What does this research mean for the field?

A spatially explicit, digital rule-based GIS decision-support framework successfully formalizes Environmental Impact Assessment logic, producing risk classifications that are substantially consistent with regulatory standards and demonstrating that receptor proximity, operational intensity, and lifecycle stage are primary determinants of environmental risk. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to improve Environmental Impact Assessments by creating a digital tool that enhances decision-making processes and risk evaluations.

May 30, 2026Open Access

A Digital Rule-Based GIS Decision Support Tool for Environmental Impact Assessment: The Case of Airport Projects

Key Points

This research aims to improve Environmental Impact Assessments by creating a digital tool that enhances decision-making processes and risk evaluations.
Developed a rule-based decision support framework using GIS technology for EIA.
Applied the framework to three airport case studies in Egypt, focusing on different environmental contexts.
Established validation through Monte Carlo simulations, sensitivity analysis, and Cohen’s Kappa agreement analysis.
Achieved stable risk classifications across various receptor categories and lifecycle phases with parameter variations (±15%).
Cohen’s Kappa values between 0.71 and 0.79 indicated strong agreement between model outputs and standard environmental classifications.
Identified key factors like receptor proximity and lifecycle stage as crucial for determining environmental risk profiles.

Abstract

Environmental Impact Assessment (EIA) is intended to function as a predictive, spatially grounded decision-support mechanism. Yet in many developing contexts, its operationalization remains fragmented, descriptive, and weakly standardized. Thus, this study addresses limitations in conventional EIA systems related to transparency, reproducibility, and uncertainty integration by proposing a spatially explicit, digital rule-based decision-support framework that operationalizes hierarchical receptor-based structuring, lifecycle-sensitive modelling, risk classification, and uncertainty propagation within an integrated Geographic Information Systems (GISs) architecture. The academic objective is to advance computational environmental assessment methodologies by formalizing EIA logic into a structured computational workflow that translates spatial interactions (including land use, population density, ecological sensitivity, hydrological zones) and project attributes (including project type, activities and operational conditions) into quantified risk profiles and mitigation mappings. This necessitates combining receptor proximity, overlap intensity, contextual sensitivity, operational conditions, and receptor vulnerability. The framework was applied to three airport case studies in Egypt—representing urban, peri-urban/desert expansion, and coastal–ecological environmental contexts—using standardized spatial preprocessing and normalized analytical scales. Validation was conducted using Monte Carlo uncertainty simulation, sensitivity analysis, Spearman rank correlation, and Cohen’s Kappa agreement analysis. The results demonstrated stable comparative risk classification across receptor categories, lifecycle phases, and impact mechanisms under moderate parameter perturbation (±15%). Cohen’s Kappa agreement values ranging from 0.71 to 0.79 indicated substantial consistency between model-generated exceedance zones and regulatory environmental classifications. In sum, the results demonstrate that receptor proximity, operational intensity, and lifecycle stage function as primary determinants of differentiated environmental risk configurations, and that the proposed framework can support transparent, reproducible, and spatially explicit environmental assessment.

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