Overcoming implementation barriers to renewable energy in developing nations: A case study of Iran using MCDM techniques and monte carlo simulation
Amir Soltani, Mohammad Imani
Abstract
• The most influential strategies for enhancing Iran's renewable energy have been identified. • Integration of AHP with fuzzy logic offers a robust assessment. • Monte carlo simulations confirmed the stability of top strategies across varied criterion weights. • Sensitivity analysis revealed significant variability in strategy effectiveness, highlighting policy and capacity impacts. • Empirical evidence supports achieving UNSDG7 with practical renewable energy strategies. For most of the world's energy portfolio, particularly in developing countries, fossil fuels form a key dependency. The seventh aim of the United Nations' Sustainable Development Goal (SDG7) is to ensure that all people have readily available, consistent, sustainable, modern energy. Developing nations encounter a range of challenges, such as environmental concerns like air pollution and climate change, increasing energy and population demands, and limited resources. These challenges are associated with achieving net zero emissions as well as a shift to renewable, clean energy sources. Using Iran as a case study, this paper addresses the challenges to the advancement of renewable energy in developing nations. It provides strategies to overcome these challenges and raise the acceptance of environmentally friendly technologies. This study uses fuzzy logic, The Analytic Hierarchy Process (AHP), and an integrated fuzzy logic and AHP (fuzzy-AHP) to evaluate and rank numerous solutions. We conducted a Monte Carlo simulation as a sensitivity analysis method to further confirm the robustness of the proposals. This simulation aimed to find out how various criterion weights influenced the ranking of multiple strategies, thereby providing a more sophisticated knowledge of their efficiency. The results indicate that Iran's primary needs, in order of priority, are "revising governmental policies," "enhanced capacity building," and "improving financial support mechanisms." The Monte Carlo simulation underscored the sensitivity of these approaches to changes in criteria weights, therefore reinforcing the validity of these conclusions.