The impacts of artificial intelligence on environmental sustainability and human well-being

2026-02-27

AI Summary

This study addresses the lack of systematic and holistic assessment in existing research on the impacts of artificial intelligence (AI) on environmental sustainability and human well-being. Through a systematic literature review, the authors screened and coded 1,291 core studies from an initial corpus of 6,655 publications, proposing the first integrated evaluation framework that encompasses both environmental and social dimensions across the full AI lifecycle and includes subjective well-being. The analysis reveals that 72% of environmental studies focus narrowly on energy consumption and carbon emissions, with 83% adopting an optimistic stance. In contrast, research on well-being exhibits a balanced yet fragmented perspective: while health and income expectations are predominantly positive, anticipated effects on employment and social cohesion are largely negative, highlighting critical blind spots in current AI impact assessments.

Technology Category

• Philosophy and Ethics of AI: Societal Impact of AI
• Humans and AI: Other Foundations of Human Computation & AI
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Sustainability and climate impact of web technologies
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impact requires accounting for computing-related, application-level, and systemic impacts, while integrating both environmental and social dimensions. Bridging these gaps is essential to understand the full scope of AI's impacts and to steer its development towards environmental sustainability and human flourishing.

Problem

• Artificial Intelligence
• Environmental Sustainability
• Human Well-being
• Systemic Impacts
• Life-cycle Assessment

Innovation

• systematic literature review
• environmental sustainability
• human well-being
• life-cycle assessment
• AI impacts