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, $\varphi$-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.