A Detailed Examination of China’s Clean Energy Mineral Consumption: Footprints, Trends, and Drivers

Chuandi Fang, Jinhua Cheng, Zhe You (), Jiahao Chen and Jing Peng
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Chuandi Fang: School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China
Jinhua Cheng: School of Economics and Management, China University of Geosciences, Wuhan 430205, China
Zhe You: School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China
Jiahao Chen: School of Economics and Management, China University of Geosciences, Wuhan 430205, China
Jing Peng: School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China

Sustainability, 2023, vol. 15, issue 23, 1-26

Abstract: As the global clean energy transition accelerates, China’s mining industry faces pressing challenges concerning the sustainable consumption of clean energy minerals. This study employed the EE-MRIO model to investigate the consumption trends of clean energy minerals across various provinces and industries in China from 2012 to 2017, specifically focusing on the resource footprints of copper, nickel, molybdenum, zinc, and cobalt. Using the random forest model, we identified the driving factors, with the goal of offering a solid scientific foundation for strategic decision making. Our findings reveal marked disparities in resource footprints among provinces, which are correlated with regional industrialization, urbanization trends, and resource reserves. Beyond the traditional resource-intensive sectors, industries like finance and real estate have significantly impacted the resource footprint. Monte Carlo simulations further validated the reliability of our model. The random forest analysis indicates that population size and energy consumption mainly determine the footprints of copper and zinc. In contrast, the footprints of nickel and cobalt are primarily influenced by technology market turnover, while molybdenum’s footprint is largely driven by population size and total carbon emissions. Drawing from these insights, we suggest several policy recommendations for clean energy mineral extraction. These include fostering inter-provincial resource collaboration, bolstering geological exploration and assessment, promoting technological innovation, advancing environmentally friendly mineral extraction techniques, and enhancing collaboration between urban planning and pivotal industries.

Keywords: resource footprint; EE-MRIO model; Monte Carlo simulation; random forest regression; environmental impact flows; determinants and drivers (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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