Metafrontier frameworks for estimating solar power efficiency in the United States using stochastic nonparametric envelopment of data (StoNED)

Solar energy is one of the most promising energy sources as it its significantly reduce greenhouse gas (GHG) emissions compared to fossil fuels. In this study, we employ the meta frontier framework to estimate US solar energy performance in 2019 using stochastic non-parametric envelopment of data (StoNED) under the convex and non-convex frameworks. This estimation allows us to monitor operating inefficiencies and technological gaps in each observation. In addition, we investigate the potential impact of the specification of a convex production technology in relation to the use of a nonconvex technology in the comparative analysis. This methodological reflection is mainly supported by the recent engineering literature that provides evidence of the nonconvex hypothesis. The results indicate that a multifaceted approach must be taken to ensure the supply of energy. Given that sunny states have the potential to transmit energy to other states, the drawbacks, such as environmental concerns and high investment expenses, drive policymakers to look for other alternatives, such as adapting panels that are suitable for specific conditions.

Automated summary

Solar energy is a promising energy source that significantly reduces greenhouse gas emissions compared to fossil fuels. In this study, a meta frontier framework is used to estimate US solar energy performance in 2019 using stochastic non-parametric envelopment of data (StoNED) under convex and non-convex frameworks. The results indicate the need for a multifaceted approach to ensure energy supply and explore alternative options such as adapting panels for specific conditions.