{"title":"Advances and challenges in energy and climate alignment of AI infrastructure expansion","authors":"Apoorv Lal , Fengqi You","doi":"10.1016/j.adapen.2025.100243","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid growth of artificial intelligence (AI) infrastructure deployment presents significant challenges for global energy systems and climate goals. While previous reviews address the sustainability of traditional data centers, Green AI approaches centered on model-level improvements or the application of AI in advancing sustainability across sectors, the energy and climate consequences of deploying AI infrastructure itself remain underexplored in prior literature. This paper reviews existing analyses on AI infrastructure’s energy and climate implications and proposes quantitative scenario-based frameworks, highlighting key research challenges at the intersection of AI-driven energy demand, region-specific clean energy strategies and their economic competitiveness, strategic levers in energy sourcing decisions, and policy dynamics. Additionally, this work identifies future research directions for aligning AI infrastructure growth with clean energy transitions through targeted mitigation opportunities across spatial and temporal horizons. First, the ambitious investment pathways for AI infrastructure development in the US underscore the need for spatially resolved scenario frameworks that reflect regional differences in deployment patterns and clean energy integration, along with the associated cost trajectories, to guide federal and state regulators. Second, the global expansion of AI infrastructure emphasizes the need for comprehensive frameworks that assess country-specific electricity demand shares, renewable transition pathways, and the influence of geopolitical restrictions, offering actionable insights for climate-conscious strategies. Finally, to prevent reinforcing fossil fuel dependency, particularly under disruptive growth scenarios, energy pathways incorporating nuclear power, renewables, energy storage, and varying grid reliance are explored as part of broader clean energy transitions, especially in regions facing energy security challenges.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100243"},"PeriodicalIF":13.8000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266679242500037X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid growth of artificial intelligence (AI) infrastructure deployment presents significant challenges for global energy systems and climate goals. While previous reviews address the sustainability of traditional data centers, Green AI approaches centered on model-level improvements or the application of AI in advancing sustainability across sectors, the energy and climate consequences of deploying AI infrastructure itself remain underexplored in prior literature. This paper reviews existing analyses on AI infrastructure’s energy and climate implications and proposes quantitative scenario-based frameworks, highlighting key research challenges at the intersection of AI-driven energy demand, region-specific clean energy strategies and their economic competitiveness, strategic levers in energy sourcing decisions, and policy dynamics. Additionally, this work identifies future research directions for aligning AI infrastructure growth with clean energy transitions through targeted mitigation opportunities across spatial and temporal horizons. First, the ambitious investment pathways for AI infrastructure development in the US underscore the need for spatially resolved scenario frameworks that reflect regional differences in deployment patterns and clean energy integration, along with the associated cost trajectories, to guide federal and state regulators. Second, the global expansion of AI infrastructure emphasizes the need for comprehensive frameworks that assess country-specific electricity demand shares, renewable transition pathways, and the influence of geopolitical restrictions, offering actionable insights for climate-conscious strategies. Finally, to prevent reinforcing fossil fuel dependency, particularly under disruptive growth scenarios, energy pathways incorporating nuclear power, renewables, energy storage, and varying grid reliance are explored as part of broader clean energy transitions, especially in regions facing energy security challenges.