Information entropy, continuous improvement, and US energy performance: a novel stochastic-entropic analysis for ideal solutions (SEA-IS)

Abstract

To address the gap in energy performance studies regarding the role of information entropy in feedback processes between input and output slacks, the paper develops a novel Stochastic-Entropic Analysis for Ideal Solutions (SEA-IS) model. The non-linear stochastic optimization model is then applied to assess the potential information gains that may arise from energy slacks minimization given the different optimal reduction quantiles in US states. The model relies on Beta distributed priors to model the odds-ratio of learning feedback and takes advantages of numerous strengths present in DEA and TOPSIS approaches for performance management. Machine learning methods are also employed to predict information gains in terms of contextual variables. We find that California is the only U.S. state that has indicate strong mutual information feedback and continuous improvements in efficiency. The results indicate there is ample scope for harnessing the power of information gains in improving energy efficiency, particularly in 37 U.S. states, which indicates scope for a public–private partnership to achieve this goal.