1.5℃全球温暖化レベル下における中国の座礁石炭火力資産の予測
Projection of stranded coal power assets in China under the 1.5 °C global warming level (原題)
Bo Li, Yiping Zeng, Bo Yan, Mingyu Li, Jianxiang Shen, Wenjia Cai, Chi Zhang
🤖 gxceed AI 要約
日本語
中国の30省・3652基の石炭火力ユニットを対象に、モンテカルロシミュレーションを用いた多基準評価で早期廃止の優先順位を決定。4つの緩和シナリオ下で座礁資産を評価し、遼寧・四川・江西が早期廃止候補、新疆と華北が高リスク、東北が移行障壁に直面すると示した。地域差別化政策と研究開発投資への示唆を提供。
English
This study evaluates stranded assets from early coal plant retirements in China using a Monte Carlo multi-criteria framework on 3652 units across 30 provinces. It identifies Liaoning, Sichuan, and Jiangxi as early retirement priorities, with Xinjiang and North China at highest risk. The findings support differentiated regional policies and targeted R&D for an equitable energy transition.
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
日本の石炭火力政策やエネルギー移行戦略に示唆を与える。特に、座礁資産リスクの定量化手法は、日本の電力会社や投資家が石炭関連資産のリスク評価を行う際に参考になる。また、地域差別化政策の重要性は、日本の地域エネルギー計画にも応用可能。
In the global GX context
This paper contributes to global discourse on stranded assets and just transition, offering a robust quantitative framework applicable to other coal-dependent economies. It aligns with global climate targets and provides insights for investors and policymakers assessing transition risks in fossil fuel assets.
👥 読者別の含意
🔬研究者:Provides a novel Monte Carlo-based multi-criteria framework for assessing stranded assets, useful for energy transition and climate finance research.
🏢実務担当者:Offers a methodology for evaluating coal asset retirement priorities and stranded asset risks, relevant for utilities and investors with coal exposure.
🏛政策担当者:Highlights regional disparities in stranded asset risks, informing differentiated policies for coal phase-out and just transition planning.
📄 Abstract（原文）
Accurately quantifying stranded assets from early coal plant retirements is essential for shaping China's coal phase-out strategy under global carbon targets. This study compiles an updated database of 3652 coal-fired units in 30 provinces. It introduces a Monte Carlo simulation multi-criteria framework to evaluate retirement priorities, incorporating technical, economic, and environmental indicators. Unlike previous studies that relied on subjective weights, this study uses Monte Carlo simulation to construct a statistically robust multicriteria framework that eliminates expert bias. Using the overnight capital cost method, we assess stranded assets under four mitigation scenarios (S1074-S1374) from 2022 to 2060. Results show that: (1) Liaoning, Sichuan, and Jiangxi could be prioritized for early retirement; (2) Xinjiang and North China face the highest stranded asset risks; and (3) Northeast China may face more significant transition barriers. The findings support targeted R&D investments and differentiated regional policies for prudent planning of additional coal-fired units to facilitate an equitable and efficient energy transition.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.1016/j.apenergy.2026.127940first seen 2026-08-31 00:18:27
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