No abstract

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.

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.

This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on labor productivity, apart from the traditional linear relationships and parametric methods employed in previous studies. Examining this topic is imperative for advancing the knowledge on the effects of air pollution on labor productivity and its association with labor insurance, employing a machine learning framework. The results reveal that air pollution, particularly PM10, has a negative impact on labor productivity. Lowering the PM10 level below 36.2 μg/m3 leads to an increase in marginal labor productivity. Additionally, the study identifies labor insurance as a significant factor in improving productivity, with a 9% increase in the total number of labor insurance holders resulting in a substantial 42.9% increase in productivity. Notably, a link between air pollution and insurance is observed, indicating that lower air pollution levels tend to be associated with higher labor insurance coverage. This research holds valuable implications for policymakers, businesses, and industries as it offers insights into improving labor productivity and promoting sustainable economic development.