非对称信息及协同效应下温室气体减排的最优政策选项

Dealing with climate change requires the appropriate design of policy instrument to control the emission of greenhouse gases (GHGs). In addition to carbon dioxide (CO2), there are other GHGs, such as methane (CH4), which are also significantly contributing to climate change. Besides, there can be synergistic effect in the joint abatement of different GHGs, which implies that the policy designed for one GHG, such as a carbon tax, can also have impact on the emissions and policy design of other GHGs. Therefore, it is necessary to take into account multiple GHGs together in the design of abatement policies. In addition, the optimal design of abatement policies from the government is subject to the asymmetric information between the government and firms. This project takes into account the asymmetric information and synergistic effect in abatement by developing a stochastic dynamic programming model which contains multiple stock pollutants, so as to comprehensively analyze the optimal policy instrument combinations for the joint abatement of multiple GHGs. The scientific contribution of this project can be summarized as: 1) it takes the other GHGs other than CO2 and the synergistic effect between various GHGs into account in the discussion of optimal policy design; 2) it considers the uncertainty/asymmetric information and its effect on the optimal policy design for GHG abatement; 3) we build and analytically solve a stochastic dynamic programming model with multiple stock pollutants; 4) it provides systematic analysis for the optimal design of policy instrument combinations for GHGs abatement and air pollution control in China.

应对气候变化需要合理制定政策工具以控制温室气体的排放。除二氧化碳外，还有甲烷等其他温室气体同样对气候变化有显著贡献，并且不同温室气体的共同减排中还可能存在协同效应，这就意味着对一种温室气体制定的减排政策例如碳税，也会对其他温室气体的排放以及政策制定产生影响。因此，有必要在政策设计中将多种温室气体共同考虑在内。此外，政府最优减排政策的制定还受到政府与企业间非对称信息的制约。本项目通过构建包含多个存量污染物的随机动态规划模型将非对称信息和减排的协同效应统筹考虑以系统分析多种温室气体共同减排的最优政策组合选项。本项目的学术贡献体现在：1）将二氧化碳以外的其他温室气体及减排中的协同效应纳入最优减排政策的研究；2）综合考虑了不确定性/非对称信息对最优减排政策制定的影响；3）构建并解析求解包含多个存量污染物的一般随机动态规划模型；4）在我国温室气体减排及治理空气污染的最优政策组合制定方面提供系统分析。

应对气候变化需要合理制定政策工具以控制温室气体的排放。除二氧化碳外，还有甲烷等其他温室气体同样对气候变化有显著贡献。因此，有必要在政策设计中将多种温室气体共同考虑在内。此外，政府最优减排政策的制定还受到政府与企业间非对称信息的制约。本项目通过构建包含多个存量污染物的随机动态规划模型将非对称信息和减排的协同效应统筹考虑以系统分析多种温室气体共同减排的最优政策组合选项。在项目执行期间，项目负责人及其团队按照研究目标设定，从多种温室气体减排、温室气体与空气污染物协同减排、以及国际气候变化合作三个方面入手开展研究。在微观层面，建立了针对多种温室气体协同减排的随机动态优化模型，并将解析解与数值解结合引入到模型的求解以及政策的分析评价中；在宏观层面，建立了国际气候合作的动态博弈模型并对不确定条件下的减排合作提出了具体的政策建议。本项目研究成果对国际及国内温室气体减排及治理空气污染的最优政策组合提供了有用参考且已经在领域内国际重要期刊上得以发表。

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