Measuring the impact of responsible factors on CO₂ emission using generalized additive model (GAM)

The indicators of economic and sustainable development ultimately significantly depend on carbon dioxide (CO₂) emissions in every country. In Bangladesh, there is an increasing trend in population, industrialization, as well as electricity demand generated from different sources, ultimately increasing CO₂ emissions. This study explores the relationship between CO₂ emissions and other significant relevant indicators. Moreover, the authors aimed to identify which model is effective at predicting CO₂ emissions and assess the accuracy of the prediction of different models. The secondary data from 1971 to 2020, was collected from the World Bank and the Bangladesh Road Transport Authority's publicly accessible website. The generalized additive model (GAM), the polynomial regression (PR), and multiple linear regression (MLR) were used for modeling CO₂ emissions. The model performance is evaluated using the Bayesian information criterion (BIC), Akaike information criterion (AIC), Root mean square error (RMSE), R-square, and mean square error (MSE). Results revealed that there are few multicollinearity problems in the datasets and exhibit a nonlinear relationship among CO₂ emissions. Among the models considered in this study, the GAM model has the lowest value of RMSE = 0.008, MSE = 0.000063, AIC = -303.21, BIC = -266.64 and the highest value of R-squared = 0.996 compared to the MLR and PR models, suggesting the most appropriate model in predicting CO₂ emissions in Bangladesh. Findings revealed that the total CO₂ emissions and other relevant risk factors is non-linear. The study suggests that the Generalized additive model regression technique can be used as an effective tool for predicting CO₂ emissions in Bangladesh. The authors believed that the findings would be helpful to policymakers in designing effective strategies in the areas of a low-carbon economy, encouraging the use of renewable energy sources, and focusing on technological advancement that reduces CO₂ emissions and ensures a sustainable environment in Bangladesh.