IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16255-d1286668.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chuandi Fang & Jinhua Cheng & Zhe You & Jiahao Chen & Jing Peng, 2023. "A Detailed Examination of China’s Clean Energy Mineral Consumption: Footprints, Trends, and Drivers," Sustainability, MDPI, vol. 15(23), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16255-:d:1286668
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16255/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16255/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin Liu & Heinz Schandl & James West & Meng Jiang & Zijian Ren & Dingjiang Chen & Bing Zhu, 2022. "Copper ore material footprints and transfers embodied in domestic and international trade of provinces in China," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1423-1436, August.
    2. Islam, Md. Monirul & Sohag, Kazi & Alam, Md. Mahmudul, 2022. "Mineral import demand and clean energy transitions in the top mineral-importing countries," Resources Policy, Elsevier, vol. 78(C).
    3. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    4. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    5. Yi, Jiahui & Dai, Sheng & Cheng, Jinhua & Wu, Qiaosheng & Liu, Kailei, 2021. "Production quota policy in China: Implications for sustainable supply capacity of critical minerals," Resources Policy, Elsevier, vol. 72(C).
    6. repec:ulb:ulbeco:2013/136280 is not listed on IDEAS
    7. Arnold Tukker & Arjan de Koning & Anne Owen & Stephan Lutter & Martin Bruckner & Stefan Giljum & Konstantin Stadler & Richard Wood & Rutger Hoekstra, 2018. "Towards Robust, Authoritative Assessments of Environmental Impacts Embodied in Trade: Current State and Recommendations," Journal of Industrial Ecology, Yale University, vol. 22(3), pages 585-598, June.
    8. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    9. Dominicy, Yves & Veredas, David, 2013. "The method of simulated quantiles," Journal of Econometrics, Elsevier, vol. 172(2), pages 235-247.
    10. Manfred Lenzen & Arne Geschke & James West & Jacob Fry & Arunima Malik & Stefan Giljum & Llorenç Milà i Canals & Pablo Piñero & Stephan Lutter & Thomas Wiedmann & Mengyu Li & Maartje Sevenster & Janez, 2022. "Implementing the material footprint to measure progress towards Sustainable Development Goals 8 and 12," Nature Sustainability, Nature, vol. 5(2), pages 157-166, February.
    11. Jiang, Wei & Sun, Yifei, 2023. "Which is the more important factor of carbon emission, coal consumption or industrial structure?," Energy Policy, Elsevier, vol. 176(C).
    12. Guliyev, Hasraddin & Mustafayev, Eldayag, 2022. "Predicting the changes in the WTI crude oil price dynamics using machine learning models," Resources Policy, Elsevier, vol. 77(C).
    13. Victor Platon & Florin Marius Pavelescu & Marius Surugiu & Simona Frone & Raluca Mazilescu & Andreea Constantinescu & Florina Popa, 2023. "Influence of Eco-Innovation and Recycling on Raw Material Consumption; Econometric Approach in the Case of the European Union," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    14. Richard Wood & Konstantin Stadler & Moana Simas & Tatyana Bulavskaya & Stefan Giljum & Stephan Lutter & Arnold Tukker, 2018. "Growth in Environmental Footprints and Environmental Impacts Embodied in Trade: Resource Efficiency Indicators from EXIOBASE3," Journal of Industrial Ecology, Yale University, vol. 22(3), pages 553-564, June.
    15. Islam, Md. Monirul & Irfan, Muhammad & Shahbaz, Muhammad & Vo, Xuan Vinh, 2022. "Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality," Renewable Energy, Elsevier, vol. 184(C), pages 1130-1149.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Lan & Jiang, Hua, 2023. "Analysis of predictions considering mineral prices, residential energy, and environmental risk: Evidence from the USA in COP 26 perspective," Resources Policy, Elsevier, vol. 82(C).
    2. Islam, Md. Monirul & Sohag, Kazi & Mariev, Oleg, 2024. "Mineral import demand-driven solar energy generation in China: A threshold estimation using the counterfactual shock approach," Renewable Energy, Elsevier, vol. 221(C).
    3. Teddy Serrano & Sandra Aparcana & Fatemeh Bakhtiari & Alexis Laurent, 2021. "Contribution of circular economy strategies to climate change mitigation: Generic assessment methodology with focus on developing countries," Journal of Industrial Ecology, Yale University, vol. 25(6), pages 1382-1397, December.
    4. Zhang, Zhenya & Chang, Zheren & Gan, Yufei & Li, Jiayan, 2025. "Renewable energy, innovation, and stock markets: Machine learning perspectives on environmental sustainability," International Review of Financial Analysis, Elsevier, vol. 97(C).
    5. Emanuel Kohlscheen, 2024. "Forecasting oil prices with random forests," Empirical Economics, Springer, vol. 66(2), pages 927-943, February.
    6. Lin Liu & Heinz Schandl & James West & Meng Jiang & Zijian Ren & Dingjiang Chen & Bing Zhu, 2022. "Copper ore material footprints and transfers embodied in domestic and international trade of provinces in China," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1423-1436, August.
    7. Viglioni, Marco Túlio Dinali & Calegario, Cristina Lelis Leal & Bruhn, Nádia Campos Pereira, 2025. "Effects of economic complexity and metallic mineral resources on renewable energy transition in developing countries," Resources Policy, Elsevier, vol. 102(C).
    8. Alexandra Leclerc & Etienne Berthet & Konstantin Stadler & Richard Wood & Alexis Laurent, 2023. "Toward global and national toxicity footprints," Journal of Industrial Ecology, Yale University, vol. 27(6), pages 1521-1537, December.
    9. Dorninger, Christian & Hornborg, Alf & Abson, David J. & von Wehrden, Henrik & Schaffartzik, Anke & Giljum, Stefan & Engler, John-Oliver & Feller, Robert L. & Hubacek, Klaus & Wieland, Hanspeter, 2021. "Global patterns of ecologically unequal exchange: Implications for sustainability in the 21st century," Ecological Economics, Elsevier, vol. 179(C).
    10. Li, Xinyi & Zhang, Chao & Yang, Xue & Xia, Ziqian & Cao, Zhi & Wang, Peng & Wang, Heming & Wang, Tao & Liu, Gang & Chen, Wei-Qiang, 2025. "Hybrid multi-stage steel footprinting unveils a more interdependent material foundation of the global economy," Ecological Economics, Elsevier, vol. 227(C).
    11. Gilang Hardadi & Alexander Buchholz & Stefan Pauliuk, 2021. "Implications of the distribution of German household environmental footprints across income groups for integrating environmental and social policy design," Journal of Industrial Ecology, Yale University, vol. 25(1), pages 95-113, February.
    12. Zheng, Li & Abbasi, Kashif Raza & Salem, Sultan & Irfan, Muhammad & Alvarado, Rafael & Lv, Kangjuan, 2022. "How technological innovation and institutional quality affect sectoral energy consumption in Pakistan? Fresh policy insights from novel econometric approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    14. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    15. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    16. Gizem Uzuner & Festus Victor Bekun & Kayode Kolawole Eluwole & Andrew Adewale Alola, 2025. "Are export and capital accumulation panacea for economic growth in Mexico?: a non-linearity evidence," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(5), pages 4559-4579, October.
    17. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    18. Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    19. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    20. Sascha O. Becker & Thiemo Fetzer, 2018. "Has Eastern European Migration Impacted UK-born Workers?," CAGE Online Working Paper Series 376, Competitive Advantage in the Global Economy (CAGE).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16255-:d:1286668. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.