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Exploring Chromium Ore Consumption: New Perspectives from Hybrid CEEMDAN–S-Curve Modeling

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Abstract

Chromium ore is an important metallic raw material that is widely used in the metallurgy industry, chemical industry, and refractory. Clarifying the consumption mechanism of chromium ore is crucial for policy making, enterprise production, and commodity investment. Based on the signal decomposition tool and S-curve model, a new hybrid complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–S-curve model is proposed to analyze chromium ore consumption of different countries for the last 100 years. The results showed the following. (1) Per capita chromium ore consumption can be decomposed into low-frequency, medium-frequency, and high-frequency components, which contribute more than 55%, 10–30%, and less than 15%, respectively, to the volatility of the original series. These can be interpreted as economic development represented by GDP per capita, shocks induced by significant events, and normal market disequilibrium, respectively. (2) The CEEMDAN–S-curve facilitates understanding and data logic of data by linking consumption to end-use segments. (3) A new strategy is provided to analyze the consumption mechanism of other commodities for future modelers. Moreover, based on the results, a series of topics related to chromium ore consumption are discussed, such as resource recovery, environmental pollution, CO2 emissions, and consumption trends. The discussions emphasize that, to reach sustainable development goals, a series of measures should be implemented, such as developing advanced smelting technology, recycling technology, and effective enforcement measures.

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Figure 1

Sources: Zhang et al, 2019; USGS, 2021; Market Watch, 2022

Figure 2

Sources: ISSF, 2021; Gao et al., 2021

Figure 3

Sources: BGS (British Geological Survey), USGS (U.S. Geological Survey), WBMS (World Bureau of Metal Statistics)

Figure 4
Figure 5
Figure 6
Figure 7

Sources: BGS (British Geological Survey), USGS (U.S. Geological Survey), UN Comtrade, WBMS (World Bureau of Metal Statistics)

Figure 8

Sources: BGS (British Geological Survey), USGS (U.S. Geological Survey), WB (The World Bank), UN Comtrade, WBMS (World Bureau of Metal Statistics); data of population and GDP per capita are from UN (United Nations) and GGDC (The Groningen Growth and Development Centre). *Note: Urbanization rate = Urban population/ total population × 100%. India is a developing country with a small urban population, so its urbanization rate keeps the low level. Therefore, there will be obvious gap with other developed countries in (c)

Figure 9

Sources: BGS (British Geological Survey), USGS (U.S. Geological Survey), WB (The World Bank), UN Comtrade, WBMS (World Bureau of Metal Statistics); data of population and GDP per capita are from UN (United Nations) and GGDC (The Groningen Growth and Development Centre)

Figure 10
Figure 11
Figure 12
Figure 13

Sources: CISA (China Iron and Steel Association), General Administration of Customs of P. R. China, National Bureau of Statistics of P. R. China, WBMS

Figure 14

Sources: General Administration of Customs of P. R. China, Ministry of Natural Resources of P. R. China, National Bureau of Statistics of P.R. China

Figure 15

Sources: CISA (China Iron and Steel Association), General Administration of Customs of P. R. China, National Bureau of Statistics of P. R. China, WBMS

Figure 16
Figure 17
Figure 18

Sources: CISA, ISSF

Figure 19

Source: ISSF

Figure 20

Sources: IAI, ISSF, WSA

Figure 21

Sources: ISSF, WSA; data of population and GDP per capita are from UN (United Nations) and GGDC (The Groningen Growth and Development Centre)

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Notes

  1. MATLAB code is available at: http://perso.ens-lyon.fr/patrick.flandrin/emd.html.

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Acknowledgments

This research is supported by grants from the geological surveying projects of China Geological Survey (Grant No. DD20190606) and Qinghai Salt Lake Industry Co., Ltd (Grant No. HE2221).

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  1. Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Zhaoshuai Pan, Zhaozhi Zhang & Dong Che

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  1. Zhaoshuai Pan
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  2. Zhaozhi Zhang
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Correspondence to Zhaozhi Zhang.

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Pan, Z., Zhang, Z. & Che, D. Exploring Chromium Ore Consumption: New Perspectives from Hybrid CEEMDAN–S-Curve Modeling. Nat Resour Res 32, 929–953 (2023). https://doi.org/10.1007/s11053-023-10176-6

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