A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth
AIデータセンター負荷増大下における炭素規制と二次利用電池投資のスタッケルベルク・ベイジアン容量市場ゲーム (AI 翻訳)
Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi, Marcus Chen I Wada, Wencong Su
🤖 gxceed AI 要約
日本語
AIデータセンターの電力需要急増が米国の系統コストと炭素排出を押し上げる中、規制者・ISO・投資家の3層ゲームを構築。炭素税・再生可能エネルギー補助金・二次利用電池補助金が投資構成と排出に与える影響を定量化し、費用対効果の高い政策を比較する。AI負荷増を明示的にモデル化し、AI×ESGの交差を示す。
English
This study models AI data-center load growth in a three-level Stackelberg-Bayesian game among regulator, ISO, and investors. It quantifies how carbon taxes and subsidies for renewables and second-life batteries reshape investment mix, emissions, and profits, comparing policy cost-effectiveness. Explicitly links AI-driven demand to carbon regulation and storage investment.
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
日本ではAIデータセンターの電力需要増加が予想され、容量市場や炭素価格制度の設計に示唆を与える。SSBJ開示やGX経済移行債との関連で、政策立案者や電力会社の投資判断に有用。
In the global GX context
Globally, this addresses the intersection of AI-driven electricity demand and carbon regulation, relevant to capacity market design and clean energy investment. Provides a framework for policymakers balancing reliability, decarbonization, and cost, applicable to ISSB/TCFD-aligned transition planning.
👥 読者別の含意
🔬研究者:Provides a game-theoretic framework linking AI load growth, carbon pricing, and storage investment, useful for energy policy modeling.
🏢実務担当者:Offers insights into how carbon policies and subsidies affect storage investment decisions, relevant for utilities and battery investors.
🏛政策担当者:Highlights the trade-offs between carbon taxes and subsidies in achieving cost-effective emission reductions under AI-driven load growth.
📄 Abstract（原文）
Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg--Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2608.03989first seen 2026-08-05 04:10:42
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