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.

Abstract When the number of Decision-Making Units (DMUs) is not large enough compared to the total number of input parameters and outputs, traditional Data Envelopment Analysis (DEA) and Network Data Envelopment Analysis (NDEA) models often produce solutions that identify many DMUs as efficient, in addition to obtaining unrealistic weight distributions. In fact, this poor discrimination power and unrealistic weight distribution presented by DEA and NDEA models remain a major challenge, leading to the development of models to improve this performance. Thus, this paper proposes a Multiple Criteria Network Data Envelopment Analysis (MCNDEA), based on relational NDEA models. The idea of this model is to be used in network structures. To test the MCNDEA model, one a real instance linked to an evaluation problem of academic departments of a public university was used. Other instances were also used to validate the proposed MCNDEA, and these tests are included in the supplementary files. Finally, it should be noted that, in summary, the model proposed in this article had greater discrimination power of the analyzed DMUs, being able to identify the most efficient departments in each of the considered stages, besides pointing out to the University Management points of improvement regarding the best use of its resources for each department.

This paper proposes the uncapacitated single allocation hub maximal covering problem with fixed costs (USAHMCP), focused on selecting the hubs to be opened and determining the allocation of each non-hub node to a single hub, assuming that the set of selected hubs is restricted by a budget for opening the hubs. Thus, the uncapacitated single allocation p-hub maximal covering problem (USApHMCP), aimed to open a predetermined number of hubs, is a particular case of this problem. This paper proposes a heuristic algorithm based on the General Variable Neighborhood Search (GVNS) metaheuristic, with two variants differing on the greedy allocations criterion. One variant, originally proposed in this paper, uses coverage potential-based allocation; the other variant uses a distance-based allocation strategy, the most used criterion in the literature. Computational experiments carried out using instances from the literature with up to 1000 nodes showed that, when the hub opening cost is higher for nodes with higher demands, USAHMCP tends to open more hubs than USApHMCP; for instances that do not make this distinction, the number of hubs tends to be the same, with possible differences in the set of opened hubs. The coverage potential-based allocation strategy found better solutions than the distance-based allocation strategy. Concerning USApHMCP, for instances with up to 200 nodes, the proposed heuristic obtained good solutions in better runtimes than the benchmarks and updated the best-known solutions for 1000-node URAND instances.

The classical Myerson value for network games assigns payoffs to the players in a network based on their productivity in it. This often results in an unequal distribution of the resources generated by the players in the network. However, when link formation is costly but essential for generating resources in the network, one way of keeping the network stable is to incentivize those lesser-paid players with discounts. In this paper, we introduce a new allocation rule called the delta-discounted network Myerson value for network games, inheriting a similar idea from cooperative game theory. This value allows the designer to control the discounting amount by adjusting the discount parameter delta. We provide two characterizations of this value and discuss an application in the power grid management problem that specifies how this value can help sustainably transport green energy.