Allocation and Pricing Mechanism of Carbon Emissions Permit of China's Industrial Firms Under
Asymmetric Information
Qian Haoqi, Wu Libo
摘要：设计完善的碳交易体系能够为我国实现碳达峰与碳中和目标作出重要贡献，其中，合理的碳排放权分配和定价机制能够通过形成有效的价格信号来促进企业的节能减排行为。本文提出了一个信息不对称情况下的碳排放权定价与分配的混合机制，该机制通过结合单一价格机制和单一数量机制的优点，能够实现有效的信息激励，促使企业将其自身真实的排放信息上报给决策者，用以制定最优政策目标。基于我国投入产出数据以及工业企业微观数据，并利用可计算一般均衡模型的仿真模拟对理论假设进行了验证。结果表明，本文提出的混合机制的减排效果优于单一碳税机制和单一碳排放权交易机制，最接近于完全信息情景，因而能够在实现碳排放总量控制的情况下形成有效的碳价信号。
关键词：信息不对称；碳排放权分配；可计算一般均衡模型；边际减排成本曲线；无监督学习
Abstract: Designing a well-established carbon emissions trading system (ETS) can contribute greatly to China's goals of peaking carbon dioxide emissions and achieving carbon neutrality. For ETS, an appropriate carbon permit allocation and pricing mechanism can promote firms' energy conservation and emission reduction behaviors through forming effective price signals. This paper proposes a hybrid pricing and allocation mechanism of carbon permits under asymmetric information. This mechanism combines advantages of both pure price control mechanism and pure quantity control mechanism, which provides effective incentives for firms to report their true emissions information to the social planner to set optimal policy targets. The simulation analyses justify theoretical model's predictions by using computable general equilibrium (CGE) model based on China's input-output data and firm-level data. Results show that abatement effect of the hybrid mechanism is closer to the full information case than that of pure price control mechanism and pure quantity control mechanism.
Keywords:Asymmetric Information；Carbon Emissions Permit Allocation；Computable General Equilibrium Model；Marginal Abatement Cost Curve；Unsupervised Learning
基金项目：国家科技部国家重点研发计划“应对全球变化的可持续性转型模式研究”（2020YFA0608600）、国家杰出青年科学基金项目“能源环境经济与政策分析”（71925010）、国家自然科学基金青年科学基金项目“碳排放峰值约束下的中国绿色电力转型研究——基于电力大数据与中国多区域CGE模型”（71703027）、上海市人才发展资金资助（2021098）。
全文：
信息不对称视角下我国工业企业碳排放权分配与定价机制研究.pdf