| 中国交通碳排放关联网络的时空动力学与驱动机制 |
| 摘要点击 1915 全文点击 103 投稿时间：2024-11-27 修订日期：2025-03-09 |
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| 中文关键词 交通碳排放 时空动态变化 探索性时空数据分析（ESTDA） 多元回归二次指派程序（MRQAP） 驱动机制 |
| 英文关键词 transportation carbon emission spatial and temporal dynamics exploratory spatiotemporal data analysis（ESTDA） multiple regression quadratic assignment procedure（MRQAP）model driving mechanism |
| DOI 10.13227/j.hjkx.202411298 |
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| 中文摘要 |
| 研究中国省域交通碳排放关联网络的时空依赖特征与格局及其驱动机制，对促进省域间交通碳减排与区域高质量减排协同发展具有重要意义. 基于二次指派程序、社会网络分析以及探索性时空数据分析并结合多元回归二次指派程序模型探讨2003~2021年中国交通碳排放关联网络的时空动力演化交互特征与驱动机制. 结果表明：①2003~2021年中国交通碳排放关联网络结构与强度相似度高，连接模式存在“时间惯性”，未来关联模式受历史关联状态影响明显. ②中国交通碳排放关联网络的空间连接偏好特征明显，空间异质性突出，集聚分布日趋明显，山东、江苏、广东与上海等核心省域主导现象突出. ③在时空交互维度上交通碳排放锁定效应与跃迁惰性突出，研究期间内省域间协同合作关系高达84.6%，但西南和北部省域间时空竞争关系突出. ④交通碳排放关联网络的驱动机制呈现出“结构锁定-时空依赖-个体属性多样性”的特点，其中经济差异矩阵与时空交互网络对其正向影响最为显著，产业差异矩阵与运输结构差异矩阵产生同配效应的负向影响最为突出. 因此建议各省从区域间协调治理、差异化减碳政策以及交通网络布局这3个方面推动区域交通碳减排目标优化与协同发展. |
| 英文摘要 |
| Understanding the spatiotemporal dependence characteristics， patterns， and driving mechanisms of the interprovincial transportation carbon emission network in China is of paramount importance for advancing interprovincial transport carbon reduction and fostering regional high-quality emission reduction coordination. Based on the quadratic assignment procedure （QAP）， social network analysis （SNA）， and exploratory spatiotemporal data analysis （ESTDA）， this research was combined with the multiple regression quadratic assignment procedure （MRQAP） model to explore the spatio-temporal dynamic evolution interaction characteristics and driving mechanism of China's transport carbon emission correlation network from 2003 to 2021. The results indicated that： ① The structure and intensity of China's transportation carbon emission correlation network from 2003 to 2021 exhibited a high degree of similarity， demonstrating a “time inertia” in which the future correlation pattern was significantly influenced by historical correlation trends. ② The transportation carbon emission correlation network showed distinct spatial connection preferences， with pronounced spatial heterogeneity and increasingly evident clustering patterns. Core provinces such as Shandong， Jiangsu， Guangdong， and Shanghai exhibited a dominant role in this network. ③ The lock-in effect and transition inertia of transport carbon emissions were prominent in the spatio-temporal interaction dimension. The inter-provincial cooperative relationship was as high as 84.6% during the study period， but the spatio-temporal competition relationship between southwest and northern provinces was prominent. ④ The driving mechanism of the transport carbon emission correlation network presented the characteristics of “structure-lock-spatiotemporal dependence-diversity of individual attributes，” in which the economic difference matrix and spatio-temporal interaction network had the most significant positive influence， and the industrial difference matrix and transport structure difference matrix had the most prominent negative influence. Therefore， it is suggested that provinces should promote the optimization and coordinated development of regional transport carbon reduction targets from three aspects： inter-regional coordination governance， differentiated carbon reduction policies， and transportation network layout. |
