UDTR — Radar mondial des droits de la Nature

Algorithmic Sustainability: Governance Conditions for AI-Driven Environmental Decision-Making

Identification

Clé publique

radar:r-fd5b1d997c2d885eb2017ff18d539feb

Auteurs

Khuloud Ali · Ghayth Tintawi · Mohamad Khaled Bassma · Aftab Haider

Année déclarée

2026

Langue déclarée

en

Type source

preprint

Première détection

2026-11-13T12:46:31.473753+00:00

Dernière observation des métadonnées

2026-11-13T12:46:31.473753+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

IMAGINE Studios · Southwest University of Political Science & Law

Pays d’affiliation déclarés

BR · CN

Résumé documentaire — langue originale

Environmental governance is no longer shaped only by expert judgement or statutory procedure. In recent years, algorithmic systems have begun to mediate how data are interpreted, to shape the scoring of risk, and to influence the way policy priorities are established. These systems now affect regulatory analysis. They also inform climate adaptation modelling and guide decisions on land use while supporting sustainability monitoring. Although artificial intelligence (AI) is often presented as a means to improve environmental outcomes, its deployment introduces lifecycle emissions while raising concerns about institutional opacity and exposing risks related to public legitimacy that remain insufficiently embedded in current governance frameworks. This article advances the concept of algorithmic sustainability and treats it as a condition of governance rather than a technical attribute of computational tools. Drawing on a structured qualitative synthesis of interdisciplinary research, the study identifies three conditions required for sustainable AI use in environmental decision systems. One concerns lifecycle carbon integrity. Another addresses institutional accountability. A third focuses on alignment with public value. These conditions are translated into a tiered Environmental AI Impact Assessment model (EAIA) designed to support regulatory oversight while remaining institutionally feasible. By separating computing-related effects from operational consequences and from wider systemic implications, the framework clarifies how algorithmic applications may improve environmental performance while still generating rebound pressures that threaten broader sustainability goals.

Identifiants

OPENALEX

https://openalex.org/W7134927960

DOI

10.20944/preprints202602.0733.v2

Repères documentaires

Famille documentaire

unknown

Types déclarés par les sources

preprint

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAlex · Document 1

• Document · https://doi.org/10.20944/preprints202602.0733.v2

Statut documentaire

Pertinence à examiner

Bibliothèque

Cette identité n’est pas encore intégrée à la Bibliothèque juridique.