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Published on: January 20, 2023
[Spatio-temporal Evolution Patterns and Influencing Factors of the Coupling Coordination Effect Between Low-carbon
1College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
Abstract:
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.
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