A Standardized Cross-Sector Digital Data Architecture for Environmental Impact Assessment: Evidence from Hydraulic Structures, Cement, and Wind Projects

Environmental Impact Assessment (EIA) information is often dispersed across project-specific documents, limiting cross-sector comparisons when environmental risks, mitigation measures, and monitoring indicators are reported using heterogeneous terminology and structures. This study discusses the potential of organizing EIA information within a common digital data architecture across three sectors: hydraulic structures, cement production, and wind energy. The dataset comprises 52 environmental risk entries from nine project-specific cases, including three barrages, three cement plants, and three wind farms. The entries were mapped to a 15-category analytical taxonomy while retaining their original terminology and linked to mitigation and monitoring records through persistent identifiers. The architecture was implemented using an Excel-based relational data model and a Power BI visualization layer. Risk prioritization was represented using a descriptive score calculated as Severity × Likelihood, with entries classified as Low (11), Medium (26), or High (15). Structural verification confirmed complete taxonomy coverage and reproducibility of all 52 risk scores, while identifier-based linkage enabled the identification of missing mitigation and monitoring relationships. The study contributes a transparent, traceable, and queryable cross-sector EIA data architecture that supports the organization, comparison, and inspection of heterogeneous environmental information. The taxonomy represents a dataset-specific analytical crosswalk, and the risk scores are descriptive prioritization attributes rather than validated predictions. The findings demonstrate the feasibility of the proposed data structure within the selected cases but do not establish improved environmental outcomes or universal applicability.