Environmental Education and SustainabilitySmart Cities and TechnologiesInnovative Approaches in Technology and Social Development
Abstract
This study presents a systematic review of research on the environmental sustainability of Open, Distance, and Digital Education (ODDE), focusing on its carbon footprint. Although higher education institutions (HEIs) are expected to reduce greenhouse gas emissions, the environmental impacts of teaching and learning modes remain underexplored compared to campus operations. To address this gap, 17 studies published between 2002 and 2025 were examined. The evidence shows that ODDE typically generates far lower emissions than campus-based education, mainly through reduced travel and campus energy use and the benefits of centralized delivery. Landmark studies reported reductions of more than 80% compared to face-to-face teaching. Studies conducted during the COVID-19 pandemic confirmed significant savings but also highlighted rebound effects, as household energy use increased while campus facilities continued to consume resources. Emerging technologies, particularly generative AI, further complicate the picture due to their high energy demands. Overall, the evidence suggests that ODDE can contribute positively to reducing the carbon footprint of higher education. However, the small number of studies, lack of standardized methodologies for carbon footprint assessment (CFA), and limited attention to rebound effects constrain broader generalizability. Future research should systematically compare different delivery models across diverse institutional contexts and develop shared CFA guidelines. Strengthening the sustainability profile of ODDE is of critical importance, not only for the higher education sector but also for advancing global climate goals.
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Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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