Spatial Patterns, Drivers, and Spillover Effects of Carbon Footprint Pressure in Chinese Cities
中国都市における炭素フットプリント圧力の空間パターン、要因、波及効果 (AI 翻訳)
Liyang Feng, Dongzhe Liang, Hui Li, Yanlong Guan
🤖 gxceed AI 要約
日本語
本研究は2000～2019年の中国都市を対象に、植生による炭素固定能力に対する炭素フットプリント圧力(CFP)の空間パターンとその要因を分析。空間計量モデルを用いて、近隣都市からの波及効果を定量化し、炭素排出強度の低下が地域全体のCFP低減に有効であることを示した。都市間連携の重要性を強調。
English
This study analyzes spatial patterns and drivers of carbon footprint pressure (CFP) across Chinese cities from 2000 to 2019, using spatial econometric models. It finds significant spatial spillover effects: a 1% reduction in neighboring cities' carbon emission intensity reduces local CFP by at least 0.8%. Results highlight the need for regional cooperation in urban climate governance.
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
日本の都市でも炭素排出と森林吸収源のバランスは重要だが、本論文は都市間の空間的波及効果を定量化しており、SSBJや自治体の気候変動計画における広域連携の根拠となる。特に、近隣都市の排出削減が自市のCFP低減に寄与する点は、関東圏や関西圏などの都市クラスター政策に応用可能。
In the global GX context
This paper provides empirical evidence of spatial spillover effects in carbon footprint pressure, relevant to global urban climate governance frameworks such as C40 and ICLEI. The finding that neighboring cities' emission reductions significantly lower local pressure supports the case for regional carbon reduction compacts, aligning with ISSB's emphasis on value chain and geographic scope.
👥 読者別の含意
🔬研究者:Spatial econometric approach to carbon footprint pressure and its spillover effects offers a methodology transferable to other urban regions.
🏢実務担当者:City sustainability teams can use the finding that reducing emission intensity in neighboring cities yields local benefits, justifying joint climate projects.
🏛政策担当者:Regional policymakers should consider inter-city spillover effects when designing carbon reduction targets and carbon sink protection policies.
📄 Abstract（原文）
Limiting anthropogenic carbon emissions within vegetation carbon sequestration capacity is essential for urban sustainability. However, previous studies frequently neglected spatial dependency among cities, leading to uncertainties in understanding whether local carbon footprint pressure (CFP) is shaped only by local conditions or also by neighboring cities. This study analyzes the spatial patterns of CFP across Chinese cities from 2000 to 2019 and further investigates its driving factors using cross-sectional data for 2019. Spatial analysis methods, an extended STIRPAT model, and spatial econometric models are combined to diagnose the socioeconomic, environmental, and spatial mechanisms of CFP. Results indicate that the mean center of CFP’s standard deviation ellipse shifted from northwest to southeast during 2000–2019, reflecting a spatial redistribution of carbon-emission pressure on vegetation carbon sequestration. The 2019 driver analysis shows that carbon-emission intensity, used here as an inverse proxy for technology-related emission efficiency, is the most influential factor: a 1% increase in carbon-emission intensity is associated with an approximate 1.2% increase in local CFP. Affluence, vegetation carbon sequestration, precipitation, temperature, and forest cover also shape CFP distribution patterns. Crucially, the study identifies significant spatial spillover effects: a 1% reduction in the carbon-emission intensity of neighboring cities is associated with at least a 0.8% reduction in local CFP. These findings suggest that CFP is jointly shaped by local socioeconomic development, ecological endowments, and inter-city spatial interactions. The study highlights the need to integrate local emission reduction, carbon-sink protection, and regional collaboration into urban climate governance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18146984first seen 2026-07-13 06:14:24
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