Beyond Green: Why Leaders Must Choose Transformation Over Technology? A Human-Centric AI Framework for Genuine Sustainability

Document Type

Article

Source of Publication

Circular Economy and Sustainability

Publication Date

8-1-2026

Abstract

Green technological substitution is not sustainability – it is a sophisticated form of delay. Genuine sustainability demands systemic transformation, and AI stands at a critical juncture: it can either accelerate that transformation or entrench the very extraction and consumption patterns it claims to address. This paper draws a sharp and necessary distinction between superficial greenwashing and genuine sustainability, arguing that meaningful progress requires grounding AI systems and services in four non-negotiable operational principles: Redesign, Anticipate, Redistribute, and Envision, which collectively form the RARE framework. Across five high-stakes domains: battery recycling, food waste, zero-emission technology, mindset change, and heritage preservation, the paper demonstrates conclusively that efficiency gains in AI development alone cannot decouple growth from ecological harm; the rebound effect systematically overwhelms them. Drawing on planetary boundary science, UN-SDG principles, and circular economy theory, this paper issues a direct challenge to leaders, organizations, and AI practitioners: human-centric AI must operate within ecological limits, distribute its benefits and burdens equitably, and anticipate intergenerational consequences, not as aspirational add-ons, but as structural requirements. Critically, AI cannot be a credible instrument of sustainability while remaining one of humanity’s fastest-growing energy consumers and producers of e-waste. The RARE framework applies with equal force and urgency to AI itself: greening AI is not optional. It is the foundational test of whether the field is serious about the transformation it promises.

ISSN

2730-597X

Publisher

Springer Science and Business Media LLC

Volume

6

Issue

4

Disciplines

Computer Sciences

Keywords

AI Systems, Genuine Sustainability, Green AI, Greenwashing, Human-centric AI, UN-SDG

Scopus ID

105043382124

Indexed in Scopus

yes

Open Access

no

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