CCS or renewables? Power project portfolio investment and low-carbon transition pathways under uncertain carbon trading price
CCSか再生可能エネルギーか？不確実な炭素取引価格下での電源プロジェクトポートフォリオ投資と低炭素移行経路 (AI 翻訳)
Xuqiao Fan, Xiaoxia Huang
🤖 gxceed AI 要約
日本語
中国の電力部門脱炭素化を支援するため、不確実な炭素取引価格と炭素割当制約の下でCCSと再生可能エネルギープロジェクトを選択するポートフォリオ最適化モデルを開発。中国大手電力企業の72の実プロジェクトデータを用い、9つの炭素価格シナリオを分析。炭素価格の成長が技術選択・NPV・排出削減に最も影響し、高成長・低ボラティリティのシナリオで最大NPV（1兆8,259億元）を達成。ベンチマークシナリオでは電力価格変動が技術選択に最も強く影響する。
English
To support China's power sector decarbonization, this study develops an uncertainty-based portfolio optimization model for selecting CCS and renewable projects under uncertain carbon trading prices and quota constraints. Using data from 72 real projects of a major Chinese power enterprise, nine carbon price scenarios are analyzed. Carbon price growth is the dominant factor affecting technology choice, NPV, and emission reduction, while volatility has a smaller effect. Under the benchmark scenario, electricity price fluctuations have the strongest influence on technology choice.
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
日本の電力会社やエネルギー企業にとって、炭素価格の不確実性を考慮した電源投資の意思決定は、GX投資戦略や統合報告書での開示に直結する。SSBJ開示や有報での気候関連リスク分析に応用可能な実証的枠組みを提供。
In the global GX context
This paper provides empirical evidence on how carbon price uncertainty shapes power investment portfolios, relevant for global climate disclosure frameworks (TCFD/ISSB) that require scenario analysis. It offers a quantitative method for companies to assess transition risks and opportunities under different carbon pricing trajectories, useful for transition finance and climate risk modeling.
👥 読者別の含意
🔬研究者:Provides a robust portfolio optimization model under uncertainty, with empirical validation using real project data, useful for energy transition and carbon pricing research.
🏢実務担当者:Offers a decision-support tool for power companies to optimize CCS vs. renewable investments under carbon price uncertainty, aiding capital allocation and climate risk disclosure.
🏛政策担当者:Highlights the critical role of carbon price growth in driving low-carbon technology adoption, informing carbon pricing policy design and power sector transition planning.
📄 Abstract（原文）
To support the decarbonization of China’s power sector, this study develops an uncertainty-based project portfolio optimization model for selecting CCS and renewable energy projects under uncertain carbon trading prices and carbon quota constraints. Carbon trading price, electricity price, fuel cost, and investment cost are modeled as uncertain variables. Using data from 72 real-world projects of a major Chinese power enterprise, nine carbon trading price scenarios with different growth rates and volatility levels are analyzed. The results show that carbon trading price growth is the dominant factor affecting technology choice, NPV, and emission reduction, while volatility has a smaller effect. The high-growth and low-volatility scenario S31 yields the highest expected NPV of RMB 1,825.9 billion, with net CO2 emissions of 1,645 Mt, whereas the low-growth and high-volatility scenario S13 yields the lowest expected NPV of RMB 674.8 billion. The lowest net CO2 emissions occur in S33, at 1,605 Mt. Under the benchmark scenario, electricity price fluctuations have the strongest influence on technology choice, while changes in fuel and investment costs have smaller effects.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/15435075.2026.2711878first seen 2026-08-14 05:00:07
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