“…Illustratively, Liu and Wu (2017) study the heterogeneous effect of emissions tax on carbon dioxide, methane, and nitrous oxide in the case of China using big data from a national census. Numerous scholars use machine learning models such as artificial neural networks or support vector machines, for example, to analyze the factors that influence carbon dioxide emissions (Li et al, 2017), forecast wind generation and facilitate renewable energy integration (Nazir et al, 2020), predict policy effect on primary energy production and consumption in the future (Sözen & Arcaklioğlu, 2011), and forecast energy-related carbon dioxide emissions (Wen & Cao, 2020;Zhao et al, 2017). Other scholars have also compared the performance of artificial neural networks with more 'traditional' techniques such as ARIMA and linear regression (Adeyinka & Muhajarine, 2020;Bilgili et al, 2012).…”