Game Strategy for Low-Carbon Investment by Electric Power Enterprises under the Dual Drivers of Carbon Quota Mechanism and Blockchain Technology
炭素割当メカニズムとブロックチェーン技術の二重駆動下における電力企業の低炭素投資のゲーム戦略 (AI 翻訳)
C. N. He, H. D. Chen, H. J. Tian, J. Zhang, L. Zhang
🤖 gxceed AI 要約
日本語
本研究は、炭素割当メカニズム（CQM）とブロックチェーン技術（BCT）の下での電力サプライチェーンの低炭素投資戦略を分析。発電企業と小売企業の2層システムをモデル化し、祖父方式（GFM）とベンチマーク方式（BMM）の下でのブロックチェーン導入の4つのシナリオをStackelbergゲームで比較。BMMはGFMよりも高い低炭素投資と需要を誘発し、ブロックチェーンの情報追跡はコストが閾値以下であれば消費者のグリーン信頼を高め、需要と収益性を向上させることを示した。
English
This study analyzes low-carbon investment strategies in power supply chains under carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon model of a power generator and retailer is examined via Stackelberg game across four scenarios (grandfathering vs. benchmarking, with/without blockchain). Results show benchmarking consistently outperforms grandfathering in investment and demand, and blockchain's traceability enhances consumer green trust and profitability when implementation costs are below a threshold. Sensitivity analysis confirms positive effects of green trust and low-carbon preference, while excessive costs weaken benefits.
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
日本の電力市場では、カーボンプライシングや排出量取引の本格導入が検討されており、本モデルは割当方式の設計（祖父方式 vs ベンチマーク方式）が投資行動に与える影響を定量的に示す。ブロックチェーンによる情報開示は、SSBJや有報での非財務情報開示の信頼性向上にも示唆を与える。
In the global GX context
This paper contributes to global discourse on carbon pricing design and digital enablers for decarbonization. It provides a quantitative framework comparing grandfathering vs. benchmarking allocation methods, relevant for ETS design worldwide. The blockchain traceability findings speak to the credibility of carbon claims and align with ISSB disclosure requirements for supply-chain emissions.
👥 読者別の含意
🔬研究者:Provides a game-theoretic model linking carbon quota allocation methods and blockchain adoption to investment and demand, offering a testable framework for empirical studies.
🏢実務担当者:Highlights that benchmarking-based quotas and cost-effective blockchain traceability can boost low-carbon investment and consumer trust, informing technology adoption and compliance strategies.
🏛政策担当者:Suggests that benchmarking allocation may be more effective than grandfathering in stimulating low-carbon investment, and that blockchain infrastructure can enhance market transparency.
📄 Abstract（原文）
This study investigates low-carbon investment strategies in power supply chains under the combined influence of carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon system consisting of a power generator and an electricity retailer is modeled, and four decision scenarios are constructed by considering blockchain adoption under both the grandfathering method (GFM) and benchmarking method (BMM). A Stackelberg game framework is employed to analyze the interactions among low-carbon technology investment, low-carbon electricity promotion, market demand, and enterprise profitability. Results show that the BMM consistently induces higher low-carbon investment levels, stronger market demand, and greater retailer profitability than the GFM, regardless of blockchain adoption. Furthermore, blockchain-enabled information traceability exhibits a significant threshold effect: when implementation costs remain below a critical level, trusted information transmission enhances consumer green trust, stimulates demand for low-carbon electricity, and improves the economic performance of supply-chain participants. Sensitivity analysis further demonstrates that consumer green trust, low-carbon preference, and responsiveness to low-carbon promotion positively influence both emissionreduction efforts and enterprise profitability, whereas excessive blockchain deployment costs weaken these benefits. The proposed framework provides a quantitative methodology for analyzing information-enabled lowcarbon decision making and coordinated investment strategies in modern power systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.7716/aem.v15i3.3322first seen 2026-08-15 04:51:53
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