China’s Chrome Demand Forecast from 2025 to 2040: Based on Sectoral Predictions and PSO-BP Neural Network

Baohua Du, Hongye Feng, Zhen Zhang, Qunyi Liu, Hongjian Zhu, Guwang Liu (), Lei Liu, Xiuli Han (), Xuguang Zhao and Shuai Li
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Baohua Du: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
Hongye Feng: Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Zhen Zhang: Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Qunyi Liu: Chinese Academy of Geological Sciences, Beijing 100037, China
Hongjian Zhu: School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China
Guwang Liu: Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Lei Liu: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
Xiuli Han: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
Xuguang Zhao: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
Shuai Li: College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China

Sustainability, 2025, vol. 17, issue 20, 1-21

Abstract: Chromium is a critical material for stainless steel production. With economic growth and the optimization and upgrading of industrial structure, China’s demand for chromium has been increasing year by year. Conducting research on chromium demand forecasting holds significant practical implications for the sustainable development of China’s chromium industrial chain. China’s chromium consumption accounts for one-third of the global, over 95% of which has long-term depended on imports, and 90% of which is used in stainless steel production. In this paper, a linear correlation model between chromium consumption and stainless steel production is constructed by using the department demand forecasting method. The importance of influencing factors on chromium demand is analyzed using the gray correlation degree, and a PSO-BP neural network algorithm is constructed to predict China’s chromium demand from 2025 to 2040. The results indicate that the predictions of the two methods are relatively consistent, with demand for chromium expected to peak in 2035 and then decline gradually thereafter. This provides an important reference basis for the security and sustainable development of China’s chromium supply chain.

Keywords: chromium; sustainable development; department demand forecast; PSO-BP neural network model; stainless steel production (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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