Data-Driven Decarbonization: Machine Learning Insights into GHG Trends and Informed Policy Actions for a Sustainable Bangladesh

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO2, N2O, and CH4 from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R2 score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh.