Identifying Critical Drivers of Transportation Carbon Emissions: An Integrated DEMATEL-Random Forest Approach
輸送部門の炭素排出の主要因の特定：統合的DEMATEL-ランダムフォレストアプローチ (AI 翻訳)
Jiachen Shou, Waner Li, Hui Li, Yanfei Zhang, Martin Skitmore, Wanru Wang, Wenbin Yao, Chunqin Zhang
🤖 gxceed AI 要約
日本語
本研究は、中国30省の輸送部門における炭素排出の主要因を特定するため、ランダムフォレストとDEMATEL手法を統合的に使用。1997年～2022年のデータに基づき、研究開発費、科学技術成果登録数、エネルギー消費量が重要な因子であることを発見。また、同じ因子でも省によって因果特性が異なり、地域差を反映している。
English
This study integrates Random Forest and DEMATEL to identify key drivers of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Findings show that R&D expenditure, registered scientific achievements, and energy consumption are crucial, with significant regional heterogeneity in causal attributes.
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
中国を対象とした研究だが、日本でも運輸部門の脱炭素政策の策定において、AIを活用した要因分析手法が参考になる。特に、都道府県別の特性を考慮したきめ細かな対策の重要性を示唆している。
In the global GX context
While focused on China, the hybrid ML-DEMATEL framework offers a replicable methodology for identifying heterogeneous drivers of transportation emissions globally, relevant for countries designing differentiated decarbonization policies.
👥 読者別の含意
🔬研究者:Demonstrates an effective combination of random forest and DEMATEL for causal analysis of emission drivers, applicable to other sectors and regions.
🏢実務担当者:Provides insights into key factors (R&D, energy consumption) that can inform corporate carbon reduction strategies in the transport sector.
🏛政策担当者:Highlights the need for region-specific policies due to spatial heterogeneity in emission drivers, supporting targeted mitigation measures.
📄 Abstract（原文）
The significant impact of greenhouse gases on global warming has drawn widespread attention. This study focuses on the development of the transportation sector and energy consumption across 30 provinces in China from 1997 to 2022, aiming to identify the key drivers of carbon emissions in China’s transportation sector and analyze their causal interactions and spatial heterogeneity. Initially, provincial carbon emissions are estimated based on reallocated energy consumption data. A random forest model is then employed to objectively screen key factors from multidimensional variables. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is utilized to reveal the interaction network among these factors, distinguish their causal attributes, and explore their inter-provincial spatial differentiation. The findings are as follows: (1) Expenditure on research and experimental development, Number of registered scientific and technological achievements, and Total energy consumption are the most crucial factors influencing emissions; (2) Total energy consumption, Green coverage rate of built-up area, and Urbanization level serve as the primary causal drivers within the system; (3) The same factor exhibits significant variations in causal attributes across different provinces, reflecting regional heterogeneity in development stages. This study provides empirical evidence and methodological support for formulating differentiated and precise traffic carbon reduction policies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/2071-1050/18/3/1508/pdffirst seen 2026-07-25 06:02:15
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