中国碳中和指数的区域差异、时空演变特征及其驱动因素分析

Analysis of Regional Differences, Spatiotemporal Evolution Characteristics and Driving Factors of China’s Carbon Neutrality Index

摘要: 实现碳中和是发展中国家应对全球气候变暖、保持经济可持续发展面临的共同挑战。基于2007~2022年中国30个省级行政区面板数据，本文以碳源与碳汇之差测度各省份碳中和指数，综合应用多种计量模型和方法，系统考察了中国碳中和指数的区域差异、时空演变特征及其驱动因素。研究发现：(1) 中国碳中和指数呈现“整体持续上升、东高西低、空间集聚性不断增强”的时空分布格局，高值区域逐步向东部沿海地区及华北平原集中；(2) 区域差异总体呈现“先快速收敛、后趋于稳定”的演变趋势，总体基尼系数由0.046下降至0.038，其中组间差异为区域差异的主要来源；(3) 各省碳中和指数维持原有状态的概率均超过82%，当中低水平省份与中高水平省份接壤时，其向上跃迁至更高水平的概率明显提升；(4) 经济发展水平、能源利用效率及财政支出规模对碳中和指数具有显著正向影响，城镇化水平则通过结构转型与技术扩散等路径产生显著的正向间接效应。研究发现不仅为深入了解碳中和进程中区域发展不均衡的内在机理与多维协同机制提供了扎实的实证支撑，也为制定差异化、精准化的区域碳减排政策，以及强化跨区域协同治理能力提供了政策启示。

Abstract: Achieving carbon neutrality is a common challenge for developing countries in addressing global warming and maintaining sustainable economic development. Based on panel data of 30 provincial-level administrative regions in China from 2007 to 2022, this paper measures the carbon neutrality index of each province by the difference between carbon sources and carbon sinks. By comprehensively applying various econometric models and methods, this paper systematically examines the regional differences, spatiotemporal evolution characteristics, and driving factors of China’s carbon neutrality index. The study found that: (1) China’s carbon neutrality index exhibits a spatiotemporal distribution pattern of “overall continuous rise, high in the east and low in the west, and increasing spatial agglomeration”, with high-value areas gradually concentrating in the eastern coastal areas and the North China Plain; (2) Regional differences generally show an evolutionary trend of “rapid convergence followed by stabilization”, with the overall Gini coefficient decreasing from 0.046 to 0.038, among which inter-group differences are the main source of regional differences; (3) The probability of each province maintaining its original carbon neutrality index exceeds 82%, and when low- to medium-level provinces border high- to medium-level provinces, the probability of them jumping to a higher level increases significantly; (4) Economic development level, energy efficiency, and fiscal expenditure scale have a significant positive impact on the carbon neutrality index, while urbanization level generates a significant positive indirect effect through structural transformation and technology diffusion. These findings not only provide solid empirical support for a deeper understanding of the inherent mechanisms and multi-dimensional collaborative mechanisms of regional development imbalances in the carbon neutrality process, but also provide policy insights for formulating differentiated and precise regional carbon emission reduction policies and strengthening cross-regional collaborative governance capabilities.