| 低碳交通与新质生产力发展耦合协调效应时空演变格局及影响因素：以长江经济带为例 |
| 摘要点击 1095 全文点击 17 投稿时间：2025-06-10 修订日期：2025-08-23 |
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| 中文关键词 新质生产力 低碳交通 时空演变趋势 耦合协调度 影响因素 |
| 英文关键词 new quality productive forces low-carbon transportation spatio-temporal evolution trends coupling coordination degree influencing factors |
| DOI 10.13227/j.hjkx.202506115 |
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| 中文摘要 |
| 协同推进低碳交通与新质生产力发展对长江经济带交通业绿色发展和促进经济高质量发展具有重要意义. 探讨长江经济带低碳交通与新质生产力发展水平之间的耦合关系，有助于为交通高质量发展提供理论支持与实践依据. 选取长江经济带2012~2022年11个省市数据，运用熵权TOPSIS法、修正的耦合协调度模型、探索性空间分析、核密度估计、地理探测器模型和时空地理加权回归模型等方法，研究了低碳交通与新质生产力发展水平耦合协调效应的时空演变格局以及影响因素. 结果表明：①长江经济带各省市低碳交通与新质生产力发展水平耦合协调度呈现逐年上升趋势，整体由磨合协调阶段到初级协调阶段，协调等级由以轻度失调和临界协调类型为主转变为以初级协调和中级协调类型为主. 空间分布不均，总体呈现“东高西低”的特点. ②由空间关联性分析可知，耦合协调度存在显著的空间集聚特征，上游地区处于“低-低”聚类状态，下游地区长期处于“高-高”聚类状态，显示出空间分布的不平衡性. ③影响因素分析中，科技创新水平、信息数字化、公共交通水平、经济发展水平、研发人力投入和研发经费投入是关键的影响因素，且各因素影响存在明显的时空异质性. |
| 英文摘要 |
| The coordinated promotion of low-carbon transportation and the development of new quality productive forces is of great significance for advancing the green transformation of the transportation industry and promoting high-quality economic growth in the Yangtze River Economic Belt. Exploring the coupling relationship between low-carbon transportation and the development level of new quality productive forces contributes to providing both theoretical support and practical reference for high-quality transportation development. Based on panel data of 11 provinces and municipalities in the Yangtze River Economic Belt from 2012 to 2022， this study employs the entropy-weighted TOPSIS method， a modified coupling coordination degree model， exploratory spatial data analysis， kernel density estimation， the geographical detector model， and the spatio-temporal geographically weighted regression （GTWR） model to examine the spatio-temporal evolution patterns and influencing factors of the coupling coordination effect between low-carbon transportation and new quality productive forces. The results show that： ① The coupling coordination degree exhibited a continuous upward trend， evolving overall from the running-in coordination stage to the primary coordination stage. The dominant coordination types shifted from mild imbalance and marginal coordination to primary and intermediate coordination， with a spatial distribution characterized by “high in the east and low in the west.” ② Spatial autocorrelation analysis revealed a significant spatial clustering pattern： Upstream regions were mainly in a “low-low” cluster， while downstream regions remained in a “high-high” cluster， indicating spatial imbalance. ③ In terms of influencing factors， technological innovation， digital information development， public transportation level， economic development level， R&D human input， and R&D expenditure were identified as key drivers， with significant spatio-temporal heterogeneity in their impacts. |
