[Spatio-temporal Evolution Patterns and Influencing Factors of the Coupling Coordination Effect Between Low-carbon Transportation and New Quality Productive Forces Development： A Case Study of the Yangtze River Economic Belt].

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.