aceEcological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)
Abstract This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduc…
Panel analysis of 67 countries found AI development significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the largest effect on energy transitions.
Evidence
- Peer-reviewedJournal of Electronic Business & Digital Economics2025-09-02
- Peer-reviewedSustainability2022-07-14
- Peer-reviewedScientific Reports2025-10-07
- Peer-reviewedApplied Chemical Engineering2024-04-10
- Peer-reviewedHumanities and Social Sciences Communications2024-08-14
How should this claim be treated?
Truvace Impact Record TRV-2026-0377, v5: “Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI).” Truvace, 2026-10-05. /record/TRV-2026-0377 (accessed at citation time). sha256 2406c46dfe1c8cb2…
Calibration history
Every change to this record since certification, in the open.
Source set updated
Source set updated
Source set updated
Source set updated
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0377 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.