Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis

Comprehensive Summary

This study conducts a systematic literature review (SLR) of 182 publications (2015–2025) to examine how Generative AI (GenAI) is being used in environmental assessment and sustainability analysis. Using the CIMO framework, the authors look at four main GenAI model types — GANs, VAEs, Transformer-based LLMs, and Diffusion Models — and how they are applied across five key areas: synthetic data generation, scenario and policy modeling, remote sensing, predictive modeling, and public engagement tools. The findings show that GenAI has strong potential to tackle persistent issues like data scarcity, limited scalability, and poor public communication of environmental data. However, the authors are careful to also flag serious concerns around model interpretability, data bias, high computational costs, and uneven institutional readiness across regions. To guide responsible use, the authors propose a three-tier framework built around technical fidelity, transparency, and ethical governance, and outline five policy priorities including building open-data ecosystems, establishing AI ethics boards, and adopting sustainable "Green AI" computing practices.

Outcomes and Implications

This research matters because GenAI tools have the potential to completely reshape how environmental and public health data is collected, modeled, and communicated — especially in resource-limited or data-poor regions that are often left out of large-scale analyses. For researchers and practitioners working at the intersection of environmental health and medicine, this means there is now a real opportunity to use synthetic data generation to fill monitoring gaps in areas where traditional surveillance infrastructure doesn't exist. The ability to simulate scenarios and predict environmental risks like air pollution, water contamination, or climate-driven disease vectors could allow health systems to get ahead of population-level threats before they become crises. That said, the authors are right to flag that without strong governance, these tools risk amplifying existing inequities — particularly if training data underrepresents marginalized communities. The call for participatory data governance and community-centered design is especially important here, as any tool built without local input is unlikely to be relevant or trusted by the communities it's meant to serve.