Changepoint model for energy-efficient technology diffusion: A comparative evaluation of empirical models

Abstract

Energy-efficient technologies (EETs) such as electric vehicles (EVs) and photovoltaic (PV) solar installations are rapidly growing fields that are revolutionizing how we use and produce energy. The global emergence of these technologies has significantly reduced greenhouse gas emissions, global warming, air pollution, oil consumption, and dependence on fossil fuels. However, adopting these technologies has also raised concerns about energy justice and has sparked a fundamental question about how energy-efficient products should be disseminated in society. Accurate forecasting of the sales trajectory of environmentally friendly technologies is crucial for their continued development. The present research aims to develop a flexible changepoint model that can successfully forecast the diffusion paradigm of Energy-efficient technological innovations by randomly capturing the evolution of adoption rates over time. The proposed model is empirically tested using historical sales data of Electric vehicles (EVs) and photovoltaic (PV) solar installations. The robustness and prediction performance of the proposed model are compared with conventional models using two quantitative measures: Entropy Ranking (ER) and Variance Ranking (VR). Assessing different empirical models can help identify the most suitable model for specific innovations and markets. The finding suggests that the developed model has superior estimation and prediction capabilities.