HOW LARGE THE DIRECT REBOUND EFFECT FOR RESIDENTIAL ELECTRICITY CONSUMPTION WHEN THE ARTIFICIAL NEURAL NETWORK TAKES ON THE ROLE? A TAIWAN CASE STUDY OF HOUSEHOLD ELECTRICITY CONSUMPTION

Amid the energy reform efforts by the Taiwan government, residential energy demand continues to face an escalating trend every year. This indicates the phenomenon of the energy efficiency gap. One of the factors that control the energy efficiency gap is the rebound effect. The rebound effect is related to the increase in energy consumption through efforts to reduce the use of energy itself. This can be due to the low cost of usage that causes a person to be encouraged to use more energy. This study aims to estimate the magnitude of the direct rebound effect of household electricity consumption in Taiwan using monthly time series data from January 1998 to December 2018 and to implement the artificial neural network (ANN) as an alternative approach to measure the direct rebound effect. Based on the simulation results, the direct rebound effect magnitude for household electricity consumption in Taiwan is in the range of 11.17% to 21.95%. GDP growth is the most important input in the model. Additionally, population growth and climate change are also critical factors and have significant implications in the model.Keywords: energy efficiency gap, direct rebound effect, artificial neural networkJEL Classifications: Q43, C63, E7DOI: https://doi.org/10.32479/ijeep.9834