一般化ディビジア指数分解法を用いた成渝都市圏の炭素排出要因分解
Factor decomposition of carbon emissions in the chengdu-chongqing metropolitan area using the generalized divisia index decomposition method (原題)
You-Wei Wan, Su-Qian Nie, Xiao-Wei Song, Jia-Kai Wen
🤖 gxceed AI 要約
日本語
GDIM（一般化ディビジア指数分解法）を用い、成渝都市圏の2014〜2023年の炭素排出データを要因分解した研究。人口増加と経済成長、総エネルギー消費が排出の駆動要因となる一方、エネルギー強度や単位GRP当たりエネルギー消費は抑制要因として働くことを示す。重慶と成都で一人当たり排出の寄与方向が逆転する点も明らかにし、都市間のエネルギー消費構造と都市化水準の差に起因すると論じる。
English
This study applies the Generalized Divisia Index Decomposition Method (GDIM) to 2014–2023 carbon emission data for the Chengdu-Chongqing metropolitan area. Population growth, economic expansion, and total energy consumption drive emissions, while energy intensity and energy use per unit of GRP suppress them. Per-capita emissions act as a driver in Chongqing but a restraint in Chengdu, reflecting differing energy and urbanization patterns.
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
中国内陸部の都市圏レベルでの排出要因分析であり、日本のSSBJ・有報開示や企業のScope算定に直接資するものではない。ただし地域単位の排出構造を分解する手法は、日本の自治体・地域脱炭素計画の要因分析に応用可能な参考例となる。
In the global GX context
Adds city-cluster-level decomposition evidence from China's inland growth region, complementing national and firm-level analyses that dominate ISSB/TCFD disclosure scholarship. The GDIM approach offers a transferable method for subnational decarbonization diagnostics relevant to regional transition planning.
👥 読者別の含意
🔬研究者:GDIMによる地域排出要因分解の実証例として、都市間比較の手法設計に参考になる。
🏛政策担当者:地域単位の排出駆動・抑制要因の特定は、地方自治体の脱炭素計画立案に示唆を与える。
📄 Abstract（原文）
Using the Generalized Divisia Index Decomposition Method (GDIM) and focusing on the Chengdu-Chongqing metropolitan area, this study analyzes carbon emission data from 2014 to 2023 to identify the primary factors influencing carbon emissions in the region and quantify the contribution of each factor. The results indicate that: (1) Population growth generally drives carbon emissions; however, per capita carbon emissions exhibit differing patterns between the two cities. In Chongqing, per capita carbon emissions act as a driver, whereas in Chengdu, they act as a restraining factor. This discrepancy stems from differences in energy consumption patterns and urbanization levels between the two cities. (2) Regarding energy consumption factors, the decomposition results show that total energy consumption has a driving effect, while energy intensity and energy consumption per unit of GRP have a suppressing effect. The driving effect of total energy consumption nearly offsets the suppressing effects of energy intensity and energy consumption per unit of GRP; therefore, total energy consumption plays a key role in carbon emissions. (3) Regarding economic scale factors, economic growth has a significant driving effect on carbon emissions in the Chengdu-Chongqing metropolitan area. In recent years, particularly during the 2021–2023 period, as the economic growth rate of the region has slowed, the inhibitory effects of per capita economic output and carbon emission intensity have become relatively more pronounced compared to previous periods.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1962372/pdffirst seen 2026-10-24 05:22:08 · last seen 2026-11-06 05:51:12
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。