中国通州区における温室効果ガス排出インベントリと要因分解に関する研究
A study of the greenhouse gas emissions inventory and driving factors decomposition in Tongzhou, China (原題)
Xiaoyi Hu, Wei Wen, Tao Yang, Xiaoqi Liu, Liyao Shen, Shaorui Wang
🤖 gxceed AI 要約
日本語
北京市通州区を対象に、エネルギー・農業・LUCF・廃棄物を含む地区レベルのGHG排出インベントリを2018〜2020年にわたり構築し、LMDI法で部門別の駆動要因を分解した。排出量は「増加後減少」の軌道をたどり、エネルギー活動が全体の94%以上を占めた。エネルギー強度・排出強度、アフリカ豚熱による畜産縮小、廃棄物焼却・医療廃棄物・下水が主要因として特定され、部門別の差別化戦略の必要性を示す。
English
This study builds a district-level GHG inventory for Tongzhou, Beijing (2018-2020), covering energy, agriculture, LUCF, and waste, and applies LMDI decomposition to identify sectoral drivers. Energy activities dominated (>94% of emissions), while agricultural reductions stemmed from African Swine Fever and waste fluctuations from incineration, medical waste, and wastewater. Findings support differentiated mitigation strategies at the grassroots governance scale.
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
中国の地区・県レベル排出インベントリ手法の事例として、日本の自治体GHG算定やSSBJ開示におけるScope 3・地域データ整備の参考になる。特にLMDIによる要因分解は、日本企業の拠点別脱炭素計画や自治体の実行計画策定に応用可能。
In the global GX context
Adds to the thin literature on sub-national GHG inventories, relevant to global disclosure scholarship on granular data needs for ISSB/CSRD Scope 3 and city-level climate action. The LMDI approach offers a replicable method for district-level mitigation planning beyond national inventories.
👥 読者別の含意
🔬研究者:地区レベルの排出インベントリ構築とLMDI分解の実証例として、サブナショナル気候政策研究に有用。
🏢実務担当者:中国拠点を持つ企業が地域別排出把握やScope 3下流データ整備の参考にできる。
🏛政策担当者:自治体レベルの差別化された緩和戦略立案に、部門別要因分解の手法が示唆を与える。
📄 Abstract（原文）
Abstract District-and county-level greenhouse gas (GHG) inventories remain insufficiently developed, limiting the formulation of targeted mitigation policies at the grassroots governance scale. Taking Tongzhou District, Beijing, as a representative rapidly urbanizing megacity sub-center, this study developed an integrated district-level GHG emissions inventory covering energy activities, agricultural production, LUCF, and waste treatment from 2018 to 2020, and applied the Logarithmic Mean Divisia Index (LMDI) method to identify sector-specific driving mechanisms. The results show that total GHG emissions excluding LUCF followed a “rise-then-decline” trajectory. Energy activities remained the dominant source, accounting for more than 94% of total emissions. The LMDI results revealed heterogeneous sectoral drivers: energy-sector changes were mainly influenced by energy intensity and emission intensity effects; agricultural emission reductions were primarily associated with African Swine Fever-induced contraction of livestock breeding; and waste-sector fluctuations were mainly driven by changes in municipal solid waste incineration, medical waste treatment, and wastewater-related emissions. These findings suggest that district-level mitigation should adopt differentiated strategies across energy, agriculture, and waste sectors.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s41598-026-73432-8first seen 2026-10-10 05:17:01
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