Esho, Esther OreofeoluwaAkinyelu, Andronicus AyobamiDinis, Maria Alzira Pimenta2026-03-062026-03-062026-02-25APA7th: Esho, E. O., Akinyelu, A. A., & Dinis, M. A. P. (2026). Sustainable generative AI and quantum computing: review assessment on the environmental impact of generative AI and quantum technologies [Review]. Frontiers in Sustainability, 7, 1–23, Article 1726832. https://doi.org/10.3389/frsus.2026.17268322673-4524http://hdl.handle.net/10284/15111This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Sustainable generative AI and quantum computing: review assessment on the environmental impact of generative AI and quantum technologiesEsther O. Esho 1*, Andronicus A. Akinyelu 2* and Maria Alzira Pimenta Dinis 3,41The Australian New Zealand Society for Ecological Economics (ANZSEE), Brisbane, QLD, Australia, 2School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa, 3Fernando Pessoa Research, Innovation and Development Institute (FP-I3ID), University Fernando Pessoa (UFP), Porto, Portugal, 4Marine and Environmental Sciences Centre (MARE), University of Coimbra, Coimbra, PortugalThe rapid advancement of Generative Artificial Intelligence (GenAI) and Quantum Computing (QC) presents transformative opportunities, yet their high compu-tational requirements raise concerns about their environmental sustainability. This comprehensive review examines the ecological footprint of both technolo-gies, focusing on key metrics like energy consumption, carbon emissions, and resource depletion. Findings from existing studies consistently indicate that the impact of GenAI is mostly driven by the immense energy demands of large-scale model training and inference. Moreover, findings from the review reveal that the footprint of QC largely stems from the energy-intensive cryogenic cooling and rare material requirements of its specialized hardware. This paper benchmarks current approaches to environmental assessment, highlighting the important role of Life Cycle Assessment (LCA) in providing a holistic view of the classification of environmental impacts across the entire supply chain, from manufacturing to disposal. This study proposes a range of domain-specific mitigation strategies, including algorithmic optimizations like pruning and distillation for AI, and cryo-genic and material sourcing improvements for quantum systems. This study also proposes a framework for proactive, responsible innovation and identifies some gaps in the literature, such as the lack of standardized metrics and transparent reporting. There is a need to embed eco-conscious principles in the design of future technologies and highlight opportunities where these technologies can be used to handle broader climate challenges. The findings in this study can be used by policymakers, researchers, and industry stakeholders in aligning technological progress with global climate and sustainability goals.KEYWORDScarbon footprint assessment, environmental sustainability, Generative Artificial Intelligence, green computing, quantum computingThe rapid advancement of Generative Artificial Intelligence (GenAI) and Quantum Computing (QC) presents transformative opportunities, yet their high computational requirements raise concerns about their environmental sustainability. This comprehensive review examines the ecological footprint of both technologies, focusing on key metrics like energy consumption, carbon emissions, and resource depletion. Findings from existing studies consistently indicate that the impact of GenAI is mostly driven by the immense energy demands of large-scale model training and inference. Moreover, findings from the review reveal that the footprint of QC largely stems from the energy-intensive cryogenic cooling and rare material requirements of its specialized hardware. This paper benchmarks current approaches to environmental assessment, highlighting the important role of Life Cycle Assessment (LCA) in providing a holistic view of the classification of environmental impacts across the entire supply chain, from manufacturing to disposal. This study proposes a range of domain-specific mitigation strategies, including algorithmic optimizations like pruning and distillation for AI, and cryogenic and material sourcing improvements for quantum systems. This study also proposes a framework for proactive, responsible innovation and identifies some gaps in the literature, such as the lack of standardized metrics and transparent reporting. There is a need to embed eco-conscious principles in the design of future technologies and highlight opportunities where these technologies can be used to handle broader climate challenges. The findings in this study can be used by policymakers, researchers, and industry stakeholders in aligning technological progress with global climate and sustainability goals.engCarbon footprint assessmentEnvironmental sustainabilityGenerative Artificial IntelligenceGreen computingQuantum computingreview10.3389/frsus.2026.1726832