Facilitating public scrutiny of EIA reports with open data and artificial intelligence: insights from a Mexican case study

Public scrutiny is a cornerstone of Environmental Impact Assessment (EIA), yet their technical complexity methods often limits the ability of stakeholders to meaningfully review EIA reports. This paper introduces a practical approach that integrates Open Data (OD), Artificial Intelligence (AI), and comparative analysis of impact significance assessment to evaluate whether key sources of uncertainty are adequately addressed in EIA reports. We apply this approach to the case of the, focusing on a technical-scientific review of an EIA report of Tren Maya, commissioned by Mexican environmental authorities. We demonstrate how our approach can be employed to assess epistemic and ontological uncertainty in biodiversity impact assessments. This approach can identify weaknesses in expert judgments, data quality, and adherence to core EIA principles. Results indicate that the EIA report underestimated biodiversity impacts and exhibited methodological inconsistencies in indicator scoring and aggregation. In general, OD-AI tools supports evidence-based scrutiny under procedural constraints, while comparative analysis highlights the risks of biased or inconsistent judgments in conventional EIA practice. The approach is transparent, operationally accessible, and adaptable to other EIA domains and contexts.

OD-AI supports public scrutiny of EIA reports.
OD enables efficient appraisal of biodiversity baseline data.
AI enables traceable, literature-backed assessments of impact significance.
Comparative analysis evaluates compliance of expert judgment with core EIA principles.

OD-AI supports public scrutiny of EIA reports.
OD enables efficient appraisal of biodiversity baseline data.
AI enables traceable, literature-backed assessments of impact significance.
Comparative analysis evaluates compliance of expert judgment with core EIA principles.