UDTR Global Rights of Nature Radar

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

Identification

Public canonical key

radar:r-fd5b1d997c2d885eb2017ff18d539feb

Authors

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

Reported year

2026

Reported language

en

Source type

preprint

First detected

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

Last metadata observation

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

Last HTTP verification

Not checked

Institutions reported by the source

IMAGINE Studios · Southwest University of Political Science & Law

Reported affiliation countries

BR · CN

Documentary abstract — original language

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.

Identifiers

OPENALEX

https://openalex.org/W7134927960

DOI

10.20944/preprints202602.0733.v2

Documentary context

Documentary family

unknown

Source-reported types

preprint

Entity mentions are not evidence of legal recognition. Jurisdiction studied is separate from author affiliation country.

Sources

OpenAlex · Record 1

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

Documentary status

Relevance pending

Library

This identity is not yet included in the Legal Library.