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Intro:  
=== Catastrophe models ===
* [https://climada-python.readthedocs.io/en/stable/tutorial/1_main_climada.html CLIMADA]: An open-source risk assessment model developed by ETH Zurich.<ref>ETHZürich, “CLIMADA: Economics of Climate Adaptation,” <nowiki>https://wcr.ethz.ch/research/climada.html</nowiki>.</ref><ref>Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019,&#x20;2019.</ref> It uses probalistic modelling to estimate the expected economic damage as a measure of risk today. The model is well suited to provide an open and independent view on physical risk, in line with TCFD and underpins the Economics of Climate Adaptation (ECA) approach. As of today, it provides global coverage of major climate-related extreme-weather hazards (tropical cyclones, river flood, agro drought, and European winter storms) at high resolution (4km) for historic and some selected climate forcing scenarios (RCPs). Also see the [https://climate-adapt.eea.europa.eu/en/metadata/tools/climada introduction] by European Environment Agency.
* The [https://oasislmf.org/ Oasis Loss Modelling Framework ("LMF")]: an open source catastrophe modeling platform. It developed by a nonprofit organization funded and owned by the Insurance Industry to promote open access to models and to promote transparency. Additionally, some firms within the insurance industry are currently working with the Association for Cooperative Operations Research and Development (ACORD) to develop an industry standard for collecting and sharing exposure data.
* [https://www.eiopa.europa.eu/tools-and-data/open-source-tools-modelling-and-management-climate-change-risks_en An open-source tools] for the modelling and management of climate change risks is developed using CLIMADA by European Insurance and Occupational Pensions Authority (EIOPA)
 
=== Macroeconomic models ===
Table 1 lists the types of economic models that can be used to assess climate risks (NGFS, 2020<ref name=":0" />). For further guidance on selecting appropriate macroeconomic models, see the recommendations by the World Bank (page 26 of this report<ref>NGFS (2022). ''Physical climate risk assessment: Practical lessons for the development of climate scenarios with extreme weather events from emerging markets and developing economies''. Available at: https://www.ngfs.net/sites/default/files/media/2022/09/02/ngfs_physical_climate_risk_assessment.pdf </ref>).
{| class="wikitable"
|+Table 1 Types of Economic Models to Assess Climate Risks (reproduced from NGFS (2020)<ref name=":0">NGFS (2020). Guide to climate scenario analysis for central banks and supervisors. Available at: https://www.ngfs.net/sites/default/files/medias/documents/ngfs_guide_scenario_analysis_final.pdf</ref>)
!Lineage
!Model Type
!Description
!Example
|-
| rowspan="5" |Integrated climate-economy
models
|Cost-benefit IAMs
|Highly aggregated model that optimises welfare by determining emissions abatement  at each step
|[[wikipedia:DICE_model|DICE]], DSICE (Cai et al., 2012<ref>Cai, Y., Judd, K. L., & Lontzek, T. S. (2012) DSICE: a dynamic stochastic integrated model of climate and  economy. RDCEP Working Paper 12-02.</ref>, Barrage, 2020<ref>Barrage, L. (2020) Optimal Dynamic Carbon Taxes in a Climate–Economy  Model  with  Distortionary  Fiscal  Policy.  The  Review  of   Economic Studies, 87(1), 1-39.</ref>)
|-
|IAMs with detailed energy system and land use
|Detailed partial (PE) or general equilibrium (GE) models of the energy system and land use. General equilibrium types are linked to a simple growth model
|PE: GCAM, IMAGE GE: MESSAGE,  REMIND-MAgPIE, WITCH<ref>Model documentation available at www.iamcdocumentation.eu/index.php/IAMC_wiki</ref>
|-
|Computable General Equilibrium  (CGE) IAMs
|Multi-sector and region equilibrium models  based on optimising behaviour assumptions
|G-CUBED, AIM, MIT-EPPA, GTAP, GEM-E3
|-
|Macro-econometric IAMs
|Multi-sector and region model similar to CGE but econometrically calibrated
|E3ME, Mercure et al., 2018<ref>Mercure, J. F., Pollitt, H., Viñuales, J. E.,  Edwards, N. R., Holden, P. B., Chewpreecha, U., ...  & Knobloch, F. (2018) Macroeconomic impact of stranded fossil fuel assets. Nature  Climate Change, 8(7), 588-593.</ref>
|-
|Stock-flow consistent IAMs
|Highly aggregated model of climate change and the monetary economy that is stock-flow consistent
|Bovari et al., 2018<ref>Bovari, E., Giraud, G., & Mc Isaac, F. (2018) Coping with collapse: a stock-flow consistent monetary macrodynamics of global warming. Ecological Economics,  147, 383-398.</ref>
|-
| rowspan="3" |Other climate-economy models
|Input-output (IO) models
|Model that tracks interdependencies between different sectors to more fully assess impacts
|Ju and Chen, 2010<ref>Ju, L. & Chen, B. (2010) An input-output model to analyse sector linkages and CO<sub>2</sub> emissions. Procedia Environmental Sciences,  2, 1841-1845.</ref>
Koks and Thissen, 2016<ref>Koks, E. E., & Thissen, M. (2016) A multiregional impact assessment model for disaster analysis. Economic Systems Research, 28(4), 429-449.</ref>
|-
|Econometric studies
|Studies assessing impact of physical risks on macroeconomic variables (e.g. GDP, labour  productivity) based on historical relationships
|Kahn et al., 2019<ref>Kahn, M. E., Mohaddes, K., Ng, R. N., Pesaran, M. H., Raissi, M., & Yang, J. C. (2019) Long-term macroeconomic effects of climate change: A cross-country analysis (No. w26167). National Bureau of Economic Research.</ref>
Burke et al., 2015<ref>Burke, M., Hsiang, S. M., & Miguel, E. (2015) Global non-linear effect of temperature on economic production. Nature, 527(7577), 235-239.</ref>
Dell et al., 2012<ref>Dell, M., Jones, B. F., & Olken, B. A. (2012) Temperature shocks and economic growth: Evidence  from the last half century. American Economic Journal:  Macroeconomics, 4(3), 66-95.</ref>
|-
|Natural catastrophe models and micro-empirical studies
|Spatially granular models and studies assessing bottom-up damages from physical risks
|SEAGLASS (e.g. Hsiang et al., 2017<ref>Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., ... & Larsen, K. (2017) Estimating economic damage from climate change in the United States. Science, 356(6345), 1362-1369.</ref>)
|-
| rowspan="3" |Modified standard macroeconomic models
|DSGE models
|Dynamic equilibrium models based on optimal decision rules of rational economic agents
|Golosov et al., 2014<ref>Golosov, M., Hassler, J., Krusell, P., & Tsyvinski, A. (2014) Optimal  taxes on fossil fuel in general equilibrium. Econometrica, 82(1), 41-88.</ref>
Cantelmo et al. 2019<ref>Cantelmo, M. A., Melina, M. G., & Papageorgiou, M. C.  (2019) Macroeconomic Outcomes in Disaster-Prone Countries. International Monetary Fund, Working Paper No.19/217, October 2019.</ref>
|-
|E-DSGE
|Slightly modified standard frameworks (that  allow for negative production externalities)
|Heutel, 2012<ref>Heutel, G. (2012) How should environmental policy respond to business cycles? Optimal policy under persistent productivity shocks. Review of Economic Dynamics, 15(2), 244-264.</ref>
|-
|Large-scale econometric models
|Models with dynamic equations to represent demand and supply, coefficients based
on regressions
|NiGEM (e.g. Vermeulen et al., 2018<ref>Vermeulen, R., Schets, E., Lohuis, M., Kolbl, B., Jansen, D. J., & Heeringa, W. (2018) An energy transition risk stress test for the financial system of the Netherlands. DNB Occasional Studies, 160-7.</ref>)
|}
 
=== [https://www.ngfs.net/ngfs-scenarios-portal/use/ NGFS Scenarios Portal] ===
The [https://www.ngfs.net/ngfs-scenarios-portal/use/ NGFS Climate Scenarios Portal] provides a comprehensive suite of data and tools to analyze transition risks, physical risks, and their broader economic and financial impacts. This was created for central banks and financial supervisors but has been reported useful for private sectors. This resource is widely recognized and used by key regulatory bodies, including the Basel Committee on Banking Supervision<ref>Basel Committee on Banking Supervision (BCBS), 2022. Principles for the effective management and supervision of climate‐related financial risks.</ref> and European Central Bank<ref>Alogoskoufis, Spyros, et al. ''ECB economy-wide climate stress test: Methodology and results''. No. 281. ECB Occasional Paper, 2021.</ref>. Figure 1 below shows an overview of the scenario analysis process using the NGFS portal.[[File:scenario_analysis_process.png|thumb|center|700px|alt=Transmission Channels|Overview of the Scenario Analysis Process (Source: NGFS (2020)<ref name=":0" />)]]
 
== Others ==


* [https://wcr.ethz.ch/research/climada.html CLIMADA]: An open-source risk assessment model developed by ETH Zurich. It uses probalistic modelling to estimate the expected economic damage as a measure of risk today. The model is well suited to provide an open and independent view on physical risk, in line with TCFD and underpins the Economics of Climate Adaptation (ECA) approach. As of today, it provides global coverage of major climate-related extreme-weather hazards (tropical cyclones, river flood, agro drought, and European winter storms) at high resolution (4km) for historic and some selected climate forcing scenarios (RCPs).
* [https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fwww.c2es.org%2Fwp-content%2Fuploads%2F2022%2F03%2FC2ES-Physical-Risk-Toolkit_download.xlsx&wdOrigin=BROWSELINK '''Physical Risk Toolkit'''] by [https://www.c2es.org/ C2ES] This toolkit helps companies identify data and information providers for conducting physical climate risk assessments, focusing on US-based publicly available federal and academic resources. It includes some tools from private organizations but does not endorse them. The toolkit mainly serves as a starting point for further analysis.
* [https://www.eiopa.europa.eu/tools-and-data/open-source-tools-modelling-and-management-climate-change-risks_en An open-source tools] for the modelling and management of climate change risks is developed using CLIMADA by European Insurance and Occupational Pensions Authority (EIOPA)
* [https://climate-impact-explorer.climateanalytics.org/ Climate Impact Explorer] by Climate Analytics.  
* https://physrisk-ui-sandbox.apps.odh-cl1.apps.os-climate.org/
* https://github.com/os-climate/physrisk?tab=readme-ov-file
* https://oss4climate.pierrevf.consulting/ui/search
* [https://www.unepfi.org/themes/climate-change/the-climate-risk-dashboard/ UNEP FI Climate Risk Dashboard]
* [https://unepgrid.ch/en/resource/climatechange UNEP GRID-Geneva Data Platform]
* [https://wesr.unep.org/ UNEP World Environment Situation Room]  
* [https://cfs.climate.esa.int/index.html#/?globe=SI0.00I25.00I23840000I0.00I1732060523550I1II ESA Climate from Space]
*[https://global.infrastructureresilience.org/ Global Resilience Index (GRI) Risk Viewer] by Resilient Planet Data Hub
*[https://drmkc.jrc.ec.europa.eu/risk-data-hub#/ Disaster Risk Data] Hub by European Commission
 
== References ==

Latest revision as of 00:19, 20 November 2024

Catastrophe models

  • CLIMADA: An open-source risk assessment model developed by ETH Zurich.[1][2] It uses probalistic modelling to estimate the expected economic damage as a measure of risk today. The model is well suited to provide an open and independent view on physical risk, in line with TCFD and underpins the Economics of Climate Adaptation (ECA) approach. As of today, it provides global coverage of major climate-related extreme-weather hazards (tropical cyclones, river flood, agro drought, and European winter storms) at high resolution (4km) for historic and some selected climate forcing scenarios (RCPs). Also see the introduction by European Environment Agency.
  • The Oasis Loss Modelling Framework ("LMF"): an open source catastrophe modeling platform. It developed by a nonprofit organization funded and owned by the Insurance Industry to promote open access to models and to promote transparency. Additionally, some firms within the insurance industry are currently working with the Association for Cooperative Operations Research and Development (ACORD) to develop an industry standard for collecting and sharing exposure data.
  • An open-source tools for the modelling and management of climate change risks is developed using CLIMADA by European Insurance and Occupational Pensions Authority (EIOPA)

Macroeconomic models

Table 1 lists the types of economic models that can be used to assess climate risks (NGFS, 2020[3]). For further guidance on selecting appropriate macroeconomic models, see the recommendations by the World Bank (page 26 of this report[4]).

Table 1 Types of Economic Models to Assess Climate Risks (reproduced from NGFS (2020)[3])
Lineage Model Type Description Example
Integrated climate-economy

models

Cost-benefit IAMs Highly aggregated model that optimises welfare by determining emissions abatement  at each step DICE, DSICE (Cai et al., 2012[5], Barrage, 2020[6])
IAMs with detailed energy system and land use Detailed partial (PE) or general equilibrium (GE) models of the energy system and land use. General equilibrium types are linked to a simple growth model PE: GCAM, IMAGE GE: MESSAGE,  REMIND-MAgPIE, WITCH[7]
Computable General Equilibrium  (CGE) IAMs Multi-sector and region equilibrium models  based on optimising behaviour assumptions G-CUBED, AIM, MIT-EPPA, GTAP, GEM-E3
Macro-econometric IAMs Multi-sector and region model similar to CGE but econometrically calibrated E3ME, Mercure et al., 2018[8]
Stock-flow consistent IAMs Highly aggregated model of climate change and the monetary economy that is stock-flow consistent Bovari et al., 2018[9]
Other climate-economy models Input-output (IO) models Model that tracks interdependencies between different sectors to more fully assess impacts Ju and Chen, 2010[10]

Koks and Thissen, 2016[11]

Econometric studies Studies assessing impact of physical risks on macroeconomic variables (e.g. GDP, labour  productivity) based on historical relationships Kahn et al., 2019[12]

Burke et al., 2015[13] Dell et al., 2012[14]

Natural catastrophe models and micro-empirical studies Spatially granular models and studies assessing bottom-up damages from physical risks SEAGLASS (e.g. Hsiang et al., 2017[15])
Modified standard macroeconomic models DSGE models Dynamic equilibrium models based on optimal decision rules of rational economic agents Golosov et al., 2014[16]

Cantelmo et al. 2019[17]

E-DSGE Slightly modified standard frameworks (that  allow for negative production externalities) Heutel, 2012[18]
Large-scale econometric models Models with dynamic equations to represent demand and supply, coefficients based

on regressions

NiGEM (e.g. Vermeulen et al., 2018[19])

NGFS Scenarios Portal

The NGFS Climate Scenarios Portal provides a comprehensive suite of data and tools to analyze transition risks, physical risks, and their broader economic and financial impacts. This was created for central banks and financial supervisors but has been reported useful for private sectors. This resource is widely recognized and used by key regulatory bodies, including the Basel Committee on Banking Supervision[20] and European Central Bank[21]. Figure 1 below shows an overview of the scenario analysis process using the NGFS portal.

Transmission Channels
Overview of the Scenario Analysis Process (Source: NGFS (2020)[3])

Others

References

  1. ↑ ETHZürich, “CLIMADA: Economics of Climate Adaptation,” https://wcr.ethz.ch/research/climada.html.
  2. ↑ Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019,+2019.
  3. ↑ 3.0 3.1 3.2 NGFS (2020). Guide to climate scenario analysis for central banks and supervisors. Available at: https://www.ngfs.net/sites/default/files/medias/documents/ngfs_guide_scenario_analysis_final.pdf
  4. ↑ NGFS (2022). Physical climate risk assessment: Practical lessons for the development of climate scenarios with extreme weather events from emerging markets and developing economies. Available at: https://www.ngfs.net/sites/default/files/media/2022/09/02/ngfs_physical_climate_risk_assessment.pdf
  5. ↑ Cai, Y., Judd, K. L., & Lontzek, T. S. (2012) DSICE: a dynamic stochastic integrated model of climate and  economy. RDCEP Working Paper 12-02.
  6. ↑ Barrage, L. (2020) Optimal Dynamic Carbon Taxes in a Climate–Economy  Model  with  Distortionary  Fiscal  Policy.  The  Review  of   Economic Studies, 87(1), 1-39.
  7. ↑ Model documentation available at www.iamcdocumentation.eu/index.php/IAMC_wiki
  8. ↑ Mercure, J. F., Pollitt, H., Viñuales, J. E.,  Edwards, N. R., Holden, P. B., Chewpreecha, U., ...  & Knobloch, F. (2018) Macroeconomic impact of stranded fossil fuel assets. Nature  Climate Change, 8(7), 588-593.
  9. ↑ Bovari, E., Giraud, G., & Mc Isaac, F. (2018) Coping with collapse: a stock-flow consistent monetary macrodynamics of global warming. Ecological Economics,  147, 383-398.
  10. ↑ Ju, L. & Chen, B. (2010) An input-output model to analyse sector linkages and CO2 emissions. Procedia Environmental Sciences,  2, 1841-1845.
  11. ↑ Koks, E. E., & Thissen, M. (2016) A multiregional impact assessment model for disaster analysis. Economic Systems Research, 28(4), 429-449.
  12. ↑ Kahn, M. E., Mohaddes, K., Ng, R. N., Pesaran, M. H., Raissi, M., & Yang, J. C. (2019) Long-term macroeconomic effects of climate change: A cross-country analysis (No. w26167). National Bureau of Economic Research.
  13. ↑ Burke, M., Hsiang, S. M., & Miguel, E. (2015) Global non-linear effect of temperature on economic production. Nature, 527(7577), 235-239.
  14. ↑ Dell, M., Jones, B. F., & Olken, B. A. (2012) Temperature shocks and economic growth: Evidence  from the last half century. American Economic Journal:  Macroeconomics, 4(3), 66-95.
  15. ↑ Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., ... & Larsen, K. (2017) Estimating economic damage from climate change in the United States. Science, 356(6345), 1362-1369.
  16. ↑ Golosov, M., Hassler, J., Krusell, P., & Tsyvinski, A. (2014) Optimal  taxes on fossil fuel in general equilibrium. Econometrica, 82(1), 41-88.
  17. ↑ Cantelmo, M. A., Melina, M. G., & Papageorgiou, M. C.  (2019) Macroeconomic Outcomes in Disaster-Prone Countries. International Monetary Fund, Working Paper No.19/217, October 2019.
  18. ↑ Heutel, G. (2012) How should environmental policy respond to business cycles? Optimal policy under persistent productivity shocks. Review of Economic Dynamics, 15(2), 244-264.
  19. ↑ Vermeulen, R., Schets, E., Lohuis, M., Kolbl, B., Jansen, D. J., & Heeringa, W. (2018) An energy transition risk stress test for the financial system of the Netherlands. DNB Occasional Studies, 160-7.
  20. ↑ Basel Committee on Banking Supervision (BCBS), 2022. Principles for the effective management and supervision of climate‐related financial risks.
  21. ↑ Alogoskoufis, Spyros, et al. ECB economy-wide climate stress test: Methodology and results. No. 281. ECB Occasional Paper, 2021.
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