give their true preferences simply because it

is cognitively taxing to think about these issues. The results might also be contaminated by psychological

biases (e.g. framing bias, information bias, strategic bias, order of questions influences the valuation).

Another stated preference method is contingent choice experiment in which respondents choose between

multiple consumption bundles (e.g. low taxes and damaged ecosystem or high taxes and healthy

ecosystem). This is also a quite flexible method as it can evaluate the value of individual attributes of the

ecosystem. An advantage over contingent valuation is that it requires respondents think in terms of trade-

offs indirectly, when making a choice between bundles, thus the cognitive burden might be lessened.

Psychological biases are less likely to contaminate the results than in contingent valuation. A disadvantage

is that respondents facing too many questions might lose interest and resort to simple decision rules (e.g.

always chose the option on the left-hand side). With stated preference methods there is always a question

of validity: will people do as they say? Respondents may associate the surveys with issues independent

of the ecosystem valuation itself and might over- or understate their true preferences.

30. Some studies use also modelling, usually in an ecological-economic framework, which often omits

the climate link (but see e.g. (Speers et al., 2016[48])). These models resemble the integrated assessment

models introduced above, but they model the relationship of economy and the ecosystem (instead of

climate), and place a stronger emphasis on spatial aspects, sometimes using land use models (Bateman

et al., 2013[49]). Models usually account for only market-based values in the assessment (Drechsler et al.,

2007[50]) (Pejchar et al., 2018[51]). Because of their inherent complexity, it is often challenging to model all

aspects of ecosystems, and economy in a single model, thus in general the models focus tend to be

relatively narrow (e.g. (Speers et al., 2016[48])) or simplify one aspect of the analysis (Waldron et al.,

2020[52]).

31. Apart from the ethical objections mentioned above, some scholars criticised Willingness-to-Pay

framework as narrow, excluding lower income households, and suggested alternative measures such as,

willingness-to-commit, which also includes time and other non-monetary effort (Folkersen, 2018[53]).

Qualitative and semi-qualitative approaches also have been successfully applied to ecosystem valuation.

As mentioned above in section 3.1, qualitative methodology provides an overview, indicating trends, and

identifying trade-offs, which subsequently require an in depth analysis (Busch et al., 2012[54]).

32. Empirical models can be complemented by modelling predictions of climate science to see how

climate change will impact ecosystems and what that implies in monetary values. For example, Brander et

al (2012[55]) estimate the value of wetlands impacted by climate change in Europe, by using a function to

determine the monetary values across regions (using meta-analysis) and multiplying this with the

projections. Ding (2016[56]) uses a similar methodology to value the non-economic climate damage to

European forests, and in a hybrid approach they combine the economic and non-economic values to

provide a comprehensive number. Fezzi et al (2014[57]), on the other hand, focuses only on market values

to agriculture. They calculate the marginal value of ecosystem services using a regressions on farm data

and using these estimated marginal values to look at the effect of projected climate.

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3.3 Migration, Conflicts, Crime

33. For assessing migration outcomes, research tends to be empirical. While there are modelling

approaches, they generally are considered to be indications at best (Cattaneo et al., 2019[58]).

McGranahan, Balk and Anderson (2007[59]), for example, calculate population living in low lying coastal

areas and thus are at risk of flooding with sea level rise. But population-at-risk measures are only reliable

if migration is the only choice of the affected population (e.g. SIDS; (Fornalé, Guélat and Piguet, 2016[60])

(Thomas and Benjamin, 2019[61])). A big challenge for quantitative studies is to assess the importance of

climate change compared to other drivers. Empirically investigating the displacement of people due to

damage to housing stock caused by sudden-onset extreme weather events may be relatively

straightforward to do. But as one considers other types of potential climate-migration outcomes, the

number of confounding variables and methodological decisions grow in non-linear fashion. Qualitative

studies are more well-established and raise fewer methodological and data difficulties than quantitative

approaches. The problem here as before is the external validity of the results and possibility of

generalisation.

34. The psychological literature has found association between hot temperatures and aggressive

behaviour in individuals (see (Miles-Novelo and Anderson, 2019[62]) for a review), mainly using

psychological experiments to assess how changing the temperature affect aggressive behaviour and

perception of aggressiveness. The implications of these psychological findings have been examined in two

areas of aggressiveness: conflicts and crimes.

35. The climate-conflict relationship is an ambiguous one, partially because of methodological variety

(Scartozzi, 2020[63]). Around 60% of research so far has used statistical methods (Ide, 2017[64]), which face

similar issues as those mentioned in section 2.1, but the results are sensitive to methodological choices

(more so than GDP outcomes, for example). Even the same methodologies applied differently on the same

episodes of conflict lead to diverging conclusions (Mach et al., 2020[65]). This highlights the importance of

methodological nuances and the need to synthesise. A growing number of studies use both qualitative and

statistical methods, to complement each other. Statistical methods can show associations, and local

knowledge can explore the underlying mechanisms.3

36. The association of criminal behaviour and temperature is also subject to scholarly debate. Again

this is also mainly based on empirical research, mainly statistical and econometric methods (section 2.1),

and the issues raised there stand here. Especially the examination of the effect of weather or climate –

thus the feasibility of adaptation (Mares and Moffett, 2019[66]) (Ranson, 2014[67]), and the possibility of

omitted variables (Lynch, Long and Stretesky, 2020[68]; Lynch et al., 2020[69]).

3.4 Health

37. Climate change has significant and complex effects on human health and mortality. Research in

this field draws on several modelling methods, such as comparative risk assessment, mechanistic, or

microeconomic modelling. Comparative risk assessment approaches allow for standardization and

comparison of risk, whereas mechanistic and behavioural modelling can provide insights into systems

dynamics and questions of how individuals, households, and other economic agents may respond to

various changing circumstances. As with other modelling techniques it is difficult to be comprehensive, to

3 For example, both Witsenburg and Adano (2009[90]) and De Juan (2015[91]) find association between precipitation

and conflicts in their respective regions. However the qualitative parts leads to different interpretations of the

correlations: in case of Witsenburg and Adano (2009[90]) according to the locals, raids are easier when there is rain.

De Juan (2015[91]), on the other hand, argues that rains provide valuable resource (water) during droughts, thus they

increase competition and conflict.

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account for all the possible interrelations and mechanisms. Hess et al (2020[70]) recommends transparency

in modelling assumptions, without favouring one approach over another.

38. Individual channels can be estimated using statistical analyses: such as the effect of extreme

weather events on injuries (Bonafede et al., 2016[71]), effect of weather on infectious diseases (Linthicum

et al., 1999[72]; Luque Fernández et al., 2009[73]), or non-infectious diseases (Makin, 2011[74]). The

estimated responses can then be inform models that link health outcomes to climate through the exposure-

response functions (Kim, Kabir and Ara Jahan, 2014[75]).

39. Similarly, mortality studies examining the impact of both extreme heat and cold mostly rely on

econometric approaches, usually with panel methods (Deschênes and Greenstone, 2011[76]; Barreca et al.,

2016[14]), sometimes relying on assumptions about the economic behaviour to identify of the effect

(Carleton et al., 2020[4]). Though controversial, it is possible to translate the effects to monetary values

(OECD, 2012[77]). The value of statistical life can be estimated by using stated preference methods

introduced in section 3.2 (Balmford et al., 2019[78]). Contingent valuation and choice experiments can

evaluate the trade-off between risk of mortality and income. For example, a 1%-point increase in risk of

mortality corresponds to 100 USD/week higher income, then (assuming linearity) a 100% mortality rate

would correspond to 10000 USD/week, which is extended over the life cycle to calculate the value of

statistical life. Revealed preference methods can also be used by estimating the wage premium of high

risk occupations.

40. Another aspect that is often understated in policy discussions is climate’s effect on mental health.

This field mostly relies on surveys and interviews to assess mental illnesses (e.g. PTSD, anxiety) after an

extreme weather event (Rataj, Kunzweiler and Garthus-Niegel, 2016[79]). Few empirical studies focus on

the possible positive psychological consequences of extreme weather events, like feelings of compassion,

sense of meaning, post-traumatic growth, or even increased acceptance of climate change and

engagement with climate mitigation, which could help build psychosocial resilience (Hayes et al., 2018[80]).

3.5 Intangible losses

41. Not all impacts, however, are quantifiable or even comparable across cultures. These losses can

include cultural symbols, places that people identify with or even ways of life. Values and losses are shaped

by social contexts and by peoples’ experiences and daily practice. Knowledge of loss therefore requires

accounting for the inherently subjective nature of values, and how these values vary across human

experiences.

42. Since the causal relationship between climate and loss experience is mediated by personal

circumstance socio-economic context, and culture, the same loss will be interpreted differently by

individuals and culture groups. One’s actions may not interpreted as rational to a person in a different

situation or cultural context. By definition, such losses can only be assessed with qualitative analysis:

usually relying on placed-based approaches, they are consciously ‘slower’ than other methods, often

resisting policy makers’ requests for rapid risk analysis tool-kits (Tschakert et al., 2019[81]).

43. A recent literature review (Tschakert et al., 2019[81]) found that most studies used multiple methods

to elicit intangible losses: most used interviews (69%), surveys (40%), qualitative participatory methods

(38%) and focus group discussions (32%). Thus, the advantages and drawbacks of qualitative approaches

(referenced in section 3.1) also apply here. Increasingly there are calls for a comprehensive ‘science of

loss’ (Barnett et al., 2016[82]; Tschakert et al., 2017[83]), which would utilise multiple methods such as

psychology, economics, ethnographies, participatory methods for a comprehensive assessment of values

lost.

44. In theory, quantitative non-market valuation methods (mentioned in section 3.2) could provide

perspective on intangible losses, but generally the literature focuses on values within their context,

recognising that values and beliefs are not independent, but have a dynamic, sometimes bi-directional

11

relations (Persson, Sahlin and Wallin, 2015[84]). In certain settings, the individual losses can be weighed

against each other to examine trade-offs, and trade-offs can provide priorities for policies (Karlsson, van

Oort and Romstad, 2015[39]).

4. Questions for discussion

45. This selected review followed the structure of scholarly literature and division of disciplines, but it

is important to remember that the outcomes are strongly interrelated. For example, Carleton (2017[85])

shows that suicide rates are correlated with temperatures in India, but only once temperature significantly

damage agricultural yields and livelihoods. Damages and losses to ecosystems could entail also more

intangible, symbolic losses (Schirpke, Meisch and Tappeiner, 2018[86]).

46. It’s also worth noting that most methodologies introduced above aim to provide a guidance on the

average or expected losses and damages, but uncertainty is at the core at science of climate change, not

only climate sensitivity, but also about the biophysical impacts of change in climate (Knutti, Rugenstein

and Hegerl, 2017[87]). Uncertainty about impacts are found to decrease cooperation (Barrett and

Dannenberg, 2014[88]), despite warranting more stringent climate policies (Weitzman, 2009[89]). Indeed

some scholars even argue that focus of policy should be on more extreme, if less likely outcomes, rather

than on expected outcomes (Pindyck, 2017[18]).

47. The workshop on 13 January is organised in the context of the OECD project on losses and

damages from climate change. The aim of the project is to prepare a report that will explore climate impact

projections as well as different types and levels of relevant uncertainties and what they mean for

approaches to managing such impacts. On that basis, it will provide an overview of existing and emerging

approaches and the key scientific and socio-economic issues relevant to limiting and managing the risks

of losses and damages from climate change in the context of uncertainty. The role of policy, finance and

technology will be highlighted. This will be complemented by a discussion on how these different

approaches affect incentives for action at national, regional and international levels. The analysis will be

global in scale but throughout the circumstances of different geographic areas or groupings will be

highlighted with a particular focus on Least Developed Countries and Small Islands Developing States.

48. As mentioned in the Introduction, the purpose of this note is to introduce well-established

methodologies and provide a background to the discussions at the OECD workshop on “Assessment of

socio-economic losses and damages from climate change”. The objective is to discuss the relative

advantages and drawbacks of the approaches, with possible complementarities across methodologies.

Furthermore to have a dialog that includes the following questions amongst others:

What are the challenges of quantifying economically near-term (2030) and medium-term (2050)

losses and damages from climate change?

How can losses and damages that are not easily quantifiable be reflected in such assessments?

What are key potential methodological issues for aggregating different losses and damages, and

how could these be overcome?

How can economics and social science better assess the implications of thresholds and tipping

points?

12

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