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.

Control charts are effective tools for detecting out-of-control conditions of process parameters in manufacturing and service industries. The development of distribution-free control charts is important in statistical process control when the process quality variable follows an unknown or a non-normal distribution. This research thus proposes to use a distribution-free technology to establish a new control region based on the exponentially weighted moving average median statistic and exponentially weighted moving average interquartile range statistic for simultaneously monitoring the process location and dispersion and further sets up a corresponding new control chart. We compute the out-of-control average run length to evaluate out-of-control detection performance of the proposed control region and also compare the proposed control region with some existing location and dispersion control charts. Results show that our proposed chart always exhibits superior detection performance when the shifts in process location and/or dispersion are small or moderate. The new control region is thus recommended. Keywords: control chart, distribution-free, dispersion and location, EWMA, kernel control region, kernel density estimation.

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This study aims to identify the factors influencing the intention of people to launch business in Indonesia, using theory of planned behavior (TPB). The implemented methods included binomial logistic regression, classification and regression tree, and structural equation modeling. To examine this issue, data were obtained through Global Entrepreneurship Monitor (GEM) from 2015–2018. The results demonstrated that TPB construct was relevant to the launch initiative of business. This emphasized the significant functions of self-efficacy, business opportunity, and role models in the plans of people, regarding the establishment of an enterprise. Therefore, this study advanced the understanding of the factors influencing entrepreneurial behavior concerning the establishment of business, as well as provided strategies and plans for its development in Indonesia.