Liu Chong, Wang Xiaoman, Li Haiyang
School of Public Policy & Management, Anhui Jianzhu University, Hefei, 230022, China.
School of Government, Sun Yat-sen University, Guangzhou, 510006, China.
Sci Rep. 2025 May 13;15(1):16554. doi: 10.1038/s41598-025-00677-0.
Studying carbon emissions (CE) at different administrative scales will help facilitate crafting tailored emission reduction policies for China's regions, which is vital for achieving the dual carbon goals. However, previous studies focused on a single administrative scale, lacking multiscale research. This paper combined energy consumption data with nighttime light data and adopted a spatial autocorrelation, variation coefficient (VC), and decoupling model to study the spatiotemporal dynamics and decoupling effect of CE at the three administrative scales of provinces, prefecture-level cities, and counties in China from 2000 to 2020. The results were as follows: (1) The VC of CE showed different trends at different scales, with its coefficient size successively ranked at the county, prefecture, and province levels. (2) CE at different scales showed positive spatial autocorrelation and the significance was strongest at the county level. (3) The decoupling trend between CE and economic growth has generally shown a positive development across different spatial scales. The average elasticity decoupling index at the provincial, prefectural, and county levels has decreased overall, from 0.88, 1.82, and 3.74 to 0.19, 1.36, and 3.05, respectively. However, the characteristics of these changes differ. The CV of the elasticity decoupling index increased at the provincial and prefectural levels, rising from 1.161 to 1.563 to 1.419 and 2.669, respectively, while it decreased slightly at the county level, from 3.862 to 3.765. (4) The dominant type of decoupling at the provincial level had changed from expansive negative decoupling (ED) to strong decoupling (SD). Meanwhile, at the prefectural and county levels, the ED was still dominant, but the number of SD had increased significantly, rising from 75 to 138 at the prefectural level and from 748 to 1160 at the county level. This study demonstrates China's carbon emissions sensitivity to scale, emphasizing the importance of adapting emission reduction measures to local conditions.
研究不同行政尺度下的碳排放（CE）将有助于为中国各地区制定量身定制的减排政策，这对于实现双碳目标至关重要。然而，以往的研究集中在单一行政尺度上，缺乏多尺度研究。本文将能源消耗数据与夜间灯光数据相结合，采用空间自相关、变异系数（VC）和解耦模型，研究了2000年至2020年中国省级、地级市和县级三个行政尺度下碳排放的时空动态和解耦效应。结果如下：（1）碳排放的变异系数在不同尺度下呈现不同趋势，其系数大小依次为县级、地级和省级。（2）不同尺度下的碳排放呈现正空间自相关，且在县级层面的显著性最强。（3）碳排放与经济增长之间的解耦趋势在不同空间尺度上总体呈现积极发展态势。省级、地级和县级的平均弹性解耦指数总体下降，分别从0.88、1.82和3.74降至0.19、1.36和3.05。然而，这些变化的特征有所不同。弹性解耦指数的变异系数在省级和地级层面有所增加，分别从1.161升至1.563和从2.669升至1.419，而在县级层面略有下降，从3.862降至3.765。（4）省级层面的主导解耦类型已从扩张性负解耦（ED）转变为强解耦（SD）。同时，在地级和县级层面，扩张性负解耦仍占主导，但强解耦的数量显著增加，地级层面从75个增至138个，县级层面从748个增至1160个。本研究证明了中国碳排放对尺度的敏感性，强调了减排措施因地制宜的重要性。
