The AI Act and its green blind spots: Hidden environmental risks in the AI lifecycle

Overview

The European Union Artificial Intelligence Act establishes a risk-based regulatory framework but addresses environmental sustainability only tangentially. Environmental impacts are not treated as a distinct risk category but instead are managed through indirect procedural mechanisms including transparency requirements, documentation standards, and governance structures. The extent to which these mechanisms adequately capture environmental concerns remains contested in regulatory scholarship.

Methods and approach

A doctrinal and operational analysis of the AI Act was conducted through systematic identification and coding of environment-relevant provisions across its Articles and Annexes. The analysis examined implementation pathways via conformity assessment procedures and post-market governance mechanisms. The research traced how environmental considerations manifest in compliance mechanisms and identified tensions between constitutional environmental commitments and statutory obligations.

Key Findings

The analysis reveals that environment-related obligations under the Act are modest, often non-mandatory, and scattered across procedural duties rather than consolidated into substantive requirements. Environmental protection commitments are present but lack integration into the Act's risk classification schema. Conformity assessment and post-market monitoring processes do not systematically incorporate lifecycle environmental assessment methodologies.

Implications

The current regulatory approach reflects an incomplete alignment between the Union's constitutional environmental protections and the AI Act's operational framework. Procedural mechanisms alone are insufficient to address the full spectrum of environmental risks across AI systems' lifecycles, from resource extraction and manufacturing through operational use and end-of-life disposal. This gap raises questions about whether existing compliance structures can effectively detect and mitigate cumulative environmental harms at scale.