Decision Support for Carbon Emission Reduction Strategies in China’s Cement Industry: Prediction and Identification of Influencing Factors

Xiangqian Li, Keke Li, Yaxin Tian, Siqi Shen, Yue Yu, Liwei Jin, Pengyu Meng, Jingjing Cao and Xiaoxiao Zhang ()
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Xiangqian Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Keke Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Yaxin Tian: School of Finance, Capital University of Economics and Business, Beijing 100070, China
Siqi Shen: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Yue Yu: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Liwei Jin: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Pengyu Meng: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Jingjing Cao: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Xiaoxiao Zhang: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101126, China

Sustainability, 2024, vol. 16, issue 13, 1-17

Abstract: China is one of the world’s largest producers and consumers of cement, making carbon emissions in the cement industry a focal point of current research and practice. This study explores the prediction of cement consumption and its influencing factors across 31 provinces in China using the RF-MLP-LR model. The results show that the RF-MLP-LR model performs exceptionally well in predicting cement consumption, with the Mean Absolute Percentage Error (MAPE) below 10% in most provinces, indicating high prediction accuracy. Specifically, the model outperforms traditional models such as Random Forest (RF), Multi-Layer Perceptron (MLP), and Logistic Regression (LR), especially in handling complex scenarios or specific regions. The study also conducts an in-depth analysis of key factors influencing cement consumption, highlighting the significant impact of factors such as per capita GDP, per capita housing construction area, and urbanization rate. These findings provide important insights for policy formulation, aiding the transition of China’s cement industry towards low-carbon, sustainable development, and contributing positively to achieving carbon neutrality goals.

Keywords: cement consumption; machine learning; carbon neutrality; prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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