## 1 Introduction
A recent study from Malaysia reported associations between CO\({}_{2}\) release, economic growth, renewable energy, urbanization, and agriculture [21]. Furthermore, this analysis also demonstrates that the relationship between CO\({}_{2}\) release and economic growth follows the environmental Kuznets curve (EKC). This research reveals that, despite a rise in CO\({}_{2}\) emissions and economic development, CO\({}_{2}\) emissions eventually decline after a certain level of growth is reached. [PERSON] et al. [22] studied the impact of biomass energy use on CO\({}_{2}\) emissions in 80 developed and developing nations. The findings of the study indicate that a rise in GDP, urbanization rate, and population increases CO\({}_{2}\) emissions. In wealthy nations, financial growth, foreign directinvestment, and openness lower CO\({}_{2}\) emissions, whereas the converse is true for poor nations. It was also reported in that study that the 1997 international accord reached by industrialized nations in the Japanese city of Kyoto, dubbed the \"Kyoto Protocol\" has a negative impact on CO\({}_{2}\) emissions and that an environmental Kuznets curve (EKC) exists. Using hierarchical regression modeling approaches, [PERSON] et al. [23] studied the effect of clean energy, population, and GDP on CO\({}_{2}\) emissions for fifty major economies from 1990 to 2015. The authors found that economic and population growth are associated with a rise in CO\({}_{2}\) emissions. Additionally, GHG emissions are inversely proportional to consumption reduction, energy efficiency, and investment in clean technology [23]. A number of studies have used a variety of machine-learning techniques to make projections about greenhouse gas emissions. [PERSON] et al. [24] used linear regression, DT, random forest (RF), and artificial neural networks (ANN) to model the correlation between global temperature and the atmospheric levels of nitrous oxide, methane, and carbon dioxide over 65 years. Based on the mean square error, the authors found that ANN was superior to the other algorithms. Three distinct ML algorithms were employed by [PERSON] et al. [25] to study the correlation between solar and wind power generation, coal production, GDP per capita, and GHG emissions in China, the USA, and India. Based on the results of the ML simulations, it is clear that a complete transition from fossil to renewable energy sources is necessary to successfully reduce GHG emissions. [PERSON] and [PERSON] [26] used data from 1990 to 2018 to train nine algorithms (ANN, AR, ARIMA, SARIMA, SARIMAX, RF, SVR, KNN, and LSTM) that forecasted GHG emissions in Iran to 2028. The authors found that an optimization model led to a 13-32% improvement in detection performance. To estimate carbon dioxide emissions from Group 20 countries, [PERSON] et al. [27] employed an adaptive neuro-fuzzy inference method and an ANN network to build multi-stage prediction models with clustered data. The growth of the economy and the use of energy were two factors taken into account by the authors. With regard to estimating future CO\({}_{2}\) emissions, both the neuro-fuzzy inference system and the ANN network performed better than multiple linear regression. Using annual data from 1970-2017, [PERSON] and [PERSON] [28] built a novel ML method called causal direction from dependence (D2C) to examine the interplay among the release of CO\({}_{2}\), energy usage, and GDP in Russia. The authors discovered evidence for causal relationships among the three factors. For this reason, [PERSON] et al. [29] also used three cutting-edge ML algorithms (support vector machine, ANN, and long-short term memory) to determine how much of an effect energy consumption has on GHG emissions in China, India, the United States of America, and Russia. The results showed a decline in future GHG emissions for the USA and Russia but an increase for China and India.