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The review aims to categorize sustainable AI practices and identify key challenges and future research directions.

April 23, 2026SustainabilityOpen Access

A Systematic Review of Green and Sustainable AI: Taxonomy, Metrics, Challenges, and Open Research Directions

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The review aims to categorize sustainable AI practices and identify key challenges and future research directions.
Conducted a systematic literature review across main databases like Scopus and Google Scholar.
Screened 325 records to select 49 studies published from 2016 to 2026.
Analyzed four categories of sustainable AI practices and their implications.
Identified four key categories: model efficiency, hardware optimization, lifecycle approaches, and policy sustainability.
Emphasized the importance of metrics for assessing AI's environmental impact.
Recommended interdisciplinary cooperation to enhance responsible AI innovation aligned with global sustainability goals.

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Systematic review explores sustainable AI practices and metrics, highlighting challenges and future research opportunities.

Marmouzi et al. (2026) studied this question.

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