*Result*: Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis.
Title:
Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis.
Authors:
Rahman SM; Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.; Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.; Applied Research Center for Environment and Marine Studies, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia., Raihan A; Applied Research Center for Environment and Marine Studies, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia. asif.raihan@kfupm.edu.sa., Abudalfa S; SDAIA-KFUPM JRC for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.; Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.
Source:
Environmental management [Environ Manage] 2026 Feb 09; Vol. 76 (3), pp. 93. Date of Electronic Publication: 2026 Feb 09.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Springer Verlag Country of Publication: United States NLM ID: 7703893 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1009 (Electronic) Linking ISSN: 0364152X NLM ISO Abbreviation: Environ Manage Subsets: MEDLINE
Imprint Name(s):
Publication: New York Ny : Springer Verlag
Original Publication: New York, Springer-Verlag.
Original Publication: New York, Springer-Verlag.
MeSH Terms:
References:
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Baño-Medina J, Manzanas R, Gutiérrez JM (2022) Downscaling multi-model climate projection ensembles with deep learning (DeepESD): Contribution to CORDEX EUR-44. Geosci Model Dev 15(2):6747–6759. https://doi.org/10.5194/gmd-15-6747-2022 . (PMID: 10.5194/gmd-15-6747-2022)
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Berthelot A, Caron E, Jay M, Lefèvre L (2024) Estimating the environmental impact of Generative-AI services using an LCA-based methodology. Proc CIRP 122:707–712. https://doi.org/10.1016/j.procir.2024.01.098 . (PMID: 10.1016/j.procir.2024.01.098)
Bibri SE, Huang J, Jagatheesaperumal SK, Krogstie J (2024) The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environ Sci Ecotechnol 20: 100433. https://doi.org/10.1016/j.ese.2024.100433 . (PMID: 10.1016/j.ese.2024.100433)
Chen M, Samat N, Tilaki MJM, Duan L (2025) Land use/cover change simulation research: A system literature review based on bibliometric analyses. Ecol Indic 170: 112991. https://doi.org/10.1016/j.ecolind.2024.112991 . (PMID: 10.1016/j.ecolind.2024.112991)
Cheong BC (2024) Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making. Front Hum Dyn 6: 1421273. https://doi.org/10.3389/fhumd.2024.1421273 . (PMID: 10.3389/fhumd.2024.1421273)
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Essamlali I, Nhaila H, ElKhaili M (2024) Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon 10(6):e27920. https://doi.org/10.1016/j.heliyon.2024.e27920 . (PMID: 10.1016/j.heliyon.2024.e27920)
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Hao H, Wang Y, Chen J (2024) Empowering scenario planning with artificial intelligence: A perspective on building smart and resilient cities. Eng 43:272–283. https://doi.org/10.1016/j.eng.2024.06.012 . (PMID: 10.1016/j.eng.2024.06.012)
Hariram NP, Mekha KB, Suganthan V, Sudhakar K (2023) Sustainalism: An integrated socio-economic-environmental model to address sustainable development and sustainability. Sustainability 15(13):10682. https://doi.org/10.3390/su151310682 . (PMID: 10.3390/su151310682)
Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, Huang K, Hussain A (2024) Interpreting black-box models: a review on explainable artificial intelligence. Cogn Comput 16(1):45–74. https://doi.org/10.1007/s12559-023-10179-8 . (PMID: 10.1007/s12559-023-10179-8)
Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840–6851. https://doi.org/10.48550/arXiv.2006.11239 . (PMID: 10.48550/arXiv.2006.11239)
Hosseini M, Gao P, Vivas-Valencia C (2025) A social-environmental impact perspective of generative artificial intelligence. Environ Sci Ecotechnol 23: 100520. https://doi.org/10.1016/j.ese.2024.100520 . (PMID: 10.1016/j.ese.2024.100520)
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Contributed Indexing:
Keywords: AI governance; Environmental management; Generative AI; Scenario modeling; Sustainability risks; Synthetic data generation
Entry Date(s):
Date Created: 20260209 Date Completed: 20260209 Latest Revision: 20260312
Update Code:
20260312
DOI:
10.1007/s00267-026-02402-7
PMID:
41661333
Database:
MEDLINE
*Further Information*
*This study presents a comprehensive review of the emerging role of Generative Artificial Intelligence (GenAI) in environmental assessment and sustainability analysis. Positioned within a new paradigm of environmental management, GenAI redefines traditional static models through dynamic, generative, and participatory approaches that integrate data synthesis, scenario modeling, and governance insight. Using a Systematic Literature Review (SLR) guided by the CIMO (Context-Intervention-Mechanism-Outcome) framework, this paper identifies and analyzes 182 scholarly and technical publications published between 2015 and 2025. The review synthesizes developments across key GenAI architectures-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based Large Language Models (LLMs), and Diffusion Models-and evaluates their applications in synthetic data generation, scenario simulation, remote sensing, predictive analytics, and public engagement. The findings reveal that GenAI holds significant potential to address data scarcity, enhance model scalability, and promote participatory and interdisciplinary decision-making, while also presenting challenges related to interpretability, data bias, validation, environmental footprint, and ethical governance. To guide responsible implementation, the study proposes a three-tier framework emphasizing technical fidelity, transparency, and governance alignment. The implications underscore that effective integration of GenAI into environmental management requires hybrid modeling, participatory data governance, and sustainable AI infrastructures to ensure transparency, accountability, and equity. Collectively, this work advances an evidence-based understanding of how GenAI can underpin a data-driven, inclusive, and ethically responsible paradigm in environmental assessment.
(© 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)*
*Competing interests: The authors declare no competing interests.*