气候经济复杂系统中的不确定性及其建模研究进展

doi:10.16381/j.cnki.issn1003-207x.2023.0591

摘要/Abstract

摘要：

对不确定性的处理是气候经济复杂系统建模的难点，也是诸多气候政策研究结论存在重大分歧的源头之一。归纳总结了气候经济复杂系统中自然气候、气候影响、社会经济、减缓与适应等模块中的不确定性并对其分类。在对不确定性的处理方法方面：对于风险型不确定性，随机动态规划正逐步替代经典的敏感性分析和蒙特卡洛模拟方法；对于模型模糊型和误用型不确定性，采用决策分析方法正成为新趋势。在不确定性的具体建模方面：（1）通常将简约的“碳-气候响应”集成到不确定性下的成本效益评估。（2）随机过程建模方法常用于模拟经济增长和技术进步的不确定性。（3）气候临界点等灾难性风险，以及气候变化的非经济损失陆续被纳入模型。（4）效用或福利的评价标准更注重对代际与代内公平，以及不确定性厌恶偏好的考量。（5）减排技术进步的内生化建模得到更多体现。（6）负排放技术、太阳地球工程技术可能成为未来不确定性建模研究的热点。模型求解困难仍是阻碍气候经济不确定性建模研究发展的瓶颈之一。今后的气候经济不确定性建模除了平衡机理相符性、数据可获性、计算可行性等方面以外，还需努力纳入不同层级决策者和群体的异质性及其博弈机制。

关键词: 气候变化, 气候经济系统, 不确定性, 决策分析

Abstract:

Dealing with uncertainty in climate-economic modeling is a challenging task and a major point of disagreement in many climate policy studies. Uncertainty exists in both the natural climate system and the socioeconomic system，as well as in their interactions，such as the socio-economic impacts of climate change and human adaptation. In addition，the limited nature of human knowledge（uncertainty）regarding the complex climate economic systems also contributes to uncertainty in modeling. The study of uncertainty in climate-economy complex systems has gained increasing attention since the development of the first fully meaningful climate-economy complex systems model by the Nobel Prize winner in economic sciences， William D Nordhaus，in the early 1990s. With the development of uncertainty decision theory and methodology，climate science， and computational technology， there have been many advances in the study of uncertainty in climate-economy complex systems. At the same time，there are still many challenges in the understanding of the mechanism of the system，model coupling，parameter calibration，solution algorithms and other aspects that need to be overcome. Climate-economy complex systems models generally consist of the climate system module，climate impact module，socio-economic system module，and mitigation and adaptation（climate policy）module，which are coupled into a closed-loop system. The key uncertainties are categorized and classified in these four key modules in the climate economy complex system， the research progress in handling uncertainties is summaried in system modeling，and the future research directions are explored， with a view to better support climate economy modeling and decision-making. In terms of methodologies to address uncertainty，the stochastic dynamic programming approach is gradually replacing the classical sensitivity analysis and Monte Carlo simulation methods for dealing with risk-based uncertainty in climate-economy models；and the decision analysis approach is emerging as a new trend for dealing with deep uncertainties （ambiguities and misspecification） in climate-economy models. In terms of specific modeling of uncertainty：（1） the simple “carbon-climate response” is often incorporated into cost-benefit assessments under uncertainty. （2） stochastic process modeling is commonly used to model uncertainty in economic growth and technological progress. （3） catastrophic risks such as climate tipping points，and the non-economic losses from climate change are gradually incorporated into models. （4） criteria for evaluating utility or social welfare are more focused on inter- and intra-generational equity，as well as the uncertainty averse preferences. （5） endogenous technological progress in emission reduction is more reflected in the model. （6） negative-emission technologies and solar geo-engineering may become hot spots in the modeling of climate-economy complex systems. Finally，responding to climate change is a cross-cutting scientific issue that requires the full cooperation of multidisciplinary experts in earth sciences，economic and management sciences，computational sciences，and other disciplines. The intrinsic characteristics of uncertainty in the climate economic system and the subjective conditions that are not yet fully recognized scientifically make the modeling of the climate-economy under uncertainty face many challenges. Model-solving remains one of the bottlenecks in the development of climate economic modeling under uncertainty. In addition to balancing model complexity，data availability and computational feasibility，future modeling of climate economic uncertainty needs to incorporate the heterogeneity and gaming mechanisms of different levels of decision makers and groups；the analytical framework for climate decision making should not be limited to cost-benefit analysis；and climate economic modeling should also incorporate the political-economic factors as well as the complex impacts of the different players.

Key words: climate change, climate-economy complex system, uncertainty, decision analysis

中图分类号:

F015

廖华, 郑国梁. 气候经济复杂系统中的不确定性及其建模研究进展[J]. 中国管理科学, 2025, 33(1): 273-286.

Hua Liao, Guoliang Zheng. Advances in Modeling Uncertainty of Climate-Economy Complex System[J]. Chinese Journal of Management Science, 2025, 33(1): 273-286.

图/表 2

参考文献 118

1	Stern N. The economics of climate change［J］. American Economic Review， 2008， 98（2）： 1-37.
2	魏一鸣，米志付，张皓. 气候政策建模研究综述：基于文献计量分析［J］. 地球科学进展， 2013，28（8）：930-938.
	Wei Y M， Mi Z F， Zhang H. Review on climate policy modeling： An analysis based on bibliometrics method［J］. Advances in Earth Science， 2013， 28（8）： 930-938.
3	Hansen L P， Sargent T J. Structured ambiguity and model misspecification［J］. Journal of Economic Theory， 2022，199： 105165.
4	Millner A， McDermott T K. Model confirmation in climate economics［J］. Proceedings of the National Academy of Sciences， 2016， 113： 8675-8680.
5	Drouet L， Bosetti V， Tavoni M. Selection of climate policies under the uncertainties in the fifth assessment report of the IPCC［J］. Nature Climate Change， 2015（5）： 937-940.
6	Stern N， Stiglitz J. The economics of immense risk， urgent action and radical change： Towards new approaches to the economics of climate change［J］. Journal of Economic Methodology， 2022，29（3）： 181-216.
7	Weyant J. Some contributions of integrated assessment models of global climate change［J］. Review of Environmental Economics and Policy， 2017， 11（1）： 115-137.
8	Farmer J D， Hepburn C， Mealy P， et al. A third wave in the economics of climate change［J］. Environmental and Resource Economics， 2015，62：329-357.
9	Nordhaus W D. A question of balance： Weighing the options on global warming policies ［M］. New Haven， CT， USA： Yale University Press，2008.
10	Deser C. Certain uncertainty： The role of internal climate variability in projections of regional climate change and risk management［J］. Earth’s Future， 2020（8）：e2020EF001854.
11	Fyfe J C， Kharin V V， Santer B D， et al. Significant impact of forcing uncertainty in a large ensemble of climate model simulations［J］. Proceedings of the National Academy of Sciences， 2021， 118：e2016549118.
12	朱欢欢，姜胜，江志红. 基于可靠性集合平均方法的全球1.5/2.0 ℃变暖下中国极端气候的未来预估［J］. 地球科学进展， 2022， 37（6）： 612-626.
	Zhu H H， Jiang S， Jiang Z H. Projection of climate extremes over China in response to 1.5/2.0 ℃ global warming based on the reliability ensemble averaging［J］.Advances in Earth Science， 2022， 37（6）：612-626.
13	Weitzman M L. Fat-tailed uncertainty in the economics of catastrophic climate change［J］. Review of Environmental Economics and Policy， 2011， 5（2）： 275-292.
14	Pindyck R S. The use and misuse of models for climate policy［J］. Review of Environmental Economics and Policy， 2020，11（1）： 100-114.
15	Pecl G T， Araújo M B， Bell JD， Blanchard J， Bonebrake TC， Chen I-C， et al. Biodiversity redistribution under climate change： Impacts on ecosystems and human well-being［J］. Science， 2017， 355： eaai9214.
16	Christensen P， Gillingham K， Nordhaus W. Uncertainty in forecasts of long-run economic growth［J］. Proceedings of the National Academy of Sciences， 2018，115：5409-5414.
17	Cheng Z， Li L， Liu J. Industrial structure， technical progress and carbon intensity in China’s provinces［J］. Renewable and Sustainable Energy Reviews， 2018， 81： 2935-2946.
18	Stern N. Public economics as if time matters： Climate change and the dynamics of policy［J］. Journal of Public Economics， 2018， 162： 4-17.
19	廖华，叶慧颖. 从气候科学到气候经济学：诺贝尔物理学奖获得者 Hasselmann 的交叉科学研究［J］. 气候变化研究进展， 2022， 18（5）： 644-652.
	Liao H， Ye H Y. From climate science to climate economics： Nobel laureate hasselmann’s interdisciplinary research［J］. Advances in Climate Change Research， 2022， 18（5）： 644-652.
20	Orlove B， Shwom R， Markowitz E， et al. Climate decision-making［J］. Annual Review of Environment and Resources， 2020， 45： 271-303.
21	Paltsev S， Capros P. Cost concepts for climate change mitigation［J］. Climate Change Economics， 2013（4）： 1340003.
22	潘家华.“地球工程”作为减缓气候变化手段的几个关键问题［J］. 中国人口·资源与环境， 2012，22（5）：22-26.
	Pan J H， Key issues related to geo-engineering as approaches to climate change mitigation［J］. China Population Resources and Environment，2012，22（5）：22-26.
23	Felgenhauer T， De Bruin KC. The optimal paths of climate change mitigation and adaptation under certainty and uncertainty［J］. International Journal of Global Warming， 2009（1）：66-88.
24	Xepapadeas A. Uncertainty and climate change： The IPCC approach v.s. decision theory［J］. Journal of Behavioral and Experimental Economics， 2024， 109：102188.
25	Knight F.Risk， uncertainty and profit［J］.Social Science Electronic Publishing， 1921（4）： 682-690.
26	Huang Z， Wei Y M， Wang K， et al. Energy economics and climate policy modeling［J］. Annals of Operations Research， 2017， 255： 1-7.
27	Gillingham K， Nordhaus W， Anthoff D， et al. Modeling uncertainty in integrated assessment of climate change： A multimodel comparison［J］. Journal of the Association of Environmental and Resource Economists， 2018（5）： 791-826.
28	龚瑶，严婷.技术冲击、碳排放与气候环境——基于DICE模型框架的模拟［J］.中国管理科学，2014，22（S1）：801-809.
	Gong Y， Yan T. Technology impact，carbon emission and climate environment based on economy and climate simulation：In the framework of DICE model［J］. Chinese Journal of Management Science， 2014，22（S1）： 801-809.
29	Cai Y， Judd K L. Stable and efficient computational methods for dynamic programming［J］. Journal of the European Economic Association， 2010（8）： 626-634.
30	Cai Y. Computational methods in environmental and resource economics［J］. Annual Review of Resource Economics， 2019（11）： 59-82.
31	Cai Y， Judd K L. A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems［J］. Quantitative Economics， 2023， 14： 651-687.
32	Weitzman M L. Additive damages， fat-tailed climate dynamics， and uncertain discounting［J］. Economics， 2009，3（1）： 20090039.
33	Tebaldi C， Knutti R. The use of the multi-model ensemble in probabilistic climate projections［J］. Philosophical Transactions of the Royal Society A： Mathematical， Physical and Engineering Sciences， 2007，365： 2053-2075.
34	Fan W， Hong L J， Zhang X. Distributionally robust selection of the best［J］. Management Science， 2020， 66： 190-208.
35	Hausfather Z， Peters G P.Emissions-the’business as usual’story is misleading［J］.Nature，2020，577（7792）： 618-620.
36	Berger L， Marinacci M. Model uncertainty in climate change economics： A review and proposed framework for future research［J］. Environmental and Resource Economics， 2020， 77： 475-501.
37	Petracou E V， Xepapadeas A， Yannacopoulos A N. Decision making under model uncertainty： Fréchet-wasserstein mean preferences［J］. Management Science， 2022， 68： 1195-1211.
38	Hansen L P， Sargent T J， Turmuhambetova G， et al. Robust control and model misspecification［J］. Journal of Economic Theory， 2006， 128： 45-90.
39	Strzalecki T. Axiomatic foundations of multiplier preferences［J］. Econometrica， 2011， 79： 47-73.
40	Cerreia‐Vioglio S， Hansen L P， Maccheroni F， et al. Making decisions under model misspecification ［EB/OL］. （2022-05-24）［2024-03-13］. .
41	Athanassoglou S， Xepapadeas A. Pollution control with uncertain stock dynamics： When， and how， to be precautious［J］. Journal of Environmental Economics and Management， 2012， 63： 304-320.
42	Rudik I. Optimal climate policy when damages are unknown［J］. American Economic Journal： Economic Policy， 2020（12）： 340-373.
43	Munshi J. From equilibrium climate sensitivity to carbon climate response［EB/OL］. （2018-03-17）［2025-01-07］. .
44	Pindyck R S. Climate change policy： What do the models tell us？［J］. Journal of Economic Literature， 2013， 51： 860-872.
45	Joos F， Roth R， Fuglestvedt J S， et al. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics： A multi-model analysis［J］. Atmospheric Chemistry and Physics， 2013， 13： 2793-2825.
46	Hennlock M. Robust control in global warming management： An analytical dynamic integrated assessment［DB/OL］. （2009-05-10）［2024-03-13］. .
47	IPCC. Climate change 2013： The physical science basis： Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change［M］. Cambridge， UK and New York， NY， USA： Cambridge University Press，2022.
48	Nordhaus WD， Popp D. What is the value of scientific knowledge？ An application to global warming using the PRICE model［J］. The Energy Journal，1997，18（1）：1-45.
49	Kelly D L， Tan Z. Learning and climate feedbacks： Optimal climate insurance and fat tails［J］. Journal of Environmental Economics and Management， 2015，72：98-122.
50	Traeger C P. A 4-stated DICE： Quantitatively addressing uncertainty effects in climate change［J］. Environmental and Resource Economics， 2014， 59： 1-37.
51	Kolstad C D. Learning and stock effects in environmental regulation： The case of greenhouse gas emissions［J］. Journal of Environmental Economics and Management， 1996， 31（1）： 1-18.
52	Matthews H D， Solomon S， Pierrehumbert R. Cumulative carbon as a policy framework for achieving climate stabilization［J］. Philosophical Transactions of the Royal Society A： Mathematical， Physical and Engineering Sciences， 2012，370： 4365-4379.
53	Matthews H D， Gillett N P， Stott P A， et al. The proportionality of global warming to cumulative carbon emissions［J］. Nature， 2009，459： 829-832.
54	Anderson E W， Brock W A， Sanstad A. Robust consumption and energy decisions.［EB/OL］. （2016-07-27）［2025-01-07］. .
55	Barnett M， Brock W， Hansen L P. Confronting uncertainty in the climate change dynamics［EB/OL］. （2021-05-27）［2024-03-13］. .
56	Barnett M， Brock W， Hansen L P. Climate change uncertainty spillover in the macroeconomy［J］. NBER Macroeconomics Annual， 2022，36： 253-320.
57	Barnett M. Climate change and uncertainty： An asset pricing perspective［J］. Management Science， 2023，69： 7562-7584.
58	MacDougall A H， Swart N C， Knutti R. The uncertainty in the transient climate response to cumulative CO2 emissions arising from the uncertainty in physical climate parameters［J］. Journal of Climate， 2017， 30： 813-827.
59	Manski C F， Sanstad A H， DeCanio S J. Addressing partial identification in climate modeling and policy analysis［J］. Proceedings of the National Academy of Sciences， 2021，118（5）：e2022886118.
60	Kopp R E， Shwom R L， Wagner G， et al. Tipping elements and climate-economic shocks： Pathways toward integrated assessment［J］.Earth’s Future， 2016（4）：346-372.
61	Berger L， Emmerling J， Tavoni M. Managing catastrophic climate risks under model uncertainty aversion［J］. Management Science， 2017， 63： 749-765.
62	Golosov M， Hassler J， Krusell P， et al. Optimal taxes on fossil fuel in general equilibrium［J］. Econometrica， 2014， 82： 41-88.
63	Gjerde J， Grepperud S， Kverndokk S. Optimal climate policy under the possibility of a catastrophe［J］. Resource and Energy Economics， 1999， 21： 289-317.
64	Ackerman F， Stanton E A， Bueno R. Fat tails， exponents， extreme uncertainty： Simulating catastrophe in DICE［J］. Ecological Economics， 2010， 69： 1657-1665.
65	Fatouros N， Cantelmo A， Papageorgiou C， et al. Monetary policy under natural disaster shocks［J］. International Economic Review， 2024. DOI：10.1111/iere.12694 .
66	Lempert R J， Collins M T. Managing the risk of uncertain threshold responses： Comparison of robust， optimum， and precautionary approaches［J］. Risk Analysis： An International Journal， 2007， 27： 1009-1026.
67	Keller K， Bolker B M， Bradford D F. Uncertain climate thresholds and optimal economic growth［J］. Journal of Environmental Economics and Management， 2004， 48： 723-741.
68	Lemoine D， Traeger C P. Ambiguous tipping points［J］. Journal of Economic Behavior & Organization， 2016， 132： 5-18.
69	Lemoine D， Traeger C. Watch your step： Optimal policy in a tipping climate［J］. American Economic Journal： Economic Policy， 2014（6）： 137-166.
70	Cai Y， Lontzek T S. The social cost of carbon with economic and climate risks［J］.Journal of Political Economy， 2019，127： 2684-2734.
71	Ploeg F V D， Zeeuw A D.Climate tipping and economic growth： Precautionary capital and the price of carbon［J］. Journal of the European Economic Association， 2018（16）： 1577-1617.
72	Cai Y， Judd K L， Lenton T M， et al. Environmental tipping points significantly affect the cost-benefit assessment of climate policies［J］. Proceedings of the National Academy of Sciences， 2015， 112： 4606-4611.
73	Lontzek T S， Cai Y， Judd K L， et al. Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy［J］. Nature Climate Change， 2015（5）： 441-444.
74	Cai Y， Lenton T M， Lontzek T S. Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction［J］. Nature Climate Change， 2016 （6）： 520-525.
75	Fillon R， Guivarch C， Taconet N. Optimal climate policy under tipping risk and temporal risk aversion［J］. Journal of Environmental Economics and Management， 2023，121：102850.
76	廖华，王方志，滕美萱. 气候经济复杂系统建模中的气候损失与适应研究进展［J］. 系统工程理论与实践， 2023，43（8）：2179-2194.
	Liao H， Wang F Z， Teng M X. Advances in climate damage and adaptation research in modelling complex climate-economy system［J］. Systems Engineering-Theory & Practice， 2023， 43（8）： 2179-2194.
77	Ciscar J C， Rising J， Kopp R E， et al. Assessing future clmate change impacts in the EU and the USA： Insights and lessons from two continental-scale projects［J］. Environmental Research Letters， 2019， 14： 084010.
78	Crost B， Traeger C P. Optimal CO2 mitigation under damage risk valuation［J］. Nature Climate Change， 2014（4）：631-636.
79	Baker E， Solak S. Management of energy technology for sustainability： How to fund energy technology research and development［J］. Production and Operations Management， 2014， 23： 348-365.
80	Lemoine D. The climate risk premium： How uncertainty affects the social cost of carbon［J］. Journal of the Association of Environmental and Resource Economists， 2021（8）： 27-57.
81	Lange A， Treich N. Uncertainty， learning and ambiguity in economic models on climate policy： Some classical results and new directions［J］. Climatic Change， 2008， 89： 7-21.
82	Heutel G， Moreno-Cruz J， Shayegh S. Solar geoengineering， uncertainty， and the price of carbon［J］. Journal of Environmental Economics and Management， 2018， 87： 24-41.
83	Scheffers B R， De Meester L， Bridge T C， et al. The broad footprint of climate change from genes to biomes to people［J］. Science， 2016， 354： aaf7671.
84	Liao H， Ye H. Endogenous economic structure， climate change， and the optimal abatement path［J］. Structural Change and Economic Dynamics， 2023， 65： 417-429.
85	Jensen S， Traeger C P. Optimal climate change mitigation under long-term growth uncertainty： Stochastic integrated assessment and analytic findings［J］. European Economic Review， 2014， 69： 104-125.
86	Bahn O， Haurie A， Malhamé R. A stochastic control model for optimal timing of climate policies［J］. Automatica， 2008， 44： 1545-1558.
87	Mi Z， Liao H， Coffman D M， et al. Assessment of equity principles for international climate policy based on an integrated assessment model［J］. Natural Hazards， 2019， 95： 309-323.
88	Nordhaus W D. A review of the stern review on the economics of climate change［J］. Journal of Economic Literature， 2007， 45： 686-702.
89	DeCanio S J， Manski C F， Sanstad A H. Minimax-regret climate policy with deep uncertainty in climate modeling and intergenerational discounting［J］. Ecologicol Economic， 2022， 201：1075522.
90	Crost B， Traeger C P.Risk and aversion in assessing climate policy［EB/OL］. （2012-12-01）［2024-03-13］. .
91	Cai Y Y， Brock W， Xepapadeas A， et al. Climate change impact on economic growth： Regional climate policy under cooperation and uncooperation［J］. Journal of the Association of Environmental and Resource Economists，2023，10（3）： 569-605.
92	杜娟，胥敬华. 基于交互式迭代算法的中国碳减排目标分配研究［J］. 中国管理科学， 2018，26（5）：31-39.
	Du J， Xu J H. A study on disaggregating China’s national carbon-reduction target based on interactive iterations［J］. Chinese Journal of Management Science， 2018， 26（5）： 31-39.
93	Cai Y， Brock W， Xepapadeas A. Climate change impact on economic growth： Regional climate policy under cooperation and noncooperation［J］. Journal of the Association of Environmental and Resource Economists， 2023（10）： 569-605.
94	Wei Y M， Han R， Liang Q M， Yu B Y， Yao Y F， Xue M M， et al. An integrated assessment of INDCs under shared socioeconomic pathways： An implementation of C 3 IAM［J］. Natural Hazards， 2018， 92： 585-618.
95	IPCC. Climate change 2022： Mitigation of climate change［M］. Cambridge and New York： Cambridge University Press，2022.
96	Wei Y M， Liu L J， Liang Q M， et al. Pathway comparison of limiting global warming to 2 C［J］. Energy and Climate Change， 2021（2）： 100063.
97	魏一鸣，米志付，张皓. 气候变化综合评估模型研究新进展［J］.系统工程理论与实践，2013，33（8）： 1905-1915.
	Wei Y M， Mi Z F， Zhang H. Progress of integrated assessment models for climate policy［J］. Systems Engineering-Theory & Practice， 2013， 33（8）： 1905-1915.
98	Baker E， Bosetti V， Salo A. Robust portfolio decision analysis： An application to the energy research and development portfolio problem［J］. European Journal of Operational Research， 2020， 284： 1107-1120.
99	Barnett M， Brock W A， Hansen L P， et al. How should climate change uncertainty impact social valuation and policy？［EB/OL］.（2023-11-03）［2024-03-13］. .
100	Wei Y M， Kang J N， Liu L C， et al. A proposed global layout of carbon capture and storage in line with a 2 C climate target［J］. Nature Climate Change， 2021（11）： 112-118.
101	赵昕，白雨，丁黎黎. 碳情绪能否助力碳捕捉与封存项目发展？［J］. 中国管理科学， 2023， 31（1）： 81-91.
	Zhao X， Bai Y， Ding L L. Can carbon emotions contribute to the development of carbon capture and storage projects？［J］. Chinese Journal of Management Science， 2023， 31（1）： 81-91.
102	Metz B， Davidson O， Coninck H， et al. Special report on carbon dioxide capture and storage［M］. Cambridge： Cambridge University Press，2005.
103	De Cian E， Massimo T. Mitigation portfolio and policy instruments when hedging against climate policy and technology uncertainty［J］. Environmental Modeling & Assessment， 2012， 17： 123-36.
104	IPCC. Special report on global warming of 1.5 ℃［M］. Cambridge and New York： Cambridge University Press，2018.
105	Pinke Z， Pow S， Kern Z. Volcanic mega-eruptions may trigger major cholera outbreaks［J］. Climate Research， 2019， 79： 151-162.
106	Emmerling J， Tavoni M. Climate engineering and abatement： A ‘flat’relationship under uncertainty［J］. Environmental and resource economics， 2018， 69： 395-415.
107	Moreno-Cruz J B， Keith D W. Climate policy under uncertainty： A case for solar geoengineering［J］. Climatic Change， 2013， 121： 431-444.
108	Bahn O， Chesney M， Gheyssens J， et al. Is there room for geoengineering in the optimal climate policy mix？［J］. Environmental Science & Policy， 2015， 48： 67-76.
109	Eric Bickel J. Climate engineering and climate tipping-point scenarios［J］. Environment Systems & Decisions， 2013， 33： 152-167.
110	Kelly D L， Heutel G， Moreno-Cruz J B， et al. Solar geoengineering， learning， and experimentation［J］. Journal of the Association of Environmental and Resource Economists， 2024，11（6）：1447-1486.
111	滕美萱，廖华，王方志.居民气候适应行为研究进展综述［J］.环境经济研究， 2022， 7（3）：145-168.
	Teng M X， Liao H， Wang F Z. A review of household adaptation to climate change［J］. Journal of Environmental Economics， 2022， 7（3）：145-168.
112	Wei Y M， Han R， Wang C， et al. Self-preservation strategy for approaching global warming targets in the post-Paris Agreement era［J］. Nature communications， 2020，11： 1624.
113	De Bruin KC， Dellink RB， Tol RS. AD-DICE： An implementation of adaptation in the DICE model［J］. Climatic Change， 2009， 95： 63-81.
114	Bosello F， Carraro C， De Cian E. Adaptation can help mitigation： An integrated approach to post-2012 climate policy［J］. Environment and Development Economics， 2013，18：270-290.
115	Bahn O， Chesney M， Gheyssens J. The effect of proactive adaptation on green investment［J］. Environmental Science & Policy， 2012， 18： 9-24.
116	Kelly D L， Kolstad CD， Mitchell G T. Adjustment costs from environmental change［J］. Journal of Environmental Economics and management， 2005， 50： 468-495.
117	Markandya A， De Cian E， Drouet L， et al. Building uncertainty into the adaptation cost estimation in integrated assessment models［EB/OL］.（2016-03-12）［2024-03-13］. .
118	The Royal Swedish Academy of Sciences. Scientific Background： Economic growth， technological change， and climate change ［EB/OL］.（2018-10-08）［2024-03-13］..

[1]	吴鑫育,谢海滨,马超群. 经济政策不确定性与人民币汇率波动率[J]. 中国管理科学, 2024, 32(8): 1-14.
[2]	代业明,孙锡连,齐尧,段金鹏. 考虑双重不确定性和用户负效用的多目标双层售电商决策[J]. 中国管理科学, 2024, 32(7): 163-171.
[3]	于文华,任向阳,杨坤,魏宇. 传染病不确定性对大宗商品期货价格波动的非对称影响研究[J]. 中国管理科学, 2024, 32(5): 254-264.
[4]	万光羽,蒲霏嫣,李紫纯,曹裕. 企业营销能力与生产运营能力交互作用及其对绩效影响研究[J]. 中国管理科学, 2024, 32(5): 275-285.
[5]	王军进, 许淞俊, 刘家国. 面对不确定性供应中断风险的制造商采购策略研究[J]. 中国管理科学, 2024, 32(12): 269-277.
[6]	郭福利,陈菊红,马歆,王萍. 考虑不确定性的多级产品服务供应链网络均衡研究[J]. 中国管理科学, 2024, 32(10): 194-202.
[7]	宫晓莉, 刘建民, 熊熊, 张维. 经济政策不确定性与国际金融网络间的尾部风险传染研究[J]. 中国管理科学, 2023, 31(7): 78-90.
[8]	甘柳, 李群翅. 研发项目投资决策与最优政府补贴研究[J]. 中国管理科学, 2023, 31(2): 63-72.
[9]	吴江,彭扬,邹黎敏,冯玉明,涂正文. 不确定性因素影响下的混沌供应链系统脉冲同步策略研究[J]. 中国管理科学, 2023, 31(11): 80-89.
[10]	陆静, 喻浩. 产业政策对股票特质波动率的影响及机制研究[J]. 中国管理科学, 2023, 31(1): 21-36.
[11]	钟婉玲, 李海奇. 国际油价、宏观经济变量与中国股市的尾部风险溢出效应研究[J]. 中国管理科学, 2022, 30(2): 27-37.
[12]	严复雷, 张语桐, 崔钟月, 张高勋. 基于非参多元Expectile模型的原油价格风险测度研究—宏观不确定性视角[J]. 中国管理科学, 2022, 30(12): 222-233.
[13]	陈刚, 付江月. 灾后不确定需求下应急医疗移动医院鲁棒选址问题研究[J]. 中国管理科学, 2021, 29(9): 213-223.
[14]	吴江华, 姜帆. 供应链中纵向信息获取和横向信息共享策略研究[J]. 中国管理科学, 2021, 29(8): 161-173.
[15]	黄春萍, 王志文, 章静敏, 曾珍香, 李香. 不确定环境下品牌联盟组织间协作模式选择的计算实验研究[J]. 中国管理科学, 2021, 29(6): 189-201.