Abstract
AbstractWe study climate change policies using the novel pattern scaling approach of regional transient climate response in order to develop a regional economy–climate model under conditions of deep uncertainty. We associate welfare weights with regions and analyze cooperative outcomes derived by the social planner's solution at the regional scale. Recent literature indicates that damages are larger in low latitude (warmer) areas and are projected to become relatively even larger in low latitude areas than at temperate latitudes. Under deep uncertainty, robust control policies are more conservative regarding emissions and, when regional distributional weights are introduced, carbon taxes are lower in the relatively poorer region. Mild concerns for robustness are welfare improving for the poor region, while strong concerns have welfare cost for all regions. We show that increasing regional temperatures will increase resources devoted to learning, in order to reduce deep uncertainty.
Highlights
The need for regional analysis of the impacts of climate change – in contrast to the global approach taken by Integrated Assessment Models (IAMs) such as DICE (Nordhaus and Sztorc, 2013; Nordhaus, 2014) – has been clearly recognized in the literature
The present paper contributes to climate change economics by studying climate change policies in a multi-regional model based on the novel pattern scaling approach of regional TCREs (RTCREs) under conditions of deep uncertainty associated with regional temperature dynamics, regional climate change damages, and policy in the form of carbon taxes
We study climate change policies by using the novel pattern scaling approach of RTCREs and develop an economy–climate model under conditions of deep uncertainty associated with temperature dynamics, regional climate change damages, and policy in the form of carbon taxes
Summary
The need for regional analysis of the impacts of climate change – in contrast to the global approach taken by Integrated Assessment Models (IAMs) such as DICE (Nordhaus and Sztorc, 2013; Nordhaus, 2014) – has been clearly recognized in the literature (see, for example, Easterling, 1997). The explicit introduction of regional temperature dynamics allows us to obtain a clearer picture of the impacts of climate change across regions, and especially across rich and poor regions Recent literature, such as Burke et al (2015), Hsiang et al (2017) and Diffenbaugh and Burke (2019a), stresses that damages are larger in low latitude (warmer). The present paper contributes to climate change economics by studying climate change policies in a multi-regional model based on the novel pattern scaling approach of RTCREs under conditions of deep uncertainty associated with regional temperature dynamics, regional climate change damages, and policy in the form of carbon taxes. In the final section we consider the possibility of diverting resources to learning, which will reduce concerns about model misspecification
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