Environmental Impact Assessment (EIA) plays a critical role in ensuring sustainable development by identifying and mitigating the adverse effects of major construction projects. However, current EIA practices, especially in developing countries, often lack a systematic approach to identifying risks and evaluating and selecting mitigation strategies, leading to suboptimal environmental protection. This is coupled with a lack of data, inconsistencies in the quality of reports, and a low compliance rate with environmental management plans, which characterize mega infrastructure projects. In this regard, the research aims to develop a structured digital framework for identifying, assessing, and prioritizing mitigation strategies in EIAs for large-scale construction projects. The research method combines a review of the existing literature and case studies of past EIAs to gather insights into common mitigation measures and their effectiveness. Based on this, key environmental impacts are analyzed and potential mitigation strategies are categorized. A multi-criteria decision analysis (MCDA) framework is proposed to evaluate mitigation strategies using the Analytical Hierarchy Process (AHP) to rank and select optimal mitigation approaches. Eventually, the research develops an EIA Risk and Mitigation Management framework (EIA-RMMS), which is a digital system developed to facilitate EIA implementation, indicating standardized risk types and potential mitigation measures. The EIA-RMMS links to project management and enables integration with Artificial Intelligence and Machine Learning. The proposed framework is applied to three case study metro line projects in Egypt to prove its effectiveness in data analysis, decision-support and report structuring. The findings are valuable for EIA practitioners and project developers seeking to align infrastructure development with ecological and social sustainability goals.
Raafat et al. (Sat,) studied this question.