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拡張STIRPATモデルによる都市炭素ピーク予測：中国亳州市のシナリオ分析

Predicting Urban Carbon Peak Through an Extended STIRPAT Model: A Scenario Analysis of Bozhou, China (原題)

Shu-Guang Zhu, Chang-Qing Wang, Yong-Chang Shen, Zheng Guo, Yun Wan, Ying Qin, Xue Li-pin, Yu-Qi Feng, Bai Sun

Buildings📚 査読済 / ジャーナル2026-11-08#政策Origin: CN
DOI: 10.3390/buildings16214458
原典: https://doi.org/10.3390/buildings16214458

🤖 gxceed AI 要約

日本語

中国亳州市を対象に、9指標を用いた拡張STIRPATモデルで都市炭素排出を分析。グレー関連度分析とリッジ回帰で多重共線性に対処し、2023〜2035年の10シナリオを構築した。ベースラインでは2030年に最大16.78Mt CO2に達する一方、統合シナリオS9・S10では新たなピークなしに減少し、2030年時点で最大38%削減と試算された。

English

An extended STIRPAT model with nine socioeconomic indicators projects urban carbon emissions in Bozhou, China, using grey relational analysis and ridge regression on 2010–2022 data. Across ten 2023–2035 scenarios, the baseline peaks at 16.78 Mt CO2 in 2030, while integrated scenarios S9/S10 decline without a new peak, cutting 2030 emissions by up to 38%. Monte Carlo analysis illustrates sensitivity to growth-rate uncertainty.

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

中国地方都市の炭素ピーク経路分析は、日本の自治体・企業がScope 3や地域脱炭素計画を策定する際のシナリオ設計手法として参考になる。ただしSSBJや有報開示との直接接続はない。

In the global GX context

Adds city-level empirical scenario evidence to the global stocktake of subnational decarbonization pathways, relevant to ISSB/CSRD-adjacent transition planning where municipal data gaps persist. Methodologically modest but useful for prefecture-level cities with limited data.

👥 読者別の含意

🔬研究者:STIRPAT拡張とリッジ回帰・グレー関連度を組み合わせた都市レベルシナリオ手法の応用例として参照できる。

🏢実務担当者:地域拠点の排出シナリオ設計や自治体との脱炭素計画協議の基礎資料になりうる。

🏛政策担当者:データ制約のある地方都市でもシナリオ分析で炭素ピーク経路を検討できることを示す。

📄 Abstract（原文）

Cities play an important role in achieving carbon emission peaking and neutrality targets, yet identifying feasible mitigation pathways remains challenging for prefecture-level cities with limited historical data. This study develops a context-specific extended STIRPAT framework to examine the relationships between urban carbon emissions and nine socioeconomic and sector-related indicators in Bozhou, China. Based on annual data from 2010 to 2022, grey relational analysis was used to support indicator selection, and ridge regression was applied to address severe multicollinearity. Ten scenarios, comprising one baseline and nine alternative development pathways, were constructed to assess carbon emission trajectories during 2023–2035. Population exhibited the largest standardized ridge regression coefficient among the selected explanatory variables. Scenario analysis indicates that carbon emissions under the baseline scenario (S1) reach a projected maximum of 16.78 Mt CO2 in 2030. The energy consumption adjustment scenario (S6) and the coordinated development scenario (S8) reach their projected maxima in 2025, at 15.60 and 15.69 Mt CO2, respectively. Under the integrated scenarios S9 and S10, emissions decline continuously throughout 2023–2035 without reaching a new peak during the projection period. Compared with S1, projected emissions in 2030 are 10.65%, 9.06%, 24.74%, and 37.99% lower under S6, S8, S9, and S10, respectively. A supplementary Monte Carlo analysis further illustrates the sensitivity of these projections to uncertainty in the assumed explanatory-variable growth rates. The findings should be interpreted as conditional scenario outcomes rather than precise forecasts or causal estimates of policy effectiveness.

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