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What do economic damage estimates tell us about financing loss and damage?

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Loss and damages is a key topic in international negotiations, but computing the financial claims associated with climate risks is complex. We show that climate-economic impact functions can be used to estimate the size of loss and damages. A fund equalizing climate risks and responsibilities is estimated at 380 USD Billion/yr by 2025, rising over time. We discuss a research agenda that can advance the policy debate.
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What do economic damage estimates tell us about
nancing loss and damage?
Massimo Tavoni ( massimo.tavoni@eiee.org )
European Institute on Economics and the Environment
Pietro Andreoni
RFF-CMCC European Institute for the Economics and the Environment (EIEE) & Politecnico di Milano
https://orcid.org/0000-0003-2487-1671
Brief Communication
Keywords:
Posted Date: November 3rd, 2022
DOI: https://doi.org/10.21203/rs.3.rs-2230294/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. 
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What do economic damage estimates tell us about financing loss and damage?
Massimo Tavoni and Pietro Andreoni, Politecnico di Milano and RFF-CMCC European Institute
on Economics and the Environment
Loss and damages is a key topic in international negotiations, but computing the financial
claims associated with climate risks is complex. We show that climate-economic impact
functions can be used to estimate the size of loss and damages. A fund equalizing climate risks
and responsibilities is estimated at 380 USD Billion/yr by 2025, rising over time. We discuss a
research agenda that can advance the policy debate.
As climate risks rise and damages rise, discussions around loss and damages from climate change
have intensified1. The debate is expected to play an important role at the UNFCCC COP27
meeting. Developing countries are expected to demand support against climate impacts through
dedicated financial facilities, either through existing or new funds Rich nations have already
pledged to mobilize 100 billion dollars per year by 2020 to support the climate transition in more
vulnerable countries. However, the total amount has not yet been achieved, and new targets
must be established for the remaining decade.
Despite the relevance of this topic for international climate negotiations, quantifying the
damages from climate change and the related financial requests is a complex task. Weather
extremes are known to cause significant economic losses. Reports quantify the monetary
damages of weather events for developing countries to more than half a trillion dollars in the last
20 years2. The European environmental agency has estimated a similar figure for Europe alone in
the past 40 years (https://www.eea.europa.eu/highlights/economic-losses-from-weather-and).
Climate change is expected to increase the frequency and severity of weather and temperature
extremes. Still, claims of damage compensation require a reliable attribution of measured
impacts to climate change against natural weather variability and to take into account contextual
factors such as demography and economic development. Therefore, assigning responsibilities
and rights to compensation between countries requires a clear identification of observed losses
to climate change and methods to project future climate damages, accounting for the uncertainty
in observed and projected climate.
One way to advance the scientific basis for financial compensation for loss and damage is to look
at the literature that quantifies climate change's economic costs. Climate-economy models have
been used to estimate residual climate damages depending on mitigation action and adaptation
efforts; a paper3 estimates residual impacts in developing countries to be $290580 billion by
2030. Similar figures are found in another modeling study4. In recent years, empirical approaches
to estimating climate impacts have considerably increased the strength of knowledge about
climate economic impacts and their unequal regional distribution. This has led the IPCC’s most
recent assessment report (AR6, WGIII SPM) to state that the global economic benefit of limiting
warming to 2°C exceeds the cost of mitigation. More recent work has shown that this might be
the case also for more stringent climate targets such as 1.5°C5, though uncertainties remain. The
empirical literature has also shown that climate change economic damages are unequally
distributed and fall disproportionally on poorer, low-emitting countries -thus corroborating their
claims for compensation6.
Here we condense the scientific evidence and quantify the financial impacts of climate change
for this decade. We apply three economic damage functions spanning different methodologies,
including empirical analysis of the relation between economic growth and temperature (BHM)7
and temperature change (KW)8, using data at country and sub-country levels, respectively; and
the most-recent bottom-up evaluation of sectoral impacts from climate change
(https://www.coacch.eu/). We apply the damage functions to a model9 and project country-level
impacts over time, based on a reference scenario of socio-economic activities and emissions10.
See Methods for details.
Climate economic impacts are expected to rise over time as the climate warms. With a
temperature increase scheduled to rise to 1.4°C by the end of the decade, global climate damages
across the three impact functions would amount to roughly 700 billion by 2025 and 1.8 trillion
USD by 2030 (Figure 1 and Fig SI1). Two damage functions agree in terms of total impacts and
regional repartition. Another foresees significantly higher overall damages but also gains in high-
income countries; this results from the non-linear relation between temperature and economic
growth, with some high-income countries currently located below the economically optimal
temperature.
Figure 1. Climate economic impacts in 2025 for three damage functions (left: absolute values,
right: GDP losses relative to baseline). Countries are grouped into low/lower-middle/upper-
middle/high income according to the World Bank’s latest classification based on 2021 GNI per
capita data.
Figure 1 shows that in absolute terms, most climate economic impacts will occur in lower-middle
income countries. These are countries primarily located in Africa and South/South East Asia
whose economies are growing but which are still vulnerable and exposed to climate risks. In
relative terms, the low-income countries the 25 poorest countries in the world all located in
Africa except Afghanistan- affected the most; their overall economic losses are very small
compared to others given the small size of their economies. This emphasizes the need to
prioritize economic transfers to where these are more needed based on welfare enhancement
and poverty eradication.
The economic impacts can be put in relation to the historical responsibility for climate change
(Table 1). We use cumulative emissions as an indicator of responsibility given their strong and
linear relation to temperature increase11. Almost two-thirds of economic impacts are expected
to take place in lower-middle income countries, but these are responsible for 10% of cumulative
emissions from 1850 to 2025 (13% from 1990). This proportion is roughly reversed for high-
income countries. Upper-middle income countries face significant impacts but less than their
share of responsibility.
The financial contributions which equalize climate risks and responsibilities are also shown in
Table 1 (for country-level maps see SI Fig. 2 and 3). Upper-middle income and high-income
regions would need to jointly contribute to a loss&damage fund with roughly 350 and 800 billion
USD/yr by 2025 and 2030 respectively. Considering 1990 as the start date for responsibility
changes these figures marginally but shifts parts of the transfers from high to upper-middle
income regions. Low-income regions entitlements to the fund would be small and as already
discussed- would help reduce the significant economic losses which these regions face.
year
Region
classification
Share of
economic
impacts
Share of
cumulative CO2
emissions
Funding
contributions
(2020 Billions USD)
2025
Low income
2.4%
0.3% [0.4%]
-15 [-14]
Lower-middle
income
62.1%
10.7% [13.7%]
-365 [-343]
Upper-middle
income
23.7%
32.6% [41.7%]
63 [128]
High income
11.8%
56.4% [44.2%]
317 [230]
2030
Low income
2.9%
0.3% [0.4%]
-48 [-46]
Lower-middle
income
65.7%
11.4% [14.3%]
-989 [-936]
Upper-middle
income
21.7%
34.5% [43.2%]
233 [393]
High income
9.7%
53.8% [42.0%]
616 [450]
Table1: Climate impacts and responsibilities for the four regional groups in 2025 and 2030. Share
of impacts based on the average of the three impact functions. Cumulative CO2 emissions from
1850 (from 1990 in square parenthesis) for an SSP2 scenario. Source: Gutschow et. Al 202112. The
financial contribution of each region to the fund is computed as the difference between the shares
of cumulative emissions and of climate impacts multiplied by global economic damages (average
between the three impact functions with responsibility for CO2 since 1850, in square parenthesis
responsibility since 1990).
We have shown how the scientific literature on climate economic impacts can help quantify the
financial requests around loss and damages. We use a variety of different impacts functions to
capture the uncertainties surrounding these estimates. Although estimates differ across impact
function and definition of responsibility, a fair consideration of loss and damages leads to a fund
size well above the current pledge of 100 USD Billion/year. What also emerges is a non-negligible
funding contributions of upper-middle income countries, though high-income ones would be by
far the biggest fund contributors. It is also clear that low-income countries should be fully and
immediately compensated as the benefits are the largest and the transfers small and politically
feasible.
Methodologically, many open questions remain. Many climate risks remain unquantified in
economic terms13. Impact functions do not capture well the adaptive capacity to manage climate
risks, but also the expenditures in adaptation. The level of persistence of climate impacts on
economic activity is an empirically open issue14: here we have used different damage functions
spanning both level and growth relations between temperature and GDP, but other impact
functions exist, and uncertainties are significant even in our sample5. The loss and damage
funding equalizes monetary losses and responsibilities without taking into welfare
considerations. Climate impacts do not include damages to natural capital and on ecosystem
services, something which now can be done thanks to better measurements and models15. Non-
use and non-anthropogenic values of natural capital are also not included. Furthermore, research
on loss and damage should not only look at the damages of climate change but also consider
possible costs related to emission reductions, especially for regions with ambitious climate
targets and policies - though these are likely to be smaller than residual impact and will be driven
by the design of mitigation policies16. Finally, as the balance of economic growth and of emissions
change between regions over time, the framework for determining the fund contributors and
beneficiaries needs to adjust dynamically and need to account for future projections and their
uncertainties17.
This calls for novel research and methodologies to quantify in a robust and scientifically validated
manner the growing claims of countries and communities which are affected disproportionally
by climate change18. These should not be used to deter or delay mitigation and adaptation
strategies in all major emitting economies but to promote areas of agreement between
developed and developing countries19 through a transparent and accountable framework which
favors decarbonization and adaptation of slow onset and disruptive events20.
Methods
We simulate the economic costs of climate change using the RICE50+ model21,22, an open-source
climate-economy model. The model features calibrated regional marginal abatement cost
curves, country-level projections of population and economic output, as well as country-level
downscaling of temperature based on the CMIP5 model ensemble. The model allows for a flexible
regional disaggregation. For this exercise, we use a near country-level disaggregation,
representing 180 independent countries. The remaining countries, mostly small states lacking
reliable data on GDP or population are grouped in a Rest of the World aggregate region that
account for 0.4% of global GDP in 2021.
For each region, total output gross of mitigation costs and damages is given by a Cobb-Douglas
production function:
󰇛 󰇜  󰇛 󰇜 󰇛 󰇜 󰇛 󰇜
where  is the total factor productivity, is population and is capital, modelled as a
depreciating stock that can be replenished through investments.
Net output is given by gross output minus the costs of climate damages (since we consider a
baseline scenario there are no costs of emission reductions):
󰇛 󰇜 󰇛 󰇜 󰇛 󰇜
Damages are computed as a fraction of total output:
󰇛 󰇜 󰇛 󰇜
󰇛 󰇜
where represents the global average temperature increase with respect to pre-industrial
levels, downscaled to country level through the following equation, where the coefficients are
obtained through the CMIP5 database:
󰇛 󰇜 󰇛󰇜 󰇛󰇜 󰇛󰇜
For demographics, GDP and emissions, we use the shared-socio-economic pathways scenario
SSP2. This is a ‘middle-of-the-road’ scenario with projections of economic activity and
demography in line with historical trends. Emissions are assumed to rise from the current levels
of 37 GtCO2 (energy-related CO2) to 42 GtCO2 by 2030, resulting in a temperature increase by
the end of the decade of 1.4°C above pre-industrial. We tested the robustness of our results to
alternative emission pathways such as those consistent with the Paris agreement target of 2°C.
Results for 2025 and 2030 hold given the inertia in temperature change.
The three different impact functions used in our scenarios differ in the formulation of . The
COACCH impact function has been recently developed in the context of the COACCH project
(https://www.coacch.eu/wp-content/uploads/2018/03/D4.3_revMAR2022.pdf) to provide a bottom-
up representation of climate impacts based on a multi-model sectorial representation of
damages. The functional form for  assumes zero damages at the model starting year
(2015) and impacts growing quadratically as global average temperature increase:
󰇛  󰇜  󰇛󰇜
󰇛󰇜󰇛󰇛󰇜 󰇜 󰇛󰇜󰇛󰇛󰇜 󰇜
The form of the impact function implies that on aggregate climate change produces only negative
impacts for each country or region. It is a standard impact function in which temperature affects
the level of economic activity but uses the best available knowledge from a variety of bottom-up,
sectoral sources. The coefficients are derived from a disaggregation in 17 macro-regions, and
downscaled constant at the iso3 level.
BHM (Burke, Hsiang and Miguel7) and KW (Kalkuhl and Wenz8) impact functions are top-down
macro-economic damages based on empirical estimation of the effects of temperature variation
on GDP growth from historical panel data at the country (BHM) and sub-country (KW) level. Thus,
this class of impact functions assumes that climate change permanently affects economic activity,
whereas the previous one assumes economic growth can recover.
BHM estimates the relation between GDP growth with the temperature level, including a linear
and a quadratic term. From this specification, they find an “optimal average temperature”,
deviating from which produces quadratic losses in GDP. Therefore, countries that have a local
average temperature that is already hotter than the optimum will suffer damages from global
temperature increase due to climate change, but cold countries will gain from the global
temperature increase until they reach the optimal temperature. This specification assumes that
temperature increase permanently effects long term economic growth. The growth effect is
implemented in the model as:
󰇛 󰇜  󰇛󰇛 󰇜 󰇛 󰇜󰇜  󰇛󰇛 󰇜 󰇛 󰇜󰇜
The growth penalty effects through the following equation (see Gazzotti, 2022 for derivation):
󰇛  󰇜 󰇛  󰇜
󰇛 󰇜
KW follows a similar approach using a data panel with sub-country level regional disaggregation,
but disentangles weather and temperature effects: the former are identified by the variation of
average temperature across adjacent years, the latter as BHM by the local average
temperature level. We implement the damage function following their central specification as:
󰇛  󰇜 󰇛  󰇜 󰇛 󰇜 󰇛  󰇜
 󰇛 󰇜
󰇛 󰇜 󰇛  󰇜
 󰇛  󰇜
Where

Is the time resolution of the model, and the coefficients  refer to the
coefficient estimates for the lagged terms and are directly summed to the main coefficient
because the of 5 years time resolution of the model doesn’t allow a representation of yearly lags.
We consider the interaction term

but not the level effects
because the related
coefficient is found not to be statistically significant in their study.
Regions are classified into the four income brackets following the latest World Bank
classification1. We compute the brackets using ISO3 data from the World Bank database, and we
apply this disaggregation statically in the time of horizon of interest, i.e. we don’t consider that
projected economic growth might shift countries from an income bracket to the next in 2025 and
2030. The country mapping is shown in SI Figure 4.
References
1. Roberts, E. & Huq, S. Coming full circle: the history of loss and damage under the UNFCCC. Int. J. Glob.
Warm. 8, 141157 (2015).
2. Ahmed, K. & Tamucci, J. What is the financial cost of loss and damage from climate change? - ELCI.
https://elc-insight.org/what-is-the-financial-cost-of-loss-and-damage-from-climate-change/ (2022).
3. Markandya, A. & González-Eguino, M. Integrated Assessment for Identifying Climate Finance Needs
for Loss and Damage: A Critical Review. in Loss and Damage from Climate Change: Concepts,
Methods and Policy Options (eds. Mechler, R., Bouwer, L. M., Schinko, T., Surminski, S. & Linnerooth-
Bayer, J.) 343362 (Springer International Publishing, 2019). doi:10.1007/978-3-319-72026-5_14.
4. Baarsch et. al. Impacts of Low Aggregate INDCs Ambition: Research commissioned by Oxfam. Oxfam
Policy & Practice https://policy-practice.oxfam.org/resources/impacts-of-low-aggregate-indcs-
ambition-research-commissioned-by-oxfam-582427/.
1 https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-
2023#:~:text=The%20World%20Bank%20assigns%20the,the%20previous%20year%20%282021%29
5. Drouet, L., Bosetti, V. & Tavoni, M. Net economic benefits of well-below 2°C scenarios and associated
uncertainties. Oxf. Open Clim. Change 2, kgac003 (2022).
6. Diffenbaugh, N. S. & Burke, M. Global warming has increased global economic inequality. Proc. Natl.
Acad. Sci. 201816020 (2019) doi:10.1073/pnas.1816020116.
7. Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic
production. Nature 527, 235239 (2015).
8. Kalkuhl, M. & Wenz, L. The impact of climate conditions on economic production. Evidence from a
global panel of regions. J. Environ. Econ. Manag. 103, 102360 (2020).
9. Gazzotti, P. et al. Persistent inequality in economically optimal climate policies. Nat. Commun. 12,
3421 (2021).
10. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse
gas emissions implications: An overview. Glob. Environ. Change 42, 153168 (2017).
11. Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne.
Nature 458, 11631166 (2009).
12. Gütschow, J., Jeffery, M. L., Günther, A. & Meinshausen, M. Country-resolved combined
emission and socio-economic pathways based on the Representative Concentration Pathway (RCP)
and Shared Socio-Economic Pathway (SSP) scenarios. Earth Syst. Sci. Data 13, 10051040 (2021).
13. Rising, J., Tedesco, M., Piontek, F. & Stainforth, D. A. The missing risks of climate change. Nature
610, 643651 (2022).
14. Piontek, F. et al. Integrated perspective on translating biophysical to economic impacts of
climate change. Nat. Clim. Change 11, 563572 (2021).
15. Bastien-Olvera, B. A. & Moore, F. C. Use and non-use value of nature and the social cost of
carbon. Nat. Sustain. 18 (2020) doi:10.1038/s41893-020-00615-0.
16. Köberle, A. C. et al. The cost of mitigation revisited. Nat. Clim. Change 11, 10351045 (2021).
17. Skeie, R. B., Peters, G. P., Fuglestvedt, J. & Andrew, R. A future perspective of historical
contributions to climate change. Clim. Change 164, 24 (2021).
18. Dorkenoo, K., Scown, M. & Boyd, E. A critical review of disproportionality in loss and damage
from climate change. WIREs Clim. Change 13, e770 (2022).
19. Pill, M. Towards a funding mechanism for loss and damage from climate change impacts. Clim.
Risk Manag. 35, 100391 (2022).
20. Robinson, S., Khan, M., Roberts, J. T., Weikmans, R. & Ciplet, D. Financing loss and damage from
slow onset events in developing countries. Curr. Opin. Environ. Sustain. 50, 138148 (2021).
21. Gazzotti, P. et al. Persistent inequality in economically optimal climate policies. Nat. Commun.
12, 3421 (2021).
22. Gazzotti, P. RICE50+: DICE model at country and regional level. Socio-Environ. Syst. Model. 4,
1803818038 (2022).
Supplementary information
Figure SI 1. Climate economic impacts in 2030 for three damage functions (left: absolute values,
right: GDP losses relative to baseline). Countries are grouped into low/lower-middle/upper-
middle/high income according to the World Bank’s latest classification based on 2021 GNI per
capita data.
Figure SI2: Per capita country contributions to the loss&damage fund for 2025. Damage function:
KW.
Figure SI3: Per capita country contributions to the loss&damage fund for 2030. Damage function:
KW.
Figure SI4: Country classification by income group based on the latest World Bank rule.
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