Power Couple? AI Growth and Renewable Energy Investment

• URL: http://arxiv.org/abs/2603.26678v1
• Date: Sun, 08 Mar 2026 20:22:37 GMT
• Title: Power Couple? AI Growth and Renewable Energy Investment
• Abstract summary: We model the equilibrium interaction between AI growth and renewable investment.<n>In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability.<n>As climate damages rise, the value of AI-enabled adaptation increases, which strengthens incentives to enable frontier scaling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI and renewable energy are increasingly framed as a "power couple" -- the idea that surging AI electricity demand will accelerate clean-energy investment -- yet concerns persist that AI will instead entrench fossil-fuel carbon lock-in. We reconcile these views by modeling the equilibrium interaction between AI growth and renewable investment. In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability; the equilibrium depends on scaling regimes and market incentives. When the market payoff to capability is supermodular and performance gains are near-linear in compute, developers push toward frontier scale even when the marginal megawatt-hour is fossil-based. In this regime, renewable expansion can primarily relax scaling constraints rather than displace fossil generation one-for-one, weakening incentives to build enough clean capacity and reinforcing fossil dependence. This yields an "adaptation trap": as climate damages rise, the value of AI-enabled adaptation increases, which strengthens incentives to enable frontier scaling while tolerating residual fossil use. When AI faces diminishing returns and lower scaling efficiency, energy costs discipline capability choices; renewable investment then both enables capability and decarbonizes marginal compute, generating an "adaptation pathway" in which climate stress strengthens incentives for clean-capacity expansion and can support a carbon-free equilibrium. A calibrated case study illustrates these mechanisms using observed magnitudes for investment, capability, and energy use. Decarbonizing AI is an equilibrium outcome: effective policy must keep clean capacity binding at the margin as compute expands.

Related papers

• A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints [3.545733459773562]
  We develop a game-theoretic model of carbon-aware AI training in which autonomous agents strategically choose whether to participate.<n>We analyze equilibrium existence, efficiency, and adaptive dynamics, and provide simulation evidence that appropriately designed incentives can eliminate grid-based energy usage.
  arXiv  Detail & Related papers  (2026-09-14T11:17:26Z)
• Efficiency vs Demand in AI Electricity: Implications for Post-AGI Scaling [26.59464335718656]
  We run U.S. scenarios that couple AI service growth with time varying compute energy intensity and economic drivers.<n>We find that service growth does not translate linearly into electricity demand; outcomes depend on efficiency trajectories and demand responsiveness.<n>With sustained efficiency improvements, AI electricity demand remains moderated; with slower or saturating gains, income-driven demand dominates by mid-century.
  arXiv  Detail & Related papers  (2026-03-11T07:50:00Z)
• AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability [16.11189838235793]
  We argue for research into, and implementation of, market-based methods that incentivize AI efficiency.<n>As a call to action, we propose a cap-and-trade system for AI.
  arXiv  Detail & Related papers  (2026-01-27T18:53:21Z)
• Improving AI Efficiency in Data Centres by Power Dynamic Response [74.12165648170894]
  The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models.<n> Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI.<n>However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight.
  arXiv  Detail & Related papers  (2025-10-13T08:08:21Z)
• AI and the Net-Zero Journey: Energy Demand, Emissions, and the Potential for Transition [0.0]
  We present energy consumption scenarios of data centers and impact on GHG emissions.<n>We address the quintessential question of whether AI will have a net positive, neutral, or negative impact on CO2 emissions by 2035.
  arXiv  Detail & Related papers  (2025-07-14T19:16:27Z)
• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.<n>We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
  We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.<n>First, it is not sustainable, as, despite efficiency improvements, its compute demands increase faster than model performance.<n>Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
  arXiv  Detail & Related papers  (2024-09-21T14:43:54Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• Sustainable Computing -- Without the Hot Air [0.8602553195689513]
  The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. This paper distills the fundamental trends that determine computing's carbon footprint and their implications for achieving sustainable computing.
  arXiv  Detail & Related papers  (2022-06-30T19:48:33Z)
• Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review [12.609078866334615]
  Governments' net zero emission target aims at increasing the share of renewable energy sources. This paper addresses how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets.
  arXiv  Detail & Related papers  (2022-02-21T10:27:31Z)
• Modelling the transition to a low-carbon energy supply [91.3755431537592]
  A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world.
  arXiv  Detail & Related papers  (2021-09-25T12:37:05Z)
• Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
  We study relevant models in the areas of computer vision and natural language processing. For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
  arXiv  Detail & Related papers  (2021-09-12T09:40:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.