Assessing the carbon footprint of artificial intelligence in higher education: a bibliometric and institutional analysis

Walter Leal Filho; Johannes M. Luetz; Abdulaziz I. Almulhim; Maria Alzira Pimenta Dinis

doi:10.1186/s12302-026-01414-8

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Assessing the carbon footprint of artificial intelligence in higher education: a bibliometric and institutional analysis

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Assessing the carbon footprint of artificial intelligence in higher education: a bibliometric and institutional analysis

Walter Leal Filho, Johannes M. Luetz, Abdulaziz I. Almulhim and Maria Alzira Pimenta Dinis

Environmental Sciences Europe, Vol.Advanced access

22-May-2026

DOI: https://doi.org/10.1186/s12302-026-01414-8

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Abstract

artificial intelligence

carbon footprint

higher education

sustainability

energy efficiency

institutional governance

The rapid integration of artificial intelligence (AI) across higher education has transformed research, teaching, and institutional operations. Yet its environmental implications remain poorly understood at the institutional level. While a growing literature examines the energy consumption and carbon footprint of AI systems, little is known about how these concerns are recognised or addressed within universities. This study addresses this gap by combining a bibliometric analysis of 461 peer-reviewed publications indexed in Scopus (2014–2025) with a multiple-case study analysis of selected research-intensive universities. The bibliometric analysis reveals a rapidly expanding research landscape dominated by themes such as machine learning, energy consumption, optimisation, and sustainability, alongside a comparatively limited focus on higher education as an institutional context. The case studies, based on sustainability reports, climate action plans, and environmental disclosures, focus on a set of research-intensive universities with substantial AI-related infrastructure. They show a consistent pattern: despite the centrality of high-performance computing (HPC) and cloud-based platforms, institutional reporting of energy and carbon emissions remains aggregated, rarely examining AI-specific impacts. This reveals a governance gap between the expanding scientific understanding of AI’s environmental footprint and the maturity of sustainability practices in academia. The novelty of this study lies in the integration of bibliometric analysis with institutional case study evidence to systematically examine how AI-related energy use and carbon emissions are addressed in higher education. By bridging these two analytical dimensions, the study provides new insight into the disconnect between research advances and institutional practice and highlights the need for dedicated frameworks to account for AI-related energy use and emissions. The study also aligns with the United Nations Sustainable Development Goals (SDGs), particularly Affordable and Clean Energy (SDG 7), Climate Action (SDG 13), and Quality Education (SDG 4), contributing to the ongoing debate on responsible and sustainable AI in higher education.

Details

Title: Assessing the carbon footprint of artificial intelligence in higher education: a bibliometric and institutional analysis
Authors: Walter Leal Filho - Manchester Metropolitan University
Johannes M. Luetz (Corresponding Author) - University of the Sunshine Coast
Abdulaziz I. Almulhim - Imam Abdulrahman Bin Faisal University
Maria Alzira Pimenta Dinis - Fernando Pessoa University
Publication details: Environmental Sciences Europe, Vol.Advanced access
Publisher: SpringerOpen
DOI: 10.1186/s12302-026-01414-8
ISSN: 2190-4715
Copyright note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Data Availability: The bibliometric data analysed in this study were retrieved from the Scopus database under licence. While the full dataset cannot be publicly redistributed, the search strategy is fully described in the Methods section, and the list of records analysed is available from the authors upon reasonable request. Data sources that underpin the multiple case study analysis are presented in Table 1.
Organisation Unit: Academic Support Unit; Sustainability Research Cluster
Language: English
Record Identifier: 991233182202621
Output Type: Journal article

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