A review paper on how artificial intelligence is reshaping environmental impact assessment in renewable energy projects

                @article{CRSIJ26000404,
                  author = {Enyonam Yawa Adinyira, Samuel Tulashie, Ludwick Adjei Henaku},
                  title = {A review paper on how artificial intelligence is reshaping environmental impact assessment in renewable energy projects},
                  journal = {Cosmo Research and Science International Journal},
                  year = {2026},
                  volume = {2},
                  number = {2},
                  pages = {504-516},
                  issn = {3108-1584},
                  url = {https://cosmorsij.com/published/CRSIJ26000404.pdf},
                  abstract = {Environmental Impact Assessment (EIA) for renewable energy projects wind farms, solar parks, hydropower dams, geothermal plants, and bioenergy facilities remains constrained by static methodologies, short‑term surveys, coarse predictive models, and weak post‑construction monitoring. This systematic review synthesizes 118 peer‑reviewed studies and real‑world case implementations (2015–2026) to examine how Artificial Intelligence (AI) is reshaping EIA within the renewable energy sector. Following a PRISMA‑guided search across five databases, four core application domains are identified: (1) predictive modelling of ecological and climatic impacts (e.g., AI‑powered bird collision avoidance, bat activity forecasting, GAN‑based climate downscaling for hydropower); (2) automated data collection via remote sensing and IoT (e.g., 3D‑CNN for land‑cover change detection, U‑Net for wind farm compliance auditing); (3) AI‑driven document processing and public participation (e.g., LLM‑assisted EIA screening, automated public comment analysis); and (4) digital twins for dynamic project monitoring (e.g., offshore wind digital twin, national‑scale solar PV simulation). Real‑world case studies demonstrate substantial performance gains, including bird collision reductions of 89%, land‑cover classification accuracy exceeding 97%, and screening time reductions from days to hours. However, persistent challenges remain: data sparsity for rare species, algorithmic bias and environmental justice concerns, opacity of black‑box models hindering legal defensibility, AI’s own environmental footprint (data centers projected to consume 945 TWh annually by 2030), and regulatory gaps in existing EIA legislation. The paper concludes with a governance framework tailored to renewable energy EIA, offering actionable recommendations for developers, regulators, and researchers to harness AI’s potential while mitigating its risks. Responsible AI integration is essential to ensure that the clean energy transition does not come at an unacceptable environmental cost.},
                  keywords = {Environmental Impact Assessment (EIA), Artificial Intelligence (AI), Renewable energy, Wind energy, Solar energy, Hydropower, Digital twins, Machine learning, Species monitoring, Systematic review, Sustainable Development Goal},
                  month = {September}
        }