中国省域碳达峰路径与政策

从省域层面推动发达地区率先碳达峰是中国实现2030年前碳达峰目标的有效途径.以江苏省为例,构建了省级LEAP-Jiangsu模型,结合改进的多层对数平均迪氏分解(M-LMDI)模型、 Tapio脱钩模型和减污降碳协同效应模型探索碳排放的关键影响因素及碳达峰路径.采用改进的多层M-LMDI模型分析了江苏省历史时期和未来预测情况下碳排放的主要影响因素;基于历史碳排放分解结果和规划目标,构建了多种发展情景的LEAP-Jiangsu模型,预测江苏省碳排放达峰时间及达峰水平;运用Tapio脱钩模型和减污降碳协同效应模型,研究碳排放与经济发展的关系和碳排放与大气污染物排放的协同效应.结果表明,2035年江苏省一次能源需求总量以标煤计约为401.2～474.6 Mt,终端能源需求量约为319.2～382.3 Mt;江苏省最可能在2025～2030年实现碳达峰目标,碳排放峰值约为815.3～845.7 Mt;能源强度降低、产业结构优化、提高终端电气化水平和能源结构调整等节能减排措施的减排贡献率分别为33.1%、 26.8%、 21%和15.2%.

Pathway and Policy for China's Provincial Carbon Emission Peak

An effective way for China to achieve a carbon emission peak by 2030 is to encourage developed regions to take the lead in attaining carbon peaking at the regional level. Considering Jiangsu Province as an example, this study established a provincial low emissions analysis platform (LEAP-Jiangsu) model. It combined the improved multilevel logarithmic mean Divisia index (M-LMDI) model, Tapio decoupling model, and the synergistic effect of pollution and carbon reduction model to explore the key influencing factors of carbon emissions and carbon reduction paths. The improved M-LMDI model was used to analyze the factors influencing historical and future carbon emissions in Jiangsu Province. Based on the analysis results and planning objectives, a LEAP-Jiangsu model involving various development scenarios was established to predict the time and value of carbon emission peaks. The Tapio decoupling and synergistic effect models were used to clarify the relationship between carbon emissions and economic development, the synergistic effect of carbon, and air pollutant emission reduction. The prediction results demonstrated that the total primary energy demand of Jiangsu Province in 2035 was predicted to be approximately 401.2-474.6 Mt, and the final energy demand would be approximately 319.2-382.3 Mt. Jiangsu Province was most likely to achieve the goal of carbon peaking in 2025-2030, and the peak carbon emission was approximately 815.3-845.7 Mt. The contribution rates of energy conservation and emission reduction measures such as energy intensity reduction, industrial structure optimization, terminal electrification improvement, and energy structure adjustment were 33.1%, 26.8%, 21%, and 15.2%, respectively.