发展中经济体二氧化碳排放趋势成为全球温升2℃的关键决定因素，其重要诱因是全球化加深带来的生产转移和排放转移。因此，追溯全球生产网络中碳排放具有重要意义，也是目前气候变化相关研究的热点之一。近年有关排放责任的争论促进了贸易隐含碳的研究以及全球投入产出数据库和相关模型的发展，但在使用微观企业数据提高进口矩阵估计的准确性方面尚缺乏研究。我们前期工作通过对接生产和贸易数据，量化了基于“比例性假定”的进口矩阵估计方法的偏误。结合文献及既往工作基础，我们推测：基于微观数据改进的投入产出表和一般均衡模型对贸易隐含碳的准确测度和减排路径模拟具有显著影响。本项目拟基于微观数据，通过改进投入产出表的进口矩阵，构建新颖的投入产出模型和改进的一般均衡模型，准确估计我国在全球生产网络中的碳排放，并辨明其发展趋势和驱动因素。进而，通过数值模拟等方法，分析减排政策的经济和环境影响，为气候变化政策等议题提供定量参考。

Developing economies’ future development pathway of carbon dioxide emissions will be a pivotal determinant in whether global temperature rise can be kept below 2°C. One of the most important reasons is that deepening globalization facilitates the reallocation of production and associated emissions. Therefore, it is of significance to study the carbon emissions embodied in trade, which is one of the most relevant topics in climate change related research. Recently, the hot-debated issues concerning emissions responsibilities have promoted trade in CO2 emissions study as well as the development of global input-output databases and relevant models. However, the efforts to improve the accuracy of import matrix using micro data are relatively lacking. Our previous work, by linking production and trade data, quantified the bias of import matrix that is estimated based on "proportionality assumption". Based on previous studies and our recent work, we hypothesized that: micro-data improved input-output tables and general equilibrium models could lead to significant impact on the accuracy of carbon embodied in trade estimation and emissions reduction pathways simulation. This project proposes to improve the import matrix in input-output tables by using micro-data, and to construct a novel and refined general equilibrium model to study the trends and drivers of China's carbon dioxide emissions development. Furthermore, the impact of different policy mix on the economy and environment will be evaluated, which will be served as reference for developing climate change policy instruments and transforming China onto a low-carbon pathway.