汚染削減と炭素排出削減の二重制約下における江蘇省電力産業のクリーン・低炭素転換経路
[Clean and Low-carbon Transformation Path of the Electric Power Industry in Jiangsu Province Under the Dual Constraints of Pollution Reduction and Carbon Emission Reduction]. (原題)
Bo Sun, Ye-Shun Zhang, Min-Min Teng
🤖 gxceed AI 要約
日本語
江蘇省の電力産業を対象に、2025〜2060年を期間として、汚染削減と炭素排出削減の二重制約下で総コスト最小化を目的とするゴールプログラミングモデルを構築。BAU、STE1、STE2の3シナリオで電源構成を最適化し、石炭火力の段階的廃止とCCSコストの逆相関、クリーンエネルギー比率と系統安定性のトレードオフなどを定量的に示した。
English
This study optimizes the clean and low-carbon transition path for Jiangsu's power industry (2025-2060) using a goal programming model that minimizes total electricity supply cost under dual constraints of pollution and carbon reduction. Across BAU, STE1, and STE2 scenarios, it reveals trade-offs between coal phase-out speed and carbon capture costs, and between clean energy share and grid stability, providing quantitative references for regional power decarbonization planning.
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
中国の省レベル電力計画の事例であり、日本の地域エネルギー計画や電力自由化下での電源構成最適化に示唆を与える。SSBJ対応のScope 2排出削減目標設定や、地域特性を踏まえたトランジション戦略の参考になり得る。
In the global GX context
Provides a provincial-level modeling framework for power sector decarbonization that aligns with global climate targets. The scenario analysis on coal phase-out and CCS costs offers insights for regions balancing energy security and decarbonization, relevant to TCFD/ISSB-aligned transition planning and power sector investors.
👥 読者別の含意
🔬研究者:電力部門の費用最小化モデルとシナリオ分析の方法論を参照できる。
🏢実務担当者:電源構成の長期計画やScope 2排出削減戦略の検討に示唆を得られる。
🏛政策担当者:地域の電力脱炭素政策の設計におけるコストと安定性のトレードオフを考慮する際の参考になる。
📄 Abstract(原文)
Against the backdrop of rapid economic development, Jiangsu Province is confronted with the dual strategic tasks of enhancing ecological environment quality and achieving carbon peaking and carbon neutrality. As a major source of carbon and pollutant emissions, the clean and low-carbon transformation of the power industry is of critical significance. Jiangsu Province is taken as the research object in this study, with the time frame spanning from 2025 to 2060. Under the baseline policy scenario (BAU) and two policy strengthening scenarios (STE1 and STE2), a goal programming model is constructed, with the minimization of the total cost of the electricity supply as the objective, while the dual constraints of pollution reduction and carbon emission reduction are satisfied. Through the optimization of the power generation structure, the optimal transformation path for the power industry is explored. The results are as follows: ① The evolution of power supply costs under different scenarios was characterized by significant differences. The total cost was the lowest in the BAU scenario, while it was the highest in the STE2 scenario, mainly due to high terminal costs and a slow reduction process. The cost was high in the early stage but dropped in the later stage in the STE1 scenario, with a high proportion of clean energy. ② The characteristics of differences in the energy structure were prominent. In the BAU scenario, the proportion of coal power gradually decreased to 2.4%, forming a pattern of coordinated transition between traditional and clean energy; in the STE1 scenario, the proportion of clean energy exceeded 65%, but the system stability faced challenges; and in the STE2 scenario, coal power was retained at 23.8%, which could maintain power supply resilience, but the proportion of clean energy was relatively low. ③ The speed of coal power exit and the cost of carbon capture showed an inverse correlation. In the BAU scenario, coal power was gradually phased out, and the cost of carbon capture was moderate; in the STE1 scenario, coal power was rapidly phased out, and the cost of carbon capture was basically zero; and in the STE2 scenario, the exit of coal power was delayed, and the cost of carbon capture was the highest. Scientific references for promoting the clean and low-carbon transformation of the power industry in Jiangsu Province can be provided by the research results.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42670113first seen 2026-09-03 05:09:19
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