We 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.

中文翻译：

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深度不确定性下的区域气候政策：稳健控制和分配问题

我们使用区域瞬态气候响应的新模式缩放方法研究气候变化政策，以便在深度不确定的条件下开发区域经济-气候模型。我们将福利权重与地区联系起来，并分析由社会规划者的解决方案在地区范围内得出的合作成果。最近的文献表明，低纬度（温暖）地区的损害更大，并且预计低纬度地区的损害会比温带地区更大。在高度不确定的情况下，稳健的控制政策在排放方面更加保守，并且当引入区域分配权重时，相对贫困地区的碳税较低。对稳健性的轻微担忧正在改善贫困地区的福利，而强烈的担忧会给所有地区带来福利成本。我们表明，区域气温升高将增加用于学习的资源，以减少深度不确定性。