Risk-based group decision making regarding renewable energy schemes using a stochastic graphical matrix model

Abstract

Improvements to sustainability are generally measurable based on their environmental, economic, and socio-cultural effects. This study applied this concept by developing and empirically testing a risk-based method for assessing renewable energy policy. An integrated theoretical framework is proposed for analyzing group decision-making regarding renewable energy (RE) policy selection. The proposed graphical matrix approach combined with Monte Carlo simulation compares alternative RE schemes by identifying and measuring critical performance indicators with acceptable reliability. The mathematical model reliably prioritizes alternatives using majority voting to address uncertainty in multi-criteria decision making process. A case study using historical data from previous RE development projects to confirm the feasibility of this approach. Compared to the conventional deterministic method, the stochastic graphical matrix approach provides more reliable estimation accuracy, decision quality, and efficiency in selection of sustainable renewable energy. The systematic approach provides policy makers information for use in evaluation by synthesizing the judgments of a panel of experts.