中国6地域における炭素排出量の多シナリオ予測：駆動要因の分析とデカップリング状態の評価
Multi-scenario prediction of carbon emissions in China's six regions: Driving factor investigation and decoupling state analysis (原題)
Feifan Guo, Qixin Hu, Yupeng Zhang, Xi Chen, Chunjing Liu, Jianyi Lu
🤖 gxceed AI 要約
日本語
中国30省を6地域に分け、2000〜2023年の炭素排出をLMDIで要因分解し、Tapioデカップリング指標で経済成長との関係を評価。STIRPAT・リッジ回帰と3つの政策シナリオで2024〜2038年の排出軌道を予測する。経済成長と産業発展が排出の主要な増加要因、エネルギー強度や人口効率が抑制要因で、地域差が大きい。大半の地域で弱いデカップリングが過半を占め、低炭素シナリオでは複数省が早期に炭素ピークに到達する。
English
This study decomposes carbon emissions across 30 Chinese provinces grouped into six regions (2000–2023) using LMDI, and assesses decoupling from economic growth via the Tapio model. A STIRPAT-ridge regression projects emissions to 2038 under baseline, intensified, and low-carbon scenarios. Economic growth and industrialization drive emissions up, while energy intensity and population efficiency suppress them, with strong regional heterogeneity. Weak decoupling dominates, and several provinces peak earlier under the low-carbon scenario.
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
中国の地域別脱炭素ガバナンスとシナリオ予測手法は、日本のSSBJ開示や自治体・企業のScope 3・削減目標設定における地域別バックキャスティングの参考になる。
In the global GX context
Offers a replicable regional decomposition-plus-scenario forecasting template relevant to ISSB/CSRD-aligned transition planning and national/regional decarbonization target-setting beyond China.
👥 読者別の含意
🔬研究者:LMDI・Tapio・STIRPATを組み合わせた地域別排出予測の実証例として参照できる。
🏢実務担当者:地域別の排出ドライバーとシナリオ感度を自社の拠点別削減目標・移行計画の前提に活用できる。
🏛政策担当者:地域別・省別の炭素ピーク時期の差を踏まえた差別化された削減政策設計の根拠になる。
📄 Abstract（原文）
Against the backdrop of global climate change and China's dual-carbon goals, identifying regional carbon-emission (CE) patterns is critical for implementing targeted emission-reduction governance. This study takes 30 provincial-level administrative units in China, which are divided into six geographic regions, and sets six influencing factors to investigate CE characteristics during 2000–2023. The LMDI model is adopted to decompose six driving factors of CE, and the Tapio decoupling model is combined to evaluate the decoupling relationship between CE and economic growth. The STIRPAT-ridge regression model is constructed, and three policy scenarios including baseline (S1), intensified (S2), and low-carbon (S3) are established to predict CE trajectories for 2024–2038 under corresponding scenario assumptions. The results show that economic growth and industrial development are the major positive drivers of CE, while energy intensity, population efficiency and urbanization-related factors exert significant inhibitory effects, with distinct heterogeneity across the six regions. Weak decoupling accounts for more than 50% of all observed decoupling samples in each region, and the six regions shift toward a coupling state as a whole in 2023. Under given scenario assumptions, compared with the S1 baseline scenario, the average CE of Northwest China decreases by 5.41% under the S2 scenario and by 10.41% under the S3 scenario. Jilin and Zhejiang achieve provincial-level carbon peaks in 2023 under scenario assumptions; five provinces reach provincial-level carbon peaks before 2024 under the S3 low-carbon scenario.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.1080/15567249.2026.2751044first seen 2026-10-25 23:52:50
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