Business School, Hohai University, Changzhou, 213022, People's Republic of China.
School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
Environ Sci Pollut Res Int. 2023 Jun;30(30):75629-75654. doi: 10.1007/s11356-023-27745-0. Epub 2023 May 24.
As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.
作为最大的能源消费国，中国控制能源消费碳排放对世界气候治理具有关键作用。然而，很少有研究从能源消费的角度探讨促进中国经济增长与“碳达峰碳中和”目标高度协同的减排路径。本文基于能源消费碳排放的测度，揭示了中国在国家-省级层面的碳排放的时空分布和演变趋势。考虑到研发和城市化等多维社会经济因素，利用 LMDI 模型对国家-省级层面的能源消费碳排放驱动效应进行分解。进一步，本文结合 Tapio 脱钩指数与 LMDI 模型，将中国的脱钩状态逐年以及分四个时期在省级层面进行分解，以探讨碳脱钩状态变化的原因。结果表明：（1）2013 年前中国能源消费碳排放增长速度较快，之后增速放缓。各省之间的碳排放规模和增长率存在显著差异，可以相应地分为四类。（2）研发规模效应、城市化效应和人口规模效应是中国碳排放增长的驱动因素；而能源结构效应、能源消费产业结构效应、能源强度效应和研发效率效应抑制了中国的碳排放增长。（3）弱脱钩是 2003 年至 2020 年中国最主要的脱钩状态，且各省之间的脱钩状态差异显著。根据结论，本文提出了基于中国能源禀赋的有针对性的政策建议。
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