## 1 Introduction
In another study using the LSTM model, it was shown that energy usage, financial sector development, GDP, population, and clean energy all had significant impacts on China and India's greenhouse gas emissions [30]. [PERSON] and [PERSON] [31] used ML models, including decision trees and random forests, to analyze what drives China's CO2 output. The authors noted that GDP, government spending, and overseas investment can all have an impact on CO2 output. The feature relevance of GDP, finance general budget revenue, and foreign investment, as calculated by random forest, was 0.45, 0.12, and 0.08, respectively. [PERSON] et al. [32] used the k-nearest neighbors (KNN) model, ensemble models, and ANN to look at the connection between CO\({}_{2}\) emissions and China's economic growth, industry trends, urban development, R&D expenditure, actual usage of foreign investment, and the development rate of energy use consumption from 2000 to 2018. When compared to other models employed in the research, the KNN model made the most accurate predictions. Using a data set spanning from 1990 to 2018, [PERSON] and [PERSON] [33] used deep learning (DL), support vector machine (SVM), and ANN methods to predict greenhouse gas emissions from Turkey's power generation industry. With an R\({}^{2}\) of between 0.861% and 0.998%, the authors judged all the models to be adequate for estimating greenhouse gas emissions. [PERSON] [34], in a separate study conducted in Turkey, used DL, SVM, and ANN to successfully forecast GHG emissions from the transportation sector. To better estimate soil GHG emissions from an agricultural area, [PERSON] et al. [35] analyzed three types of ML regression models, namely classical regression, shallow learning, and DL. The authors verified that the LSTM model had the highest R\({}^{2}\) coefficient, and the lowest root mean square error (RMSE) values of all the ML models tested. The cyclic and periodic fluctuations in CO\({}_{2}\) fluxes were well replicated by the traditional regression models (RF, SVM, and LASSO), but the peak values of N\({}_{2}\)O fluxes were not predictably predicted. They discovered that shallow ML was the most responsive to hyperparameter adjustment, but it was also the least successful at estimating GHG fluxes compared to the other ML models they studied. [PERSON] and [PERSON] [36] employed six models to forecast India's greenhouse gas emissions: an ARIMA model, a SARIMAX model, a Holt-Winters model, a linear regression model, an RF model, and a DL-based long short-term memory (LSTM) model. They found that LSTM, SARIMAX, and Holt-Winters (H-W) were the three most reliable models for projecting India's GHG emissions across the indicators used. [PERSON] et al. [37] also looked into GHG emissions in India, but they used a DL hybrid model involving a convolution neural network and long short-term memory network (CNN-LSTM) to make their predictions. To anticipate the total CO\({}_{2}\) emissions from rice crops in India from 2019 to 2025, [PERSON] et al. [38] examined and contrasted four prediction models: RF, SARIMAX, H-W, and SVR. Compared to SARIMAX and RF, the MAPE and MSE values for the H-W and the SVR were found to be lower. These findings suggest that H-W and SVR are reliable models for estimating paddy crops' cumulative CO\({}_{2}\) emissions. As was noted in the above discussion, a range of machine learning strategies have been applied in several different research projects to generate forecasts regarding the emissions of greenhouse gases. However, decision tree-based machine learning requires more focus.