[论文解读] Impact of COVID-19 type events on the economy and climate under the stochastic DICE model

Pavel V. Shevchenko, Daisuke Murakami+2 more|arXiv (Cornell University)|Nov 1, 2021

Climate Change Policy and Economics参考文献 22被引用 5

一句话总结

本文通过在世界总产出中引入类似疫情的离散随机冲击，将确定性DICE模型扩展为随机最优控制框架，以评估其对气候与经济的长期影响。结果表明，短期冲击对温度与排放的长期影响可忽略不计；而持续5%的产出下降可使峰值温度降低0.1°C；在随机冲击下采用次优的确定性政策，由于经济活动减少导致排放下降，产生约0.25°C的冷却效应。

ABSTRACT

The classical DICE model is a widely accepted integrated assessment model for the joint modeling of economic and climate systems, where all model state variables evolve over time deterministically. We reformulate and solve the DICE model as an optimal control dynamic programming problem with six state variables (related to the carbon concentration, temperature, and economic capital) evolving over time deterministically and affected by two controls (carbon emission mitigation rate and consumption). We then extend the model by adding a discrete stochastic shock variable to model the economy in the stressed and normal regimes as a jump process caused by events such as the COVID-19 pandemic. These shocks reduce the world gross output leading to a reduction in both the world net output and carbon emission. The extended model is solved under several scenarios as an optimal stochastic control problem, assuming that the shock events occur randomly on average once every 100 years and last for 5 years. The results show that, if the world gross output recovers in full after each event, the impact of the COVID-19 events on the temperature and carbon concentration will be immaterial even in the case of a conservative 10\% drop in the annual gross output over a 5-year period. The impact becomes noticeable, although still extremely small (long-term temperature drops by $0.1^\circ \mathrm{C}$), in a presence of persistent shocks of a 5\% output drop propagating to the subsequent time periods through the recursively reduced productivity. If the deterministic DICE model policy is applied in a presence of stochastic shocks (i.e. when this policy is suboptimal), then the drop in temperature is larger (approximately $0.25^\circ \mathrm{C}$), that is, the lower economic activities owing to shocks imply that more ambitious mitigation targets are now feasible at lower costs.

研究动机与目标

• 评估类似疫情的随机冲击对DICE模型长期经济与气候影响。
• 将确定性DICE模型扩展为包含总产出离散冲击的随机最优控制框架。
• 评估持续性与暂时性冲击对碳浓度、温度及减缓政策有效性的不同影响。
• 比较在随机环境中采用最优随机控制与次优确定性政策应用下的结果差异。
• 探究冲击引发的经济衰退是否使更雄心勃勃的气候目标在更低成本下成为可能。

提出的方法

• 将DICE模型重新表述为具有六个确定性状态变量的动态规划最优控制问题：大气与上下层海洋中的碳浓度、大气与下层海洋温度，以及经济资本。
• 引入一个离散的随机冲击过程，使世界总产出在五年内减少5%或10%，建模为平均每100年发生一次的跳跃过程。
• 将模型扩展以包含两个控制变量：碳排放减缓率与消费，在不确定性下进行优化。
• 使用蒙特卡洛模拟与最小二乘蒙特卡洛技术，将扩展后的随机DICE模型求解为递归最优控制问题。
• 模拟多种情景：(A1, A2) 暂时性与持续性冲击，以及 (C) 将确定性DICE政策应用于随机轨迹。
• 以DICE-2016模型为基础框架，通过减少总产出，将冲击引起的净产出与排放减少纳入模型。

实验结果

研究问题

• RQ1 世界总产出的暂时性与持续性COVID-19型冲击如何影响长期碳浓度与大气温度？
• RQ2 在具有重复冲击的随机环境中应用确定性DICE政策会产生何种影响？
• RQ3 冲击引发的经济衰退在多大程度上可减少排放并降低峰值温度，即使没有主动减缓措施？
• RQ4 与确定性情景相比，持续性冲击的存在如何改变实现气候目标的可行性与成本？
• RQ5 与确定性DICE模型相比，随机冲击框架是否导致不同的最优减缓与消费轨迹？

主要发现

• 五年内年总产出暂时下降10%对长期温度与碳浓度的影响可忽略不计，峰值温度变化低于0.01°C。
• 持续5%的产出下降导致可测量但微小的长期温度降低，约0.1°C，源于持续较低的排放。
• 当在随机环境中应用确定性DICE政策（即次优政策）时，与确定性基线相比，峰值大气温度下降约0.25°C。
• 在所有随机情景中，碳浓度（MAT）均低于确定性DICE解，次优政策情景下峰值水平平均降低10%。
• 经济资本（K）与净产出（Ynet）在随机冲击下持续降低，尤其在持续性冲击情景中，因生产率与产出下降而显著降低。
• 结果表明，类似COVID-19的经济冲击可无意中降低排放，并使更雄心勃勃的气候目标在更低的减缓成本下成为可能。