From Spatial Evolution to Low-Carbon Transition: Regional Heterogeneity and Stage Diagnosis of Carbon Emissions Across 19 Urban Agglomerations in China
空間的進化から低炭素移行へ：中国の19都市圏における炭素排出の地域的不均一性と段階診断 (AI 翻訳)
Ye Duan, Minghan Yang, Zhaowei Hou, Hongye Wang, Albert Fekete, Dongge Ning
🤖 gxceed AI 要約
日本語
中国の19都市圏（2006-2023年）を対象に、空間統計と機械学習（ランダムフォレスト、SHAP）を組み合わせ、炭素排出の時空間パターンと要因を分析。産業構造と経済発展が主要因で、5つの発展タイプを特定し、EKC分析により段階別のガバナンス戦略を提案。
English
This study analyzes spatiotemporal carbon emission patterns across 19 Chinese urban agglomerations (2006-2023) using spatial statistics and machine learning (random forest, SHAP). It identifies industrial structure and economic development as key drivers, classifies five development types, and proposes stage-specific governance strategies based on EKC analysis.
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
日本の都市圏や自治体の脱炭素計画に示唆を与える。特に、地域ごとの排出特性に応じた差別化戦略は、日本の地域間格差やSSBJ対応にも応用可能。
In the global GX context
This paper offers a replicable geospatial modeling framework for urban agglomerations, relevant to global low-carbon planning and collaborative governance. Its ML-integrated approach to emission heterogeneity can inform differentiated climate policies in other countries.
👥 読者別の含意
🔬研究者:都市圏の炭素排出分析におけるMLと空間統計の統合手法を学ぶ価値がある。
🏢実務担当者:地域別の排出特性に基づく脱炭素戦略の策定に参考になる。
🏛政策担当者:都市圏の段階別ガバナンス戦略は、地域差を考慮した政策設計に有用。
📄 Abstract（原文）
Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and to establish a type-specific governance framework. Based on multi-source geospatial and socioeconomic data from 19 urban agglomerations for the period 2006–2023, this study integrates spatial autocorrelation analysis, standard deviation ellipse analysis, hotspot analysis, random forest regression with SHAP interpretation, K-medoid clustering, and the Environmental Kuznets Curve (EKC) model to systematically examine emission evolution, influencing factors, and governance pathways. The results indicate the following: (1) carbon emissions in China’s urban agglomerations increased continuously during the study period and exhibited significant spatial heterogeneity, characterized by a “high east–low west” pattern, expanding eastern emission hotspots, and a gradual southwest shift in the emission centroid; (2) industrial structure and economic development level were identified as the dominant factors associated with carbon-emission differences, while energy efficiency, urbanization, and population density showed heterogeneous relationships across regions; (3) five carbon-emission development types were identified, including high-carbon high-development, transition-pressure, resource-dependent, stable-development, and low-carbon potential agglomerations, each exhibiting distinct development characteristics and governance requirements; and (4) EKC analysis revealed differentiated development stages among these types, suggesting that carbon governance should be tailored according to regional development conditions, dominant drivers, and emission-transition stages. This study provides an integrated geospatial modeling framework for understanding carbon-emission heterogeneity and offers scientific support for differentiated low-carbon planning and collaborative governance of urban agglomerations.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/ijgi15080352first seen 2026-08-06 04:55:41
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