低炭素交通と新質生産力発展の結合協調効果の時空間進化パターンと影響要因：長江経済帯を事例に
[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]. (原題)
Yan-Fei Li, Cheng Li
🤖 gxceed AI 要約
日本語
長江経済帯11省・市の2012〜2022年のパネルデータを用い、低炭素交通と新質生産力の発展の結合協調度を評価。エントロピーTOPSIS法や地理検出器、GTWRモデル等で時空間進化と影響要因を分析。結合協調度は上昇傾向にあり、空間的には「東高西低」の分布を示した。技術革新、デジタル情報発展、公共交通水準などが主要な促進要因である。
English
Using panel data from 11 provinces in the Yangtze River Economic Belt (2012-2022), this study evaluates the coupling coordination between low-carbon transportation and new quality productive forces. Methods include entropy-weighted TOPSIS, coupling coordination model, spatial analysis, and GTWR. Results show rising coordination levels with a spatial pattern of 'high in the east, low in the west'. Key drivers include technological innovation, digital development, and public transport level.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国の地域政策に基づく実証研究であり、日本では直接対応する政策はないが、地域交通の脱炭素化と産業高度化の連携評価手法は、日本の地域別GX戦略やスマートシティ政策の参考になり得る。
In the global GX context
This study provides empirical evidence on regional low-carbon transport and industrial upgrading, relevant to global discussions on just transition and regional decarbonization. The coupling coordination methodology could inform integrated assessment frameworks for transport and economic development in other regions.
👥 読者別の含意
🔬研究者:Provides a methodological framework for assessing coupling coordination between low-carbon transport and economic development, applicable to other regions.
🏢実務担当者:Offers insights for regional transport planners and policymakers on key drivers of low-carbon transport development.
🏛政策担当者:Highlights the importance of technological innovation and digital infrastructure in promoting low-carbon transport, useful for regional policy design.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42670112first seen 2026-09-03 05:10:50
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