Chen Danqing, Li Shuangshuang
School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 351008, People's Republic of China.
School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, People's Republic of China.
Sci Rep. 2025 May 20;15(1):17442. doi: 10.1038/s41598-025-01748-y.
Carbon neutrality is a critical pathway to achieving a sustainable future. Investigating the driving factors for carbon neutrality can provide empirical evidence to support ecosystem protection. Prior studies used mean regression to investigate carbon neutrality, concealing the heterogeneity of carbon neutrality. In this paper, we introduce a dynamic spatial Durbin quantile regression (DSDQR) model along with its estimation method, and derive the marginal effect formulas for independent variables at different quantiles. Then we apply this methodology to examine the impact mechanisms of environmental governance pressure, economic growth, and their interaction effects on carbon neutrality performance using Chinese provincial data spanning 2011-2022. Key findings include: (1) Temporal, spatial, and path dependencies in carbon neutrality performance are prevalent across nearly all provinces. (2) Environmental governance pressure exhibits an inhibitory short-term effect on carbon neutrality in provinces at medium and low quantiles, while it has a positive long-term impact in high quantile provinces. (3) Economic growth generally hinders carbon neutrality performance in most provinces. However, economic growth in high quantile provinces exerts a positive long-term influence on carbon neutrality performance after the COVID-19 pandemic. (4) The interaction between environmental governance pressure and economic growth demonstrates a significant positive short-term impact on carbon neutrality performance post-epidemic, yet it has a negative long-term effect in high quantile provinces. Finally, this article calls for differentiated decarbonization strategies based on provincial carbon neutrality development stages.
碳中和是实现可持续未来的关键途径。探究碳中和的驱动因素可为支持生态系统保护提供实证依据。先前的研究使用均值回归来研究碳中和，掩盖了碳中和的异质性。在本文中，我们引入了动态空间杜宾分位数回归（DSDQR）模型及其估计方法，并推导了不同分位数下自变量的边际效应公式。然后，我们运用该方法，利用2011 - 2022年中国省级数据，考察环境治理压力、经济增长及其交互作用对碳中和绩效的影响机制。主要研究结果包括：（1）几乎所有省份的碳中和绩效都普遍存在时间、空间和路径依赖性。（2）环境治理压力对中低量化省份的碳中和具有短期抑制作用，而对高量化省份具有长期积极影响。（3）经济增长总体上阻碍了大多数省份的碳中和绩效。然而，高量化省份的经济增长在新冠疫情后对碳中和绩效产生了长期积极影响。（4）环境治理压力与经济增长的交互作用在疫情后对碳中和绩效具有显著的短期正向影响，但在高量化省份具有长期负向影响。最后，本文呼吁根据省级碳中和发展阶段制定差异化的脱碳策略。
