CO2 Emission Modeling in Asian Countries Using a Truncated Spline Nonparametric Regression Approach on Panel Data

Abstract

Climate change, driven by increasing carbon dioxide (CO₂) emissions, has become a major global challenge, with Asian countries contributing nearly 50% of total worldwide emissions. This study aims to model the factors affecting CO₂ emissions in Asian countries using a truncated spline nonparametric regression approach with panel data from 2020 to 2023. The predictor variables considered are population density, primary energy consumption, and urban population, while CO₂ emissions serve as the response variable. Optimal knot points were determined using the Generalized Cross Validation (GCV) criterion. The results indicate that the model with three knots provides the best performance, yielding a GCV value of 0.00004, a Mean Square Error (MSE) of 0.005248, and a coefficient of determination (R²) of 99.99206%. The findings reveal that differences in energy consumption patterns, industrialization levels, and dependence on fossil fuels contribute significantly to variations in CO₂ emissions among countries. The thematic map analysis further shows that high emissions are not always associated with high population density or urban population. Overall, the selected predictor variables explain almost all variations in CO₂ emissions across Asian countries. These findings provide a more comprehensive understanding of emission dynamics and offer valuable insights for the development of sustainable energy and environmental policies in the Asian region.