Machine learning-based carbon emission prediction and influence factor analysis discussion in China cement industry

Based on a carbon emission inventory of China's cement industry, this study evaluates the performance of six machine learning models-ridge regression (RR), polynomial regression (PR), random forest (RF), support vector machine (SVR), gradient boosted regression tree (GBRT), and feed-forward neural network (FNN)-in predicting carbon emissions. Model accuracy, feature importance, and residual distributions were analyzed. Results show that clinker production and coal consumption are the dominant factors, contributing 83.7% and 11.95% to emissions, respectively. PR and FNN achieved the best performance with R-2 values up to 0.99 and lowest mean square errors (0.11 and 1.82). Their mechanisms were further adapted to improve the generalization of other models. Spatial analysis revealed that North, South, and Southwest China are major emission regions. Using the optimal model, emissions in 2035 are projected to reach 519.14 million tonnes. This study offers technical insights for model optimization and supports low-carbon policymaking in the cement industry.