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- Trajectories of circular economy in cities: key patterns and emerging pathwaysPublication . Aina, Yusuf; Almulhim, Abdulaziz I.; Salami, Babatunde Abiodun; Swart, Julia; Abubakar, Ismaila Rimi; Dinis, Maria Alzira Pimenta; Sharifi, AyyoobUrbanisation and escalating resource pressures have intensified the need for systemic approaches to sustainable development, positioning the circular economy (CE) as a critical framework for cities. This study reviews academic literature to examine current trends, challenges, and forward-looking strategies for CE implementation in urban contexts. Using a search process aligned with the PRISMA protocol, 668 peer-reviewed articles were analysed through inductive content analysis. The findings reveal that CE practices in cities are increasingly shaped by global sustainability agendas, particularly in relation to SDGs 9 to 12. The analysis identifies eight thematic clusters that characterise urban CE pathways: urban planning and the built environment, energy and mobility systems, waste and resource management, water and urban agriculture, citizen engagement, governance and regulation, technological innovation, and socio-cultural transformation. A conceptual framework integrating eight thematic clusters is presented, illustrating how cities transition from linear to circular systems through policy alignment, digital innovation, and multisectoral collaboration. Case studies from global urban centres illustrate strategies ranging from smart infrastructure and circular procurement to social inclusion and localised production, all of which enable CE advancement. These findings reinforce the view of CE as a multidimensional approach capable of fostering urban resilience, environmental stewardship, and inclusive economic growth.
- Assessing the carbon footprint of artificial intelligence in higher education: a bibliometric and institutional analysisPublication . Leal Filho, Walter; Luetz, Johannes; Almulhim, Abdulaziz I.; Dinis, Maria Alzira PimentaThe 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.