An explainable percolation-based clustering framework for China's transport carbon emissions analysis
中国の交通炭素排出分析のための説明可能なパーコレーションベースのクラスタリングフレームワーク (AI 翻訳)
Pengfei Xu, Siqi Jia, Y Cao, Chunpeng Chen, Jianqiang Cui, Nan Xu, Dong Lin, Yifu Ou
🤖 gxceed AI 要約
日本語
本研究は、パーコレーション理論と時空間クラスタリングを統合し、中国323都市の地上交通由来炭素排出（TCE）の空間クラスタを客観的に特定した。ランダムフォレストと解釈可能な機械学習を用いて地域別の排出要因を分析し、長江デルタではGDP、北京・天津・河北では人口と交通、珠江デルタでは道路貨物が主要因であることを明らかにした。
English
This study integrates percolation theory with spatiotemporal clustering to objectively delineate ground transport carbon emission (TCE) clusters for 323 Chinese cities. Using random forest and interpretable ML, it reveals regional heterogeneity: GDP dominates in Yangtze River Delta, population and transport in Beijing-Tianjin-Hebei, road freight in Pearl River Delta, and service sector/temperature in other regions.
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SSBJやスコープ3対応においても、排出の空間的クラスタリング分析は自治体間連携やサプライチェーン排出削減に有用な視点を提供する。特に交通部門の排出実態把握と地域別対策立案に示唆を与える。
In the global GX context
This framework advances global climate disclosure scholarship by demonstrating an objective, data-driven method for spatial emission clustering, relevant for TCFD/ISSB reporting of Scope 3 transport emissions and cross-jurisdiction mitigation strategies.
👥 読者別の含意
🔬研究者:Introduces a novel percolation-based clustering method for emission analysis, with potential for global adaptation.
🏢実務担当者:Provides actionable insights for city-level transport emission reduction strategies and cross-city coordination.
🏛政策担当者:Highlights the need for region-specific emission reduction policies and collaborative frameworks across jurisdictions.
📄 Abstract（原文）
Mitigating carbon emissions requires coordinated actions across jurisdictions, as emissions often exhibit strong spatial-temporal synchronization and interdependencies. Understanding how emissions cluster spatially is therefore essential for designing collaborative mitigation strategies. However, existing studies on the spatial clustering of carbon emissions remain limited by subjective parameter settings and insufficient exploration of heterogeneous driving factors across clusters. Here, we integrate the percolation theory in physics with a spatial-temporal clustering algorithm to objectively delineate clusters of ground-transport-related carbon emissions (TCE) for 323 Chinese cities in 2019. Building on six identified spatial clusters at the regional level, we employ random forest models and interpretable machine learning techniques to examine the effects of various factors on TCE. The results further uncover regional heterogeneity in emission determinants: In the Yangtze River Delta cluster, per capita GDP plays a leading role. In contrast, emissions in the Beijing-Tianjin-Hebei and Central Plains clusters are primarily associated with population scale, transport provision, and intensive passenger mobility. A distinct pattern emerges in the Pearl River Delta, where emissions are overwhelmingly driven by road freight demand. Finally, in the Middle Reaches of the Yangtze River and the northern Jiangsu-Shandong coastal belt clusters, service-sector activity and temperature emerge as key contributing factors. This study contributes to the methodological frontier by introducing a percolation-based spatial clustering framework for emission analysis, which can be generalizable to broader contexts. Furthermore, it provides actionable insights for fostering cross-city coordination in emission mitigation, advancing China's decarbonization targets and sustainable transport goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/15481603.2026.2677263first seen 2026-06-17 05:48:24 · last seen 2026-06-17 07:14:08
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