Research and Prediction Analysis of Key Factors Influencing the Carbon Dioxide Emissions of Countries Along the “Belt and Road” Based on Panel Regression and the A-A-E Coupling Model

Abstract

With the in-depth implementation of China’s “Belt and Road” strategic policy, member countries along the Belt and Road have gained enormous economic benefits. Thus, it is important to accurately grasp the factors that affect carbon emissions and coordinate the relationship between economic development and environmental protection, which can impact the living environment of people worldwide. In this study, the researchers gathered data from the World Bank database, identified key indicators significantly impacting carbon emissions, employed the Pearson correlation coefficient and random forest model to perform dimensionality reduction on these indicators, and subsequently assessed the refined data using a panel regression model to examine the correlation and significance of these indicators and carbon emissions across various country types. To ensure the stability of the results, three prediction models were selected for coupling analysis: the adaptive neuro-fuzzy inference system (ANFIS) from the field of machine learning, the autoregressive integrated moving average (ARIMA) model, and the exponential smoothing method prediction model (ES) from the field of time series prediction. These models were used to assess carbon emissions from 54 countries along the Belt and Road from 2021 to 2030, and a coupling formula was defined to integrate the prediction results. The findings demonstrated that the integrated prediction amalgamates the forecasting traits of the three approaches, manifesting remarkable stability. The error analysis also indicated that the short-term prediction results are satisfactory. This has substantial practical implications for China in terms of fine-tuning its foreign policy, considering the entire situation and planning accordingly, and advancing energy conservation and emission reduction worldwide.