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From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate

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arxiv 2501.16548 v2 pith:HVWOLMTP submitted 2025-01-27 cs.CY

classification cs.CY
keywords effectsclimatedirectefficiencyreboundconsumptioncrisisdebate
verification ladder T0 review T1 audit T2 compute T3 formal
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As the climate crisis deepens, artificial intelligence (AI) has emerged as a contested force: some champion its potential to advance renewable energy, materials discovery, and large-scale emissions monitoring, while others underscore its growing carbon footprint, water consumption, and material resource demands. Much of this debate has concentrated on direct impacts -- energy and water usage in data centers, e-waste from frequent hardware upgrades -- without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses. Rebound effects undermine the assumption that improved technical efficiency alone will ensure net reductions in environmental harm. Instead, the trajectory of AI's impact also hinges on business incentives and market logics, governance and policymaking, and broader social and cultural norms. We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions. We conclude with recommendations that address rebound effects and challenge the market-driven imperatives fueling uncontrolled AI growth. By broadening the analysis to include both direct and indirect consequences, we aim to inform a more comprehensive, evidence-based dialogue on AI's role in the climate crisis.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. CEO-DC: Driving Decarbonization in HPC Data Centers with Actionable Insights

    cs.AR 2025-07 conditional novelty 6.0 of 10

    A decision framework using new carbon and price efficiency metrics shows most AI platform improvements cannot keep pace with demand growth, and short upgrade cycles need carbon prices far above current levels.

  2. A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A skill-graph random-walk model gives closed-form accuracy-versus-compute formulas for four reasoning strategies and connects them to training scaling.

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