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 R2 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.

Machine learning-based carbon emission prediction and influence factor analysis discussion in China cement industry
    Abstract
    1 Introduction
    2 Data and research methodology
        2.1 Description of the data
        2.2 Data pre-processing
            2.2.1 Handling of outliers
            2.2.2 Standardization
            2.2.3 Data segmentation
        2.3 Machine learning models
            2.3.1 Ridge regression
            2.3.2 Polynomial regression
            2.3.3 Random forests
            2.3.4 Support vector machine regression
            2.3.5 Gradient boosted regression trees
            2.3.6 Feedforward neural networks
    3 Results and discussion
        3.1 Feature importance analysis
        3.2 Residual analysis and kernel density estimation
            3.2.1 Residual distribution
            3.2.2 Kernel density estimation
        3.3 Absolute cumulative error
        3.4 Comparison of the performance of the models
        3.5 Sectoral carbon emissions and spatial distribution
            3.5.1 Carbon emissions from industries with higher carbon emissions by province
            3.5.2 Distribution of CO2 emissions
            3.5.3 China cement production and growth rate
        3.6 Carbon dioxide emission control measures in the cement industry
        3.7 Mechanism analysis and model improvement of FNN and PR models
    4 Conclusion
    References

• Chao‑qiang Wang(School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China) Corresponding author
• An‑ping Zuo(School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
• Yan‑yan Liu(School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)