Article Contents
REVIEW   Open Access     Cite

Assessing socioeconomic risks of climate change through integrated modelling

    Show all affliationsShow less
More Information
  • Corresponding authors: wei@bit.edu.cn (Y. W.); z.mi@ucl.ac.uk (Z. M.)
  • DownLoad: Full size image
    1. Incorporate key elements of integrated assessment modelling into the Intergovernmental Panel on Climate Change (IPCC)’s framework of climate change risks.

      Investigate the integrated assessment tools for climate risks, highlighting their modelling features, scientific foundations, limitations and future directions.

      Identify key modelling challenges: uncertainty, climate damage monetization, long-term economic modelling, high-resolution climate extremes, and adaptation.

      Outline pathways to enhance models by bridging integrated assessment models (IAMs) and empirical models.

  • Climate change poses significant socio-economic risks, necessitating integrated assessment modelling that bridges natural and socioeconomic systems to identify and manage climate risks. However, research on key components of integrated assessment frameworks remains fragmented across disciplines, lacking explicit integration with the theoretical framework of climate change risk. This hinders the practical application of integrated assessment approaches in climate risk management. In this narrative review, we synthesize key elements of integrated assessment modelling—climate-related scenarios, climate simulations, climate impacts and damages, and uncertainties—within the Intergovernmental Panel on Climate Change (IPCC)’s risk framework, defined by hazard, exposure, and vulnerability. We examine how these elements are represented in integrated assessment models (IAMs) and empirical approaches, highlighting their modelling features and scientific foundations. We identify critical challenges in current research, including (1) the treatment of uncertainty, (2) monetization of climate damages, (3) representation of long-term economic impacts, and (4) incorporation of high-resolution climate extremes and adaptation dynamics. We propose that addressing these gaps requires bridging IAMs and empirical methods to improve multi-model integration, economic characterization, and the understanding of climate–economy interactions across spatial and temporal scales.
  • 加载中
  • [1] Rezaei E.E. and Gohar L. (2023). Climate change impacts on crop yields. Nat. Rev. Earth Environ. 4:831−846. DOI:10.1038/s43017-023-00491-0

    View in Article CrossRef Google Scholar

    [2] Pryor S.C., Barthelmie R.J., Bukovsky M.S., et al. (2020). Climate change impacts on wind power generation. Nat. Rev. Earth Environ. 1:627−643. DOI:10.1038/s43017-020-0101-7

    View in Article CrossRef Google Scholar

    [3] Zhang Z., Hui H. and Song Y. (2025). Mitigating the vicious cycle between urban heatwaves and building energy systems in Guangdong–Hong Kong–Macao Greater Bay Area. Innov. Energy 2:100080. DOI:10.59717/j.xinn-energy.2025.100080

    View in Article CrossRef Google Scholar

    [4] Matthews T., Wilby R.L., Kala C.P., et al. (2025). Mortality impacts of the most extreme heat events. Nat. Rev. Earth Environ. 6:193−210. DOI:10.1038/s43017-024-00635-w

    View in Article CrossRef Google Scholar

    [5] Adshead D., Fuldauer L.I., Thacker S., et al. (2024). Climate threats to coastal infrastructure and sustainable development outcomes. Nat. Clim. Chang. 14:344−352. DOI:10.1038/s41558-024-01950-2

    View in Article CrossRef Google Scholar

    [6] Cheng Y., Li C., Xu Y., et al. (2024). Extreme impacts on electric power systems from non-catastrophic meteorological conditions. Innov. Energy 1:100008. DOI:10.59717/j.xinn-energy.2024.100008

    View in Article CrossRef Google Scholar

    [7] Burke M., Hsiang S.M. and Miguel E. (2015). Global non-linear effect of temperature on economic production. Nature 527:235−239. DOI:10.1038/nature15725

    View in Article CrossRef Google Scholar

    [8] Tran T.-N.-D., Tapas M.R., Do S.K., et al. (2024). Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina. J. Environ. Manage. 363:121375. DOI:10.1016/j.jenvman.2024.121375

    View in Article CrossRef Google Scholar

    [9] Chang J.J., Wei Y.M., Yuan X.C., et al. (2020). The Nonlinear Impacts of Global Warming on Regional Economic Production: An Empirical Analysis from China. Weather Clim. Soc. 12:759−769. DOI:10.1175/WCAS-D-20-0029.1

    View in Article CrossRef Google Scholar

    [10] Tran T.-N.-D. and Lakshmi V. (2024). Enhancing human resilience against climate change: Assessment of hydroclimatic extremes and sea level rise impacts on the Eastern Shore of Virginia, United States. Sci. Total Environ. 947:174289. DOI:10.1016/j.scitotenv.2024.174289

    View in Article CrossRef Google Scholar

    [11] Burke M., Davis W.M. and Diffenbaugh N.S. (2018). Large potential reduction in economic damages under UN mitigation targets. Nature 557:549−553. DOI:10.1038/s41586-018-0071-9

    View in Article CrossRef Google Scholar

    [12] Marshall S.R.O., Tran T.-N.-D., Arshad A., et al. (2025). SWAT and CMIP6-driven hydro-climate modeling of future flood risks and vegetation dynamics in the White Oak Bayou Watershed, United States. Earth Syst. Environ. DOI:10.1007/s41748-025-00621-2.

    View in Article Google Scholar

    [13] Wei Y.M., Han R., Liang Q.M., et al. (2020). Self-preservation strategy for approaching global warming targets in the post-Paris Agreement era. Nat. Commun. 11:1624. DOI:10.1038/s41467-020-15453-z

    View in Article CrossRef Google Scholar

    [14] Wei Y.M., Han R., Wang C., et al. (2018). An integrated assessment of INDCs under Shared Socioeconomic Pathways: an implementation of C3IAM. Nat. Hazards 92:585−618. DOI:10.1007/s11069-018-3297-9

    View in Article CrossRef Google Scholar

    [15] Wei Y.-M., Kang J.-N. and Chen W. (2022). Climate or Carbon Mitigation Engineering Management. Engineering 17:17−21. DOI:10.1016/j.eng.2021.09.008

    View in Article CrossRef Google Scholar

    [16] Intergovernmental Panel on Climate Change. (2023). Climate Change 2022: Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press).

    View in Article Google Scholar

    [17] Rising J., Tedesco M., Piontek F., et al. (2022). The missing risks of climate change. Nature 610:643−651. DOI:10.1038/s41586-022-05243-6

    View in Article CrossRef Google Scholar

    [18] Diaz D. and Moore F. (2017). Quantifying the economic risks of climate change. Nat. Clim. Chang. 7:774−782. DOI:10.1038/NCLIMATE3411

    View in Article CrossRef Google Scholar

    [19] Piontek F., Drouet L., Emmerling J., et al. (2021). Integrated perspective on translating biophysical to economic impacts of climate change. Nat. Clim. Chang. 11:563−572. DOI:10.1038/s41558-021-01065-y

    View in Article CrossRef Google Scholar

    [20] Gambhir A., Butnar I., Li P.H., et al. (2022). Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways. Nat. Clim. Chang. 12:88−96. DOI:10.1038/s41558-021-01236-x

    View in Article CrossRef Google Scholar

    [21] Drouet L., Bosetti V., Padoan S.A., et al. (2021). Net zero-emission pathways reduce the physical and economic risks of climate change. Nat. Clim. Chang. 11:1070−1076. DOI:10.1038/s41558-021-01218-z

    View in Article CrossRef Google Scholar

    [22] Tapas M.R., Tran T.-N.-D., Do S.K., et al. (2024). A methodological framework for assessing sea level rise impacts on nitrate loading in coastal agricultural watersheds using SWAT+: A case study of the Tar-Pamlico River basin, North Carolina, USA. Sci. Total Environ. 951:175523. DOI:10.1016/j.scitotenv.2024.175523

    View in Article CrossRef Google Scholar

    [23] Nguyen B.Q., Van Binh D., Tran T.-N.-D., et al. (2024). Response of streamflow and sediment variability to cascade dam development and climate change in the Sai Gon Dong Nai River basin. Clim. Dyn. 62:7997−8017. DOI:10.1007/s00382-024-07319-7

    View in Article CrossRef Google Scholar

    [24] Tran T.N.D., Nguyen B.Q., Grodzka-Łukaszewska M., et al. (2024). Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections. IEEE JSTAEORS 17:7516−7529. DOI:10.1109/JSTARS.2024.3380514

    View in Article CrossRef Google Scholar

    [25] Schwoerer T., Ives M.C., Knowlton J.G., et al. (2023). Climate policy must account for community-specific socio-economic, health, and biophysical conditions—evidence from coastal Alaska. Reg. Environ. Change 23:90. DOI:10.1007/s10113-023-02080-9

    View in Article CrossRef Google Scholar

    [26] Reinwald F., Thiel S., Kainz A., et al. (2024). Components of urban climate analyses for the development of planning recommendation maps. Urban Clim. 57:102090. DOI:10.1016/j.uclim.2024.102090

    View in Article CrossRef Google Scholar

    [27] Tran T.-N.-D., Nguyen B.Q., Grodzka-Łukaszewska M., et al. (2023). The role of reservoirs under the impacts of climate change on the Srepok River basin, Central Highlands of Vietnam. Front. Environ. Sci. 11:1304845. DOI:10.3389/fenvs.2023.1304845

    View in Article CrossRef Google Scholar

    [28] Intergovernmental Panel on Climate Change. (2014). Climate Change 2014 – Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects: Working Group II Contribution to the IPCC Fifth Assessment Report (Cambridge University Press).

    View in Article Google Scholar

    [29] Liu H., Mi Z., Wu M., et al. (2024). The Innovation Energy: An international journal of energyscience and engineering. Innov. Energy 1:100001. DOI:10.59717/j.xinn-energy.2024.100001

    View in Article CrossRef Google Scholar

    [30] Nordhaus W.D. (1993). Rolling the ‘DICE’: an optimal transition path for controlling greenhouse gases. Resour. Energy Econ. 15:27−50. DOI:10.1016/0928-7655(93)90017-O

    View in Article CrossRef Google Scholar

    [31] Tol R.S.J. and Fankhauser S. (1998). On the representation of impact in integrated assessment models of climate change. Environ. Model. Assess. 3:63−74. DOI:10.1023/a:1019050503531

    View in Article CrossRef Google Scholar

    [32] Wilson C., Guivarch C., Kriegler E., et al. (2021). Evaluating process-based integrated assessment models of climate change mitigation. Clim. Change 166:1−22. DOI:10.1007/s10584-021-03099-9

    View in Article CrossRef Google Scholar

    [33] Batten S. (2018). Climate change and the macro-economy: a critical review. Bank of England Working Papers :706. DOI:10.2139/ssrn.3104554.

    View in Article Google Scholar

    [34] Nordhaus W.D. (2017). Evolution of Assessments of the Economics of Global Warming Changes in the DICE model, 1992 – 2017. Natl. Bur. Econ. Res. Work. Pap. Ser. :23319. DOI:10.3386/w23319.

    View in Article Google Scholar

    [35] Weyant J. (2017). Some Contributions of Integrated Assessment Models of Global Climate. Change. Rev. Environ. Econ. Policy 11:115−137. DOI:10.1093/reep/rew018

    View in Article CrossRef Google Scholar

    [36] Bosetti V. (2021). Integrated Assessment Models for Climate Change. Oxford Res. Encycl. Econ. Fin. DOI:10.1093/acrefore/9780190625979.013.572.

    View in Article Google Scholar

    [37] Nordhaus W.D. (2017). Revisiting the social cost of carbon. PNAS 114:1518−1523. DOI:10.1073/pnas.1609244114

    View in Article CrossRef Google Scholar

    [38] Silva Herran D., Tachiiri K. and Matsumoto K.i. (2019). Global energy system transformations in mitigation scenarios considering climate uncertainties. Appl. Energy 243:119−131. DOI:10.1016/j.apenergy.2019.03.069

    View in Article CrossRef Google Scholar

    [39] Luderer G., Madeddu S., Merfort L., et al. (2022). Impact of declining renewable energy costs on electrification in low-emission scenarios (vol 7, pg 32, 2022). Nat. Energy 7:380−381. DOI:10.1038/s41560-022-01000-1

    View in Article CrossRef Google Scholar

    [40] Johns T.C., Durman C.F., Banks H.T., et al. (2006). The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Clim. 19:1327−1353. DOI:10.1175/JCLI3712.1

    View in Article CrossRef Google Scholar

    [41] Castruccio S., McInerney D.J., Stein M.L., et al. (2014). Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs. J. Clim. 27:1829−1844. DOI:10.1175/JCLI-D-13-00099.1

    View in Article CrossRef Google Scholar

    [42] van Vuuren D.P., Edmonds J., Kainuma M., et al. (2011). How well do integrated assessment models simulate climate change. Clim. Change 104:255−285. DOI:10.1007/s10584-009-9764-2

    View in Article CrossRef Google Scholar

    [43] Tebaldi C., Armbruster A., Engler H.P., et al. (2020). Emulating climate extreme indices. Environ. Res. Lett. 15:074006. DOI:10.1088/1748-9326/ab8332

    View in Article CrossRef Google Scholar

    [44] Meinshausen M., Raper S.C.B. and Wigley T.M.L. (2011). Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6–Part 1: Model description and calibration. Atmos. Chem. Phys. 11:1417−1456. DOI:10.5194/acp-11-1417-2011

    View in Article CrossRef Google Scholar

    [45] Millar R.J., Nicholls Z.R., Friedlingstein P., et al. (2017). A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmos. Chem. Phys. 17:7213−7228. DOI:10.5194/acp-17-7213-2017

    View in Article CrossRef Google Scholar

    [46] Yuan X.C., Zhang N., Wang W.Z., et al. (2021). Large-scale emulation of spatio-temporal variation in temperature under climate change. Environ. Res. Lett. 16:014041. DOI:10.1088/1748-9326/abd213

    View in Article CrossRef Google Scholar

    [47] Calvin K., Patel P., Clarke L., et al. (2019). GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci. Model Dev. 12:677–698. DOI:10.5194/gmd-12-677-2019.

    View in Article Google Scholar

    [48] Stehfest E., van Vuuren D., Bouwman L., et al. (2014). Integrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications. (Netherlands Environmental Assessment Agency (PBL)).

    View in Article Google Scholar

    [49] Manne A.S. and Richels R.G. (2005). MERGE: an integrated assessment model for global climate change. Energy Environ.:175–189.

    View in Article Google Scholar

    [50] Farmer J.D., Hepburn C., Mealy P., et al. (2015). A Third Wave in the Economics of Climate Change. Environ. Resour. Econ. 62:329−357. DOI:10.1007/s10640-015-9965-2

    View in Article CrossRef Google Scholar

    [51] Rising J.A., Taylor C., Ives M.C., et al. (2022). Challenges and innovations in the economic evaluation of the risks of climate change. Ecol. Econ. 197:107437. DOI:10.1016/j.ecolecon.2022.107437

    View in Article CrossRef Google Scholar

    [52] Hsiang S. (2016). Climate Econometrics. Annu. Rev. Resour. Econ. 8:43−75. DOI:10.1146/annurev-resource-100815-095343

    View in Article CrossRef Google Scholar

    [53] Castle J.L. and Hendry D.F. (2022). Econometrics for Modelling Climate Change. Oxford Res. Encycl. Econ. Fin. DOI:10.1093/acrefore/9780190625979.013.675.

    View in Article Google Scholar

    [54] Chang J.J., Mi Z.F. and Wei Y.M. (2023). Temperature and GDP: A review of climate econometrics analysis. Struct. Chang. Econ. Dyn. 66:383−392. DOI:10.1016/j.strueco.2023.05.009

    View in Article CrossRef Google Scholar

    [55] Fankhauser S. and Tol R.S.J. (2005). On climate change and economic growth. Resour. Energy Econ. 27:1−17. DOI:10.1016/j.reseneeco.2004.03.003

    View in Article CrossRef Google Scholar

    [56] Lecocq F. and Shalizi Z. (2007). How might climate change affect economic growth in developing countries? A review of the growth literature with a climate lens. World Bank Policy Res. Work. Pap. :4315.

    View in Article Google Scholar

    [57] Graff Zivin J. and Neidell M. (2009). Medical technology adoption, uncertainty, and irreversibilities: is a bird in the hand really worth more than in the bush. Health Econ. 19:142−153. DOI:10.1002/hec.1455

    View in Article CrossRef Google Scholar

    [58] Zhang P., Deschenes O., Meng K., et al. (2018). Temperature effects on productivity and factor reallocation: Evidence from a half million chinese manufacturing plants. J. Environ. Econ. Manage. 88:1−17. DOI:10.1016/j.jeem.2017.11.001

    View in Article CrossRef Google Scholar

    [59] Henseler M. and Schumacher I. (2019). The impact of weather on economic growth and its production factors. Clim. Change 154:417−433. DOI:10.1007/s10584-019-02441-6

    View in Article CrossRef Google Scholar

    [60] Letta M. and Tol R.S.J. (2019). Weather, Climate and Total Factor Productivity. Environ. Resour. Econ. 73:283−305. DOI:10.1007/s10640-018-0262-8

    View in Article CrossRef Google Scholar

    [61] Moore F.C. and Diaz D.B. (2015). Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Chang. 5:127−131. DOI:10.1038/nclimate2481

    View in Article CrossRef Google Scholar

    [62] Gambhir A., Ganguly G. and Mittal S. (2022). Climate change mitigation scenario databases should incorporate more non-IAM pathways. Joule 6:2663−2667. DOI:10.1016/j.joule.2022.11.007

    View in Article CrossRef Google Scholar

    [63] Drouet L., Bosetti V. and Tavoni M. (2015). Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC. Nat. Clim. Chang. 5:937−940. DOI:10.1038/NCLIMATE2721

    View in Article CrossRef Google Scholar

    [64] van Vuuren D.P., Kriegler E., O’Neill B.C., et al. (2014). A new scenario framework for climate change research: scenario matrix architecture. Clim. Change 122:373−386. DOI:10.1007/s10584-013-0906-1

    View in Article CrossRef Google Scholar

    [65] Burke M., Dykema J., Lobell D.B., et al. (2015). Incorporating Climate Uncertainty into Estimates of Climate Change Impacts. Rev. Econ. Stat. 97:461−471. DOI:10.1162/REST_a_00478

    View in Article CrossRef Google Scholar

    [66] Kunreuther H., Heal G., Allen M., et al. (2013). Risk management and climate change. Nat. Clim. Chang. 3:447−450. DOI:10.1038/NCLIMATE1740

    View in Article CrossRef Google Scholar

    [67] Frigg R., Bradley S., Du H., et al. (2014). Laplace’s demon and the adventures of his apprentices. Philos. Sci. 81:31−59. DOI:10.1086/674416

    View in Article CrossRef Google Scholar

    [68] Stainforth D.A., Allen M.R., Tredger E.R., et al. (2007). Confidence, uncertainty and decision-support relevance in climate predictions. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365:2145−2161. DOI:10.1098/rsta.2007.2074

    View in Article CrossRef Google Scholar

    [69] Daron J.D. and Stainforth D.A. (2013). On predicting climate under climate change. Environ. Res. Lett. 8:034021. DOI:10.1088/1748-9326/8/3/034021

    View in Article CrossRef Google Scholar

    [70] Hawkins E., Smith R.S., Gregory J.M., et al. (2016). Irreducible uncertainty in near-term climate projections. Clim. Dyn. 46:3807−3819. DOI:10.1007/s00382-015-2806-8

    View in Article CrossRef Google Scholar

    [71] Kikstra J.S., Waidelich P., Rising J., et al. (2021). The social cost of carbon dioxide under climate-economy feedbacks and temperature variability. Environ. Res. Lett. 16:094037. DOI:10.1088/1748-9326/ac1d0b

    View in Article CrossRef Google Scholar

    [72] Hornsey M.J., Harris E.A., Bain P.G., et al. (2016). Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Chang. 6:622−626. DOI:10.1038/NCLIMATE2943

    View in Article CrossRef Google Scholar

    [73] Yang P., Mi Z., Yao M., et al. (2021). The impact of climate risk valuation on the regional mitigation strategies. J. Clean. Prod. 313:127786. DOI:10.1016/j.jclepro.2021.127786

    View in Article CrossRef Google Scholar

    [74] Kousser T. and Tranter B. (2018). The influence of political leaders on climate change attitudes. Glob. Environ. Change 50:100−109. DOI:10.1016/j.gloenvcha.2018.03.005

    View in Article CrossRef Google Scholar

    [75] O’Neill B.C., Tebaldi C., van Vuuren D.P., et al. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9:3461−3482. DOI:10.5194/gmd-9-3461-2016

    View in Article CrossRef Google Scholar

    [76] Riahi K., van Vuuren D.P., Kriegler E., et al. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 42:153−168. DOI:10.1016/j.gloenvcha.2016.05.009

    View in Article CrossRef Google Scholar

    [77] Jaxa-Rozen M. and Trutnevyte E. (2021). Sources of uncertainty in long-term global scenarios of solar photovoltaic technology. Nat. Clim. Chang. 11:266−273. DOI:10.1038/s41558-021-00998-8

    View in Article CrossRef Google Scholar

    [78] Forster P.M., Maycock A.C., McKenna C.M., et al. (2020). Latest climate models confirm need for urgent mitigation. Nat. Clim. Chang. 10:7−10. DOI:10.1038/s41558-019-0660-0

    View in Article CrossRef Google Scholar

    [79] Nordhaus W. (2019). Climate Change: The Ultimate Challenge for Economics. Am. Econ. Rev. 109:1991−2014. DOI:10.1257/aer.109.6.1991

    View in Article CrossRef Google Scholar

    [80] Hänsel M.C., Drupp M.A., Johansson D.J.A., et al. (2020). Climate economics support for the UN climate targets. Nat. Clim. Chang. 10:781−789. DOI:10.1038/s41558-020-0833-x

    View in Article CrossRef Google Scholar

    [81] Burke M., Hsiang S.M. and Miguel E. (2016). Climate Economics. Science 352:292−293. DOI:10.1126/science.aad9634

    View in Article CrossRef Google Scholar

    [82] Calel R., Chapman S.C., Stainforth D.A., et al. (2020). Temperature variability implies greater economic damages from climate change. Nat. Commun. 11:5028. DOI:10.1038/s41467-020-18797-8

    View in Article CrossRef Google Scholar

    [83] O’Neill B.C., Carter T.R., Ebi K.L., et al. (2020). Achievements and needs for the climate change scenario framework. Nat. Clim. Chang. 10:1074−1084. DOI:10.1038/s41558-020-00952-0

    View in Article CrossRef Google Scholar

    [84] Sutton R.T. (2019). Climate Science Needs to Take Risk Assessment Much More Seriously. Bull. Am. Meteorol. Soc. 100:1637−1642. DOI:10.1175/BAMS-D-18-0280.1

    View in Article CrossRef Google Scholar

    [85] Moss R.H., Edmonds J.A., Hibbard K.A., et al. (2010). The next generation of scenarios for climate change research and assessment. Nature 463:747−756. DOI:10.1038/nature08823

    View in Article CrossRef Google Scholar

    [86] Lamarque J.F., Bond T.C., Eyring V., et al. (2011). Global and regional evolution of short-lived radiatively-active gases and aerosols in the Representative Concentration Pathways. Clim. Change 109:191−212. DOI:10.1007/s10584-011-0155-0

    View in Article CrossRef Google Scholar

    [87] Rosentrater L.D. (2010). Representing and using scenarios for responding to climate change. Wiley Interdiscip. Rev. Clim. Change 1:253−259. DOI:10.1002/wcc.32

    View in Article CrossRef Google Scholar

    [88] van Vuuren D.P., Edmonds J., Kainuma M., et al. (2011). The representative concentration pathways: an overview. Clim. Change 109:5−31. DOI:10.1007/s10584-011-0148-z

    View in Article CrossRef Google Scholar

    [89] Wu T.W., Lu Y.X., Fang Y.J., et al. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12:1573−1600. DOI:10.5194/gmd-12-1573-2019

    View in Article CrossRef Google Scholar

    [90] Hassan W.H. and Nile B.K. (2021). Climate change and predicting future temperature in Iraq using CanESM2 and HadCM3 modeling. Model. Earth Syst. Environ. 7:737−748. DOI:10.1007/s40808-020-01034-y

    View in Article CrossRef Google Scholar

    [91] Chapman C.C., Monselesan D.P., Risbey J.S., et al. (2022). A large-scale view of marine heatwaves revealed by archetype analysis. Nat. Commun. 13:7843. DOI:10.1038/s41467-022-35493-x

    View in Article CrossRef Google Scholar

    [92] Paltsev S. and Sokolov A. (2021). Scenarios with MIT Integrated Global Systems Model: Significant Global Warming Regardless of Different Approaches. World Sci. Encycl. Clim. Change Case Stud. Clim. Risk Action Oppor. 2:235−241. DOI:10.1007/s10584-009-9792-y

    View in Article CrossRef Google Scholar

    [93] Sokolov A.P., Schlosser C.A., Dutkiewicz S., et al. (2005). MIT integrated global system model (IGSM) version 2: model description and baseline evaluation. (MIT Joint Program on the Science and Policy of Global Change).

    View in Article Google Scholar

    [94] Smith C.J., Forster P.M., Allen M., et al. (2018). FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. Geosci. Model Dev. 11:2273–2297. DOI:10.5194/gmd-11-2273-2018.

    View in Article Google Scholar

    [95] Hartin C.A., Patel P., Schwarber A., et al. (2015). A simple object-oriented and open-source model for scientific and policy analyses of the global climate system–Hector v1.0. Geosci. Model Dev. 8:939–955. DOI:10.5194/gmd-8-939-2015.

    View in Article Google Scholar

    [96] Rogelj J., Popp A., Calvin K.V., et al. (2018). Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Chang. 8:325–332. DOI:10.1038/s41558-018-0091-3.

    View in Article Google Scholar

    [97] Lee J.Y., Marotzke J., Bala G., et al. (2023). Future Global Climate: Scenario-Based Projections and Near-Term Information. Masson-Delmotte V., Zhai P., Pirani A., et al. (eds). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press), pp:553–672. DOI:10.1017/9781009157896.006.

    View in Article Google Scholar

    [98] Brecha R.J., Ganti G., Gidden M.J., et al. (2022). Institutional decarbonization scenarios evaluated against the Paris Agreement 1.5 °C goal. Nat. Commun. 13:4304. DOI:10.1038/s41467-022-31734-1.

    View in Article Google Scholar

    [99] Kober T., Schiffer H.-W., Densing M., et al. (2020). Global energy perspectives to 2060–WEC's World Energy Scenarios 2019. Energy Strategy Rev. 31:100523. DOI:10.1016/j.esr.2020.100523

    View in Article CrossRef Google Scholar

    [100] Grubler A., Wilson C., Bento N., et al. (2018). A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies. Nat. Energy 3:515–527. DOI:10.1038/s41560-018-0172-6.

    View in Article Google Scholar

    [101] Ives M., Mercure J.-F., Way R., et al. (2021). A new perspective on decarbonising the global energy system. (Smith School of Enterprise and the Environment, University of Oxford), Report No. 21-04.

    View in Article Google Scholar

    [102] Zonooz M.R.F., Nopiah Z., Yusof A.M., et al. (2009). A review of MARKAL energy modeling. Eur. J. Sci. Res. 26:352−361.

    View in Article Google Scholar

    [103] Pfenninger S., Hawkes A. and Keirstead J. (2014). Energy systems modeling for twenty-first century energy challenges. Renew. Sustain. Energy Rev. 33:74−86. DOI:10.1016/j.rser.2014.02.003

    View in Article CrossRef Google Scholar

    [104] Loulou R. and Labriet M. (2008). ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure. Comput. Manag. Sci. 5:7−40. DOI:10.1007/s10287-007-0046-z

    View in Article CrossRef Google Scholar

    [105] Mantzos L. (2009). Overview of primes energy system model. (E3MLab of National Technical University of Athens).

    View in Article Google Scholar

    [106] Bosello F. and Cian E.D. (2014). Documentation on the development of damage functions and adaptation in the WITCH model. CMCC Res. Pap. :RP0228.

    View in Article Google Scholar

    [107] Gracceva F. and Zeniewski P. (2013). Exploring the uncertainty around potential shale gas development–A global energy system analysis based on TIAM (TIMES Integrated Assessment Model). Energy 57:443−457. DOI:10.1016/j.energy.2013.06.006

    View in Article CrossRef Google Scholar

    [108] Guo F., van Ruijven B., Zakeri B., et al. (2021). Global Energy Interconnection: A scenario analysis based on the MESSAGEix-GLOBIOM Model. (International Institute for Applied Systems Analysis).

    View in Article Google Scholar

    [109] Fujimori S., Hasegawa T. and Masui T. (2017). AIM/CGE V2.0: Basic Feature of the Model. Fujimori S., Kainuma M. and Masui T. (eds). Post-2020 Climate Action: Global and Asian Perspectives (Springer Singapore):281–302. DOI:10.1007/978-981-10-3869-3_13.

    View in Article Google Scholar

    [110] Hilaire J. and Bertram C. (2020). The REMIND-MAgPIE model and scenarios for transition risk analysis. (Potsdam Institute for Climate Impact Research).

    View in Article Google Scholar

    [111] Way R., Ives M.C., Mealy P., et al. (2022). Empirically grounded technology forecasts and the energy transition. Joule 6:2057−2082. DOI:10.1016/j.joule.2022.08.009

    View in Article CrossRef Google Scholar

    [112] Emmerling J., Drouet L., van der Wijst K.I., et al. (2019). The role of the discount rate for emission pathways and negative emissions. Environ. Res. Lett. 14:104008. DOI:10.1088/1748-9326/ab3cc9

    View in Article CrossRef Google Scholar

    [113] de Oliveira C.C., Angelkorte G., Rochedo P.R., et al. (2021). The role of biomaterials for the energy transition from the lens of a national integrated assessment model. Clim. Change 167:57. DOI:10.1007/s10584-021-03201-1

    View in Article CrossRef Google Scholar

    [114] Daioglou V., Rose S.K., Bauer N., et al. (2020). Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study. Clim. Change 163:1603−1620. DOI:10.1007/s10584-020-02799-y

    View in Article CrossRef Google Scholar

    [115] Moore F.C., Baldos U., Hertel T., et al. (2017). New science of climate change impacts on agriculture implies higher social cost of carbon. Nat. Commun. 8:1607. DOI:10.1038/s41467-017-01792-x

    View in Article CrossRef Google Scholar

    [116] Jägermeyr J., Müller C., Ruane A.C., et al. (2021). Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2:873−885. DOI:10.1038/s43016-021-00400-y

    View in Article CrossRef Google Scholar

    [117] Heinicke S., Frieler K.A., Jägermeyr J., et al. (2022). Global gridded crop models underestimate yield responses to droughts and heatwaves. Environ. Res. Lett. 17:044026. DOI:10.1088/1748-9326/ac592e

    View in Article CrossRef Google Scholar

    [118] Kahn M.E., Mohaddes K., Ng R.N., et al. (2021). Long-term macroeconomic effects of climate change: A cross-country analysis. Energy Econ. 104:105624. DOI:10.1016/j.eneco.2021.105624

    View in Article CrossRef Google Scholar

    [119] Rising J. and Devineni N. (2020). Crop switching reduces agricultural losses from climate change in the United States by half under RCP 8.5. Nat. Commun. 11:4991. DOI:10.1038/s41467-020-18725-w.

    View in Article Google Scholar

    [120] Rode A., Carleton T., Delgado M., et al. (2021). Estimating a social cost of carbon for global energy consumption. Nature 598:308−314. DOI:10.1038/s41586-021-03883-8

    View in Article CrossRef Google Scholar

    [121] Nordhaus W.D. (1992). An Optimal Transition Path for Controlling Greenhouse Gases. Science 258:1315−1319. DOI:10.1126/science.258.5086.1315

    View in Article CrossRef Google Scholar

    [122] Cai Y.Y. and Lontzek T.S. (2019). The Social Cost of Carbon with Economic and Climate Risks. J. Polit. Econ. 127:2684−2734. DOI:10.1086/701890

    View in Article CrossRef Google Scholar

    [123] Yang P., Yao Y.F., Mi Z., et al. (2018). Social cost of carbon under shared socioeconomic pathways. Glob. Environ. Change 53:225−232. DOI:10.1016/j.gloenvcha.2018.10.001

    View in Article CrossRef Google Scholar

    [124] Luderer G., Pehl M., Arvesen A., et al. (2019). Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies. Nat. Commun. 10:5229. DOI:10.1038/s41467-019-13067-8

    View in Article CrossRef Google Scholar

    [125] Ricke K., Drouet L., Caldeira K., et al. (2018). Country-level social cost of carbon. Nat. Clim. Chang. 8:895−900. DOI:10.1038/s41558-018-0282-y

    View in Article CrossRef Google Scholar

    [126] Creutzig F., Agoston P., Goldschmidt J.C., et al. (2017). The underestimated potential of solar energy to mitigate climate change. Nat. Energy 2:17140. DOI:10.1038/nenergy.2017.140

    View in Article CrossRef Google Scholar

    [127] Xiao M., Junne T., Haas J., et al. (2021). Plummeting costs of renewables - Are energy scenarios lagging. Energy Strategy Rev. 35:100636. DOI:10.1016/j.esr.2021.100636

    View in Article CrossRef Google Scholar

    [128] Hoekstra A., Steinbuch M. and Verbong G. (2017). Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation. Complexity 2017:1967645. DOI:10.1155/2017/1967645

    View in Article CrossRef Google Scholar

    [129] Shiraki H. and Sugiyama M. (2020). Back to the basic: toward improvement of technoeconomic representation in integrated assessment models. Clim. Change 162:13−24. DOI:10.1007/s10584-020-02731-4

    View in Article CrossRef Google Scholar

    [130] Victoria M., Haegel N., Peters I.M., et al. (2021). Solar photovoltaics is ready to power a sustainable future. Joule 5:1041−1056. DOI:10.1016/j.joule.2021.03.005

    View in Article CrossRef Google Scholar

    [131] Wilson C., Grubler A., Bauer N., et al. (2012). Future capacity growth of energy technologies: are scenarios consistent with historical evidence. Clim. Change 118:381−395. DOI:10.1007/s10584-012-0618-y

    View in Article CrossRef Google Scholar

    [132] Huntingford C., Williamson M.S. and Nijsse F.J.M.M. (2020). CMIP6 climate models imply high committed warming. Clim. Change 162:1515−1520. DOI:10.1007/s10584-020-02849-5

    View in Article CrossRef Google Scholar

    [133] Tebaldi C., Debeire K., Eyring V., et al. (2020). Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. Discuss. 2020:1−50. DOI:10.5194/esd-2020-68

    View in Article CrossRef Google Scholar

    [134] Herger N., Sanderson B.M. and Knutti R. (2015). Improved pattern scaling approaches for the use in climate impact studies. Geophys. Res. Lett. 42:3486−3494. DOI:10.1002/2015GL063569

    View in Article CrossRef Google Scholar

    [135] Masson-Delmotte V., Zhai P., Pirani A., et al. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press).

    View in Article Google Scholar

    [136] Pindyck R.S. (2013). Climate change policy: what do the models tell us. J. Econ. Lit. 51:860−872. DOI:10.1257/jel.51.3.860

    View in Article CrossRef Google Scholar

    [137] Schultes A., Piontek F., Rogelj J., et al. (2021). Economic damages from on-going climate change imply deeper near-term emission cuts. Environ. Res. Lett. 16:104053. DOI:10.1088/1748-9326/ac27ce

    View in Article CrossRef Google Scholar

    [138] Takakura J.Y., Takahashi K., Fujimori S., et al. (2019). Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Chang. 9:737−741. DOI:10.1038/s41558-019-0578-6

    View in Article CrossRef Google Scholar

    [139] Snyder A., Calvin K., Phillips M., et al. (2020). The domestic and international implications of future climate for US agriculture in GCAM. PLoS ONE 15:e0237918. DOI:10.1371/journal.pone.0237918

    View in Article CrossRef Google Scholar

    [140] Carleton T.A. and Hsiang S.M. (2016). Social and economic impacts of climate. Science 353:aad9837. DOI:10.1126/science.aad9837

    View in Article CrossRef Google Scholar

    [141] Dell M., Jones B.F. and Olken B.A. (2014). What do we learn from the weather. The new climate-economy literature. J. Econ. Lit. 52:740−798. DOI:10.1257/jel.52.3.740

    View in Article CrossRef Google Scholar

    [142] Kalkuhl M. and Wenz L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions. J. Environ. Econ. Manage. 103:102360. DOI:10.1016/j.jeem.2020.102360

    View in Article CrossRef Google Scholar

    [143] Pindyck R.S. (2017). The Use and Misuse of Models for Climate Policy. Rev. Environ. Econ. Policy 11:100−114. DOI:10.1093/reep/rew012

    View in Article CrossRef Google Scholar

    [144] Roson R. and Sartori M. (2016). Estimation of Climate Change Damage Functions for 140 Regions in the GTAP 9 Database. J. Glob. Econ. Anal. 1:78−115. DOI:10.21642/JGEA.010202AF

    View in Article CrossRef Google Scholar

    [145] Howard P.H. and Sterner T. (2025). Methodology Matters: A Careful Meta-Analysis of Climate Damages. Environ. Resour. Econ. DOI:10.1007/s10640-025-01016-7.

    View in Article Google Scholar

    [146] Islam M.M. (2022). Distributive justice in global climate finance – Recipients’ climate vulnerability and the allocation of climate funds. Glob. Environ. Change 73:102475. DOI:10.1016/j.gloenvcha.2022.102475

    View in Article CrossRef Google Scholar

    [147] Pendergrass A.G. and Hartmann D.L. (2014). The Atmospheric Energy Constraint on Global-Mean Precipitation Change. J. Clim. 27:757−768. DOI:10.1175/JCLI-D-13-00163.1

    View in Article CrossRef Google Scholar

    [148] Lesk C., Rowhani P. and Ramankutty N. (2016). Influence of extreme weather disasters on global crop production. Nature 529:84−87. DOI:10.1038/nature16467

    View in Article CrossRef Google Scholar

    [149] Forzieri G., Bianchi A., e Silva F.B., et al. (2018). Escalating impacts of climate extremes on critical infrastructures in Europe. Glob. Environ. Change 48:97−107. DOI:10.1016/j.gloenvcha.2017.11.007

    View in Article CrossRef Google Scholar

    [150] Byers E., Gidden M., Leclère D., et al. (2018). Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett. 13:055012. DOI:10.1088/1748-9326/aabf45

    View in Article CrossRef Google Scholar

    [151] DeFries R.S., Edenhofer O., Halliday A., et al. (2019). The missing economic risks in assessments of climate change impacts. (Grantham Research Institute on Climate Change and the Environment, The Earth Institute at Columbia University, Potsdam Institute for Climate Impact Research).

    View in Article Google Scholar

    [152] Carleton T. and Greenstone M. (2022). A guide to updating the US Government’s social cost of carbon. Rev. Environ. Econ. Policy 16:196−218. DOI:10.1086/720988

    View in Article CrossRef Google Scholar

    [153] Stern N., Stiglitz J., Karlsson K., et al. (2022). A Social Cost of Carbon Consistent with a Net-Zero Climate Goal. (Roosevelt Institute).

    View in Article Google Scholar

    [154] Sarofim M.C., Waldhoff S.T. and Anenberg S.C. (2021). A temperature binning approach for multi-sector climate impact analysis. Clim. Change 165:22. DOI:10.1007/s10584-021-03048-6

    View in Article CrossRef Google Scholar

    [155] Takakura J., Fujimori S., Hanasaki N., et al. (2021). Reproducing complex simulations of economic impacts of climate change with lower-cost emulators. Geosci. Model Dev. 14:3121−3140. DOI:10.5194/gmd-14-3121-2021

    View in Article CrossRef Google Scholar

    [156] Auffhammer M. and Mansur E.T. (2014). Measuring climatic impacts on energy consumption: A review of the empirical literature. Energy Econ. 46:522−530. DOI:10.1016/j.eneco.2014.04.017

    View in Article CrossRef Google Scholar

    [157] Davis L.W. and Gertler P.J. (2015). Contribution of air conditioning adoption to future energy use under global warming. PNAS 112:5962−5967. DOI:10.1073/pnas.1423558112

    View in Article CrossRef Google Scholar

    [158] Schlenker W. and Roberts M.J. (2009). Nonlinear temperature effects indicate severe damages to U. S. crop yields under climate change. PNAS 106:15594−15598. DOI:10.1073/pnas.0906865106

    View in Article CrossRef Google Scholar

    [159] Schlenker W., Michael Hanemann W. and Fisher A.C. (2005). Will US agriculture really benefit from global warming. Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 95:395−406. DOI:10.1257/0002828053828455

    View in Article CrossRef Google Scholar

    [160] Hsiang S.M. (2010). Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. PNAS 107:15367−15372. DOI:10.1073/pnas.1009510107

    View in Article CrossRef Google Scholar

    [161] Chen X.G. and Yang L. (2019). Temperature and industrial output: Firm-level evidence from China. J. Environ. Econ. Manage. 95:257−274. DOI:10.1016/j.jeem.2017.07.009

    View in Article CrossRef Google Scholar

    [162] Missirian A. and Schlenker W. (2017). Asylum applications respond to temperature fluctuations. Science 358:1610−1613. DOI:10.1126/science.aao0432

    View in Article CrossRef Google Scholar

    [163] Carleton T., Hsiang S.M. and Burke M. (2016). Conflict in a changing climate. Eur. Phys. J. Spec. Top. 225:489−511. DOI:10.1140/epjst/e2015-50100-5

    View in Article CrossRef Google Scholar

    [164] Dasgupta S., van Maanen N., Gosling S.N., et al. (2021). Effects of climate change on combined labour productivity and supply: an empirical, multi-model study. Lancet Planet. Health 5:e455−e465. DOI:10.1016/S2542-5196(21)00170-4

    View in Article CrossRef Google Scholar

    [165] Neidell M., Graff Zivin J. and Sheahan M. (2021). Temperature and work: Time allocated to work under varying climate and labor market conditions. PLoS ONE 16:e0254224. DOI:10.1371/journal.pone.0254224

    View in Article CrossRef Google Scholar

    [166] Yang J., Yin P., Zhou M., et al. (2021). Projecting heat-related excess mortality under climate change scenarios in China. Nat. Commun. 12:1039. DOI:10.1038/s41467-021-21305-1

    View in Article CrossRef Google Scholar

    [167] Graff Zivin J. and Neidell M. (2014). Temperature and the allocation of time: Implications for climate change. J. Labor Econ. 32:1−26. DOI:10.1086/671766

    View in Article CrossRef Google Scholar

    [168] Hsiang S., Kopp R., Jina A., et al. (2017). Estimating economic damage from climate change in the United States. Science 356:1362−1368. DOI:10.1126/science.aal4369

    View in Article CrossRef Google Scholar

    [169] Dell M., Jones B.F. and Olken B.A. (2012). Temperature Shocks and Economic Growth: Evidence from the Last Half Century. Am. Econ. J. Macroecon. 4:66−95. DOI:10.1257/mac.4.3.66

    View in Article CrossRef Google Scholar

    [170] Lemoine D. and Kapnick S. (2016). A top-down approach to projecting market impacts of climate change. Nat. Clim. Chang. 6:51−55. DOI:10.1038/NCLIMATE2759

    View in Article CrossRef Google Scholar

    [171] Newell R.G., Prest B.C. and Sexton S.E. (2021). The GDP-Temperature relationship: Implications for climate change damages. J. Environ. Econ. Manage. 108:102445. DOI:10.1016/j.jeem.2021.102445

    View in Article CrossRef Google Scholar

    [172] Burke M. and Tanutama V. (2019). Climatic constraints on aggregate economic output. Natl. Bur. Econ. Res. Work. Pap. :25779. DOI:10.3386/w25779.

    View in Article Google Scholar

    [173] Colacito R., Hoffmann B. and Phan T. (2019). Temperature and Growth: A Panel Analysis of the United States. J. Money Credit Bank. 51:313−368. DOI:10.1111/jmcb.12574

    View in Article CrossRef Google Scholar

    [174] Yuan X.C., Yang Z.M., Wei Y.M., et al. (2020). The Economic Impacts of Global Warming on Chinese Cities. Clim. Change Econ. 11:2050007. DOI:10.1142/S2010007820500074

    View in Article CrossRef Google Scholar

    [175] Stern N. (2013). The Structure of Economic Modeling of the Potential Impacts of Climate Change: Grafting Gross Underestimation of Risk onto Already Narrow Science Models. J. Econ. Lit. 51:838−859. DOI:10.1257/jel.51.3.838

    View in Article CrossRef Google Scholar

    [176] Hsiang S.M. and Jina A.S. (2014). The causal effect of environmental catastrophe on long-run economic growth: Evidence from 6,700 cyclones. Natl. Bur. Econ. Res. Work. Pap. :20352. DOI:10.3386/w20352.

    View in Article Google Scholar

    [177] Pretis F., Schwarz M., Tang K., et al. (2018). Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376:20160460. DOI:10.1098/rsta.2016.0460.

    View in Article Google Scholar

    [178] Dietz S., Rising J., Stoerk T., et al. (2021). Economic impacts of tipping points in the climate system. PNAS 118:e2103081118. DOI:10.1073/pnas.2103081118

    View in Article CrossRef Google Scholar

    [179] Cai Y., Judd K.L., Lenton T.M., et al. (2015). Environmental tipping points significantly affect the cost−benefit assessment of climate policies. PNAS 112:4606−4611. DOI:10.1073/pnas.1503890112

    View in Article CrossRef Google Scholar

    [180] Carleton T., Jina A., Delgado M., et al. (2022). Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits. Q. J. Econ. 137:2037−2105. DOI:10.1093/qje/qjac020

    View in Article CrossRef Google Scholar

    [181] Auffhammer M. (2018). Quantifying Economic Damages from Climate Change. J. Econ. Perspect. 32:33−52. DOI:10.1257/jep.32.4.33

    View in Article CrossRef Google Scholar

    [182] Burke M. and Emerick K. (2016). Adaptation to Climate Change: Evidence from US Agriculture. Am. Econ. J. Econ. Policy 8:106−140. DOI:10.1257/pol.20130025

    View in Article CrossRef Google Scholar

    [183] Chapagain D., Baarsch F., Schaeffer M., et al. (2020). Climate change adaptation costs in developing countries: insights from existing estimates. Clim. Dev. 12:934−942. DOI:10.1080/17565529.2020.1711698

    View in Article CrossRef Google Scholar

    [184] Malik A., Li M., Lenzen M., et al. (2022). Impacts of climate change and extreme weather on food supply chains cascade across sectors and regions in Australia. Nat. Food 3:631−643. DOI:10.1038/s43016-022-00570-3

    View in Article CrossRef Google Scholar

    [185] Lau C.K., Cai Y. and Gozgor G. (2023). How do weather risks in Canada and the United States affect global commodity prices? Implications for the decarbonisation process. Ann. Oper. Res. DOI:10.1007/s10479-023-05672-0.

    View in Article Google Scholar

  • Cite this article:

    Chang J., Mi Z., Yu B., et al. (2026). Assessing socioeconomic risks of climate change through integrated modelling. The Innovation Energy 3:100140. https://doi.org/10.59717/j.xinn-energy.2026.100140
    Chang J., Mi Z., Yu B., et al. (2026). Assessing socioeconomic risks of climate change through integrated modelling. The Innovation Energy 3:100140. https://doi.org/10.59717/j.xinn-energy.2026.100140

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Figures(4)     Tables(4)

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(2908) PDF downloads(1326)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint