This paper presents a novel approach to conducting environmental and social risk assessments associated with financing renewable energy projects. Risks such as community opposition, regulatory delays, and ecological damage can significantly impact both project viability and investment decisions. The aim is to provide a tool that enhances decision-making in the face of the uncertainty associated with developing these projects. The proposed method consists of two phases. First, a fuzzy risk assessment is conducted based on expert judgment to evaluate both the frequency and intensity of risks. This assessment also incorporates weights derived from the Extended Best-Worst method. In the second stage, a fuzzy mathematical programming model is developed to select the optimal combination of mitigation measures. The goal is to minimize the project’s overall risk while allowing for flexible adherence to the budget. Trade-offs are analyzed and ranked across seven key dimensions: economic, ecological, political, cultural and overall risks and feasibility, and cost of the solution. This analysis is performed using the Technique of Preference Ordering by Similarity to Ideal Solution (TOPSIS). The methodology is applied to a real case study using the MEVIMS system developed by Bancomext and Deutsche Gesellschaft für Internationale Zusammenarbeit. Out of 24 identified risks, the optimal set of mitigation actions results in a 14% reduction in total project risk, utilizing approximately 15% of the project budget. The most balanced solution–considering risk reduction, budget compliance, and performance across the four dimensions of sustainability–was identified using TOPSIS.
Bilbao-Terol et al. (Fri,) studied this question.