对实现碳中和的气候政策进行稳健性评估:DRO-IAMS 方法

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Operations Research Pub Date : 2024-10-30 DOI:10.1016/j.cor.2024.106879
Guiyu Li, Hongbo Duan
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引用次数: 0

摘要

气候-经济综合系统中存在大量不确定性,包括参数不确定性和模型不确定性,这给评估《巴黎协定》承诺的气候目标带来了巨大挑战。在本研究中,我们通过将分布稳健优化(DRO)方法与综合评估模型(IAMs)有效耦合,建立了气候政策稳健性评估框架,称为 DRO-IAMS 框架,其中 "S "强调了所纳入的多个 IAMs。我们的方法通过最坏情况下的条件风险值(CVaR)准则,有效捕捉胖尾效应并利用其可操作性,确定实现碳中性目标的保障概率。我们开发的 DRO-IAMS 框架利用不确定性参数的离散支持,可以使用 IAMs 方便地获取全球气温升高(GTI)的目标值,从而有效地规避了分析利用黑箱特征 IAMs 的困难,并以更全面、更灵活的方式整合了 DRO(如矩、j-发散和 Wasserstein 模糊集)和 IAMs(如 DICE、FUND 和 CVaR)、DICE、FUND 和 E3METL),以应对气候政策评估中的参数和模型不确定性。我们的研究结果表明,参数的不确定性和模型的不确定性--作为对气候变暖和政策的经济绩效有重大影响的关键问题--可能会导致对气候目标实现情况的评估出现偏差。我们提出的 DRO-IAMS 方法--通过其设计--表明能够通过更严格的减缓努力来有效地缓解这些问题,并且与常见的基于抽样的方法相比,能够为典型的气候政策提供更可靠的评估。
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Robustness assessment of climate policies towards carbon neutrality: A DRO-IAMS approach
There are plenty of uncertainties in the integrated climate-economic system including parameter uncertainty and model uncertainty, which significantly challenges the assessment of climate goals committed in the Paris Agreement pledges. In this study, we develop a robustness assessment framework of climate policy by effectively coupling the distributionally robust optimization (DRO) methodology with integrated assessment models (IAMs), termed DRO-IAMS framework, where “S” emphasizes the multiple IAMs being incorporated. Our approach determines a safeguarding probability for the achievement of carbon-neutrality target through the worst-case Conditional Value-at-Risk (CVaR) criterion by effectively capturing the fat-tail effect and exploiting its tractability. Leveraging a discrete support of uncertain parameters over which the objective value of global temperature increase (GTI) can be readily accessible using the IAMs, our developed DRO-IAMS framework effectively circumvents the difficulty in utilizing analytically the black-box-featured IAMs, and achieves a comprehensive and more flexible fashion in integrating the DRO (e.g, moment, ϕ-divergence, and Wasserstein ambiguity sets) and IAMs (e.g., DICE, FUND, and E3METL) to cope with parameter- and model uncertainties in climate policy assessment. Our results suggest that parameter uncertainty and model uncertainty — as critical issues that can have significant impacts on the warming and economic performance of policies — could incur biased assessment for the realization of climate targets. Our proposed DRO-IAMS approach — by its design — is shown to be able to effectively mitigate such issues by pursuing stricter mitigation efforts, and can produce more reliable assessments for typical climate policies than the common sampling-based approaches.
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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