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

Abstract

Previous energy performance studies neglected the role of information entropy in feedback processes between input and output slacks. Superior energy performance may be achieved through the capability of learning from how increased outputs could yield reduced inputs and vice-versa. This paper focus on this gap, by presenting an assessment of US states for a 35-year period in lieu of relevant socio-economic and demographic variables. US is the world largest energy producer and consumer, being well-known not only for innovation in efficient energy use but also for managerial feedback mechanisms in the energy field which ensures continuous improvement in generation and consumption. First, a novel SEA-IS (Stochastic-Entropic Analysis for Ideal Solutions) model is developed to assess the potential information gains that may arise from energy slacks minimization given different optimal reduction quantiles in US states. This nonlinear stochastic optimization model not only relies on Beta distributed priors to model the odds-ratio of learning feedback but also 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. Results indicate that California is the only U.S. state that has indicate strong mutual information feedback and continuous improvements in efficiency. 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.

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