Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

查看全文
  • Walter Leal Filho,
  • Johannes M. Luetz,
  • Abdulaziz I. Almulhim,
  • Maria Alzira Pimenta Dinis

摘要

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.