An investment evaluation and incentive allocation model for public-private partnerships in renewable energy development projects

Abstract

This study proposes an investment evaluation and incentive allocation model for public-private partnerships (PPPs) in renewable energy development projects (REDPs). A hybrid multicriteria decision-making (MCDM) and bi-level optimization model are proposed to evaluate investment opportunities and allocate government financial incentives (GFIs) to REDPs. The best-worst method (BWM) is used to weigh the evaluation criteria. VIKOR and grey relational analysis (GRA) rank and allocate GFIs to the private sector companies selected to participate in the REDPs. The government uses the PPP to persuade digital services companies in the private sector to invest in underdeveloped REDPs using financial incentives with minimal risk and maximum return. An iterative full-enumeration-based heuristic model is developed to handle the computational intractability in the bi-level model. The computational results show that political and financial support and land use are the most and the least important criteria, respectively. Moreover, we show that the government prefers to allocate a significant portion of its GFIs to waste heat recovery and hydropower in partnership with digital services companies. The results from the bi-level model help government agencies and policymakers offer equitable incentive programs in the energy sectors.