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カーシェアリングシステムにおける動的価格設定と配車 (AI 翻訳)

Li, Rui

Figshareデータセット2025-11-03#政策Origin: CN
DOI: 10.6084/m9.figshare.30513833
原典: https://doi.org/10.6084/m9.figshare.30513833

🤖 gxceed AI 要約

日本語

中国285都市のパネルデータとSLX-GTWRモデルを用い、都市の産業集積（IA）が炭素排出に与える影響を分析。直接効果より空間スピルオーバー効果が支配的で、地域間の連関が重要。効果は時空間的に不均一で、地域によって排出削減と増加が混在し、125都市では排出削減に寄与。都市計画と空間経済構造の低炭素移行への重要性を示す。

English

Using panel data from 285 Chinese cities and the SLX-GTWR model, this study finds that urban industrial agglomeration (IA) significantly affects carbon emissions, with spatial spillovers dominating total effects. Direct and spillover effects exhibit pronounced spatio-temporal heterogeneity: IA increases emissions in northeast and southwest regions but reduces them in central and northwest China. Optimized industrial spatial structure contributes to emission reductions in 125 cities, highlighting the role of urban planning and inter-city linkages for climate mitigation and energy transition.

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

日本の都市計画や地域間連携（例：広域連携、国土形成計画）に対する示唆に富む。中国の実証だが、都市の産業構造と排出のスピルオーバー関係は日本でも参考になり、SSBJ対応を含む地域の脱炭素戦略立案に示唆を与える。

In the global GX context

This paper contributes to global urban climate policy literature by quantifying spatial spillover effects of industrial agglomeration on carbon emissions, using a novel spatio-temporal model. It provides policy-relevant evidence for low-carbon urban planning and inter-city coordination, relevant to countries with polycentric urban systems and to climate disclosure frameworks that emphasize location-based risk.

👥 読者別の含意

🔬研究者:Spatial econometrics approach (SLX-GTWR) offers a method to analyze spatio-temporal heterogeneity in emissions determinants.

🏢実務担当者:Urban planners and regional development agencies can use insights on industry spatial structure to design low-carbon land-use policies.

🏛政策担当者:Evidence that emission reductions can be offset by spillovers underscores the need for inter-city cooperation in climate policy.

📄 Abstract（原文）

Understanding how urban spatial structure shapes carbon emissions is critical for climate mitigation and sustainable energy transition. This study examines the impact of urban industrial spatial structure on carbon emissions, using industrial agglomeration (IA) as a proxy for urban spatial organization and land-use configuration. A unified framework integrating spatial spillover effects and spatio-temporal heterogeneity is developed based on the SLX-GTWR model and applied to panel data from 285 Chinese cities. The results show that IA significantly affects carbon emissions, with total effects largely driven by spatial spillovers, highlighting the importance of inter-city linkages. Both direct and spillover effects exhibit pronounced spatio-temporal heterogeneity. Temporally, the impact follows nonlinear patterns and presents an “inverse relationship”, where local emission reductions are often offset by increases in neighboring areas. Spatially, IA increases emissions in northeast and southwest regions but reduces them in central and northwest China. Overall, optimized urban industrial spatial structure contributes to emission reductions in 125 cities. These findings demonstrate the importance of urban planning and spatial economic organization in shaping carbon emissions, and provide policy-relevant insights for climate-oriented urban planning and low-carbon energy transition.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。