“双碳”目标下中国服务业碳强度时空交互特征及跃迁机制

为实现国家自主贡献承诺，如期达到“碳达峰、碳中和”目标，中国服务业的低碳发展是必然趋势。基于多种空间分析方法，从时空交互视角研究了中国服务业碳强度差异格局、空间关联、动态演化及跃迁机制。结果表明：(1) 2005—2019年中国服务业碳强度的总体差异存在动态收敛趋势，在空间上也呈现显著的聚类现象，且空间集聚水平逐渐趋于稳定。(2)在服务业碳强度局部空间结构与依赖方向上，西北与东北地区波动性较强，东部沿海地区相对稳定；在碳强度时空跃迁的过程中整体表现出一定的转移惰性，具有较强的空间依赖或路径锁定特征，其中中部、西部的多数地区始终保持高碳强度属性，是制约中国服务业协同减排的关键区域。(3)服务业碳强度的时空网络格局主要以正向关联为主，表现出较强的空间整合性，但少数邻接省域仍存在一定程度的时空竞争。(4)各地区服务业碳强度时空跃迁的驱动模式存在差异，其中，东部沿海省份主要受人口—城镇化制约模式的影响，西北、西南和东北的多数地区主要受技术—规制驱动模式的影响。自东南至西北，中国服务业碳强度的跃迁模式逐渐呈现出“同向制约—反向发展—同向发展”的阶梯递变格局。因此，政府减排政策的制定不仅应统筹考虑...

Spatiotemporal interaction characteristics and transition mechanism of carbon intensity in China's service industry under the targets of carbon peak and carbon neutrality

To realize carbon‐related nationally determined contributions and achieve the targets of carbon peak and carbon neutrality on schedule, low-carbon development of China's service industry is an inevitable trend. On the basis of the comprehensive application of multiple spatial analysis methods, the spatiotemporal evolution and dynamic interaction characteristics of carbon intensity in China's service industry from 2005 to 2019 are analyzed from the perspective of spatiotemporal interaction. Combined with quantile regression and the nested model of spatiotemporal transition, the driving mechanism patterns of carbon intensity in China's service industry under different transition types are revealed. The results are as follows: (1) The carbon intensity in China's service industry first increased and then decreased from 2005 to 2019, showing spatially unbalanced characteristics. (2) The kernel density curve demonstrates the dynamic convergence trend of regional differences in the carbon intensity of the service industry. And the carbon intensity also showed a significant spatial agglomeration phenomenon according to the spatial autocorrelation analysis. (3) Based on the evolution analysis of the spatial correlation pattern of carbon intensity in the service industry from 2005 to 2019, a path-locking feature was shown by few spatiotemporal transitions across different types. The eastern coastal region had relatively stable spatial structure and spatial dependence direction, while the central and western regions demonstrated the opposite. (4) The spatiotemporal network pattern of carbon intensity in China's service industry was dominated by positive correlations, although a certain degree of spatiotemporal competition was found between some neighboring provinces. (5) Regional differences existed in the spatiotemporal transition driving patterns of carbon intensity in China's service industry. Specifically, the eastern coastal provinces were mainly influenced by the population-urbanization restriction mode, while most regions in the northwest, southwest, and northeast were mainly influenced by the driving pattern of technology-regulation. From the northwest to the southeast, the spatiotemporal transition patterns showed an evolutionary characteristic of "synthetic development-synthetic restriction". Therefore, the formulation of the emission reduction policies should not only consider various driving/restriction factors but also emphasize differentiated emission reduction measures in China's service industry by combining different types of carbon intensity agglomeration and transition paths, as well as avoiding the regional closure of inter-provincial emission reduction policies through synergistic emission reduction.