Plateau That Never Comes: When Efficiency Claims in Datacenters and AI Become Greenwashing

2026-06-02

AI Summary

This study addresses a critical gap in the sustainability discourse surrounding data centers and AI expansion, which often tout efficiency gains without empirical evidence of absolute reductions in resource consumption. For the first time, it systematically integrates the rebound effect into AI sustainability assessment, proposing a diagnostic framework encompassing five dimensions: metrics, system boundaries, reinvestment, burden shifting, and governance. Through qualitative analysis and policy diagnosis, combined with life-cycle perspectives and explicit system boundary delineation, the paper uncovers how firms leverage efficiency improvements and renewable energy procurement to engage in “greenwashing.” The findings reveal that dominant sustainability narratives overlook energy rebound effects and full life-cycle environmental burdens, failing to demonstrate genuine declines in absolute resource use. In response, the study advocates a new paradigm centered on “digital sufficiency,” which reorients the burden of proof toward demonstrating net environmental benefit.

Abstract

Datacenter expansion under generative AI is increasingly framed as compatible with sustainability because of efficiency gains, cleaner electricity procurement, and improved facility design. Yet these claims often do not show that absolute electricity, water, material, waste, and community-facing burdens are falling. This Perspective addresses that evidentiary gap. Rather than asking whether efficiency gains are real, we ask when such gains are being enlarged into claims of system-wide sustainability to justify continued expansion. We develop a rebound-informed diagnostic framework for evaluating AI and datacenter sustainability narratives across five tests: metric, boundary, reinvestment, burden shifting, and governance. Applied to major AI industry sustainability reporting, the framework shows that firms largely justify continued expansion through efficiency improvements and clean-energy procurement, rather than by demonstrating reductions in absolute resource use. Applied to plateau claims in the literature, we show that many claims establish local or relative improvements while leaving energy rebound, lifecycle burdens, and enforceable limits unresolved. We argue that these sustainable-growth narratives begin to function as greenwashing when they use efficiency improvements to claim sustainability even as absolute energy, water, material, and public health burdens continue to increase. We conclude by positioning digital sufficiency as a burden-of-proof framework for governance: those advocating further datacenter expansion must show that it reduces, rather than merely redistributes or defers, absolute burdens across the full system.

Problem

• greenwashing
• datacenter sustainability
• efficiency rebound
• absolute resource use
• AI expansion

Innovation

• rebound effect
• sustainability narratives
• digital sufficiency
• burden shifting
• datacenter expansion