Share:


Game equilibrium based control analysis on the sustainable market structure of rare metal mineral resources – evidence from China

    Shijie Ding Affiliation
    ; Jianbai Huang Affiliation
    ; Xiaodan Zhang Affiliation
    ; Meirui Zhong Affiliation

Abstract

In rare metal mineral market, as a complex system, multiple decision-making among the stakeholders increases the complexity in its market structure and dynamic process. The unreasonable compensation pricing mechanism for the development of the rare metal mineral resources in China requires to be studied. Drawing on the methods of game theory model and chaos control analysis, this paper builds theoretical model of rare metal mineral market structure, corporating related parameters of rare metal in the game theory model, to conduct the chaotic nature and path analysis, expecting to solve the bottleneck problems that restrict the rare metal pricing and resource security and enhance the waste valorization for the sustainability. Specificly, a Cournot-Nash Equilibrium model is built to analyze the Cournot-equilibrium point, the stability of the Cournot Equilibrium point, the chaotic status, as well as the pattern to chaos of the game system in the rare metal mineral resource market, numerical simulation is used to verify the model. The conclusions facilitate the formulation of industrial economic policies and further improvement of managerial strategies to solve market problems.

Keyword : rare metal minerals, multiple decision-making, equilibrium price, complexity analysis, numerical simulation

How to Cite
Ding, S., Huang, J., Zhang, X., & Zhong, M. (2021). Game equilibrium based control analysis on the sustainable market structure of rare metal mineral resources – evidence from China. Journal of Environmental Engineering and Landscape Management, 29(2), 73-84. https://doi.org/10.3846/jeelm.2021.14186
Published in Issue
May 13, 2021
Abstract Views
416
PDF Downloads
343
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adelman, M. A., & Waltins, G. C. (2008). Reserve prices and mineral resource theory. The Energy Journal, 29, 1–16. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol29-NoSI-1

Afflerbach, P., Fridgen, G., Keller, R., Rathgeber, A. W., & Strobel, F. (2014). The by-product effect on metal markets – New insights to the price behavior of minor metals. Resources Policy, 42, 35–44. https://doi.org/10.1016/j.resourpol.2014.08.003

Alexander, R., Achim, W., & Kristina, W. (2012). Bargaining and inequity aversion: On the efficiency of the double auction. Economics Letters, 114(2), 178–181. https://doi.org/10.1016/j.econlet.2011.09.027

Begossi, A. (2014). Ecological, cultural, and economic approaches to managing artisanal fisheries. Environment, Development and Sustainability, 16, 5–34. https://doi.org/10.1007/s10668-013-9471-z

Chen, J. Y., Zhu, X. H., & Li, H. L. (2020). The pass-through effects of oil price shocks on China’s inflation: A time-varying analysis. Energy Economics, 86, 1–12. https://doi.org/10.1016/j.eneco.2020.104695

Chen, Q. S., Yu, W. J., & Zhang, Y. F. (2016). Dianshi: research on global mineral resource industry development in the next 20 years. Science Press.

Clearwater, S. (1996). Market-based control: A paradigm for distributed resource allocation. World Scientific. https://doi.org/10.1142/2741

Du, F. L., Ren, Y., Dong, J. Z., Ma H. F., Zhang, X. M., & Zhang, L. (2017). The optimal extraction paths under different rare-earth market structure combinations. China Polulation.Resources and Environment, 27(2), 109–116.

Garnier, J., & Solna, K. (2019). Chaos and order in the bitcoin market. Physica A, 524, 708–721. https://doi.org/10.1016/j.physa.2019.04.164

Gong, X., & Lin, B. Q. (2017).The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market. Energy Economics, 74, 370–386. https://doi.org/10.1016/j.eneco.2018.06.005

Gong, X., & Lin, B. Q. (2018). Structural breaks and volatility forecasting in the copper futures market. Journal of Futures Markets, 38(3), 290–339. https://doi.org/10.1002/fut.21867

Hartwick, J. M. (1977). Intergenerational equity and the investing of rents from exhaustible resources. American Economics Review, 67(5), 972–974.

He, H. L., & Chen, L. (2017). Vertical related markets, resource tax reform and the export of China’s rare earth: Evaluation of implementation effect of volume-based and ad valorem tax of resources. The Study of Finance and Economics, 43(7), 95–106.

Huang, J.-b., Tan, N., & Zhong, M.-r. (2014). Incorporating overconfidence into real option decision-making model of metal mineral resources mining project. Discrete Dynamics in Nature and Society, 2014, 232516. https://doi.org/10.1155/2014/232516

Jia, S. X., Huang, J. B., & Zhong, M. R. (2017). Research on balance of benefits of metal compensation and ecological compensation under the construction of ecological civilization system. Chinese Journal of Management Science, 25(11), 122–133.

Li, H. L., Chen, J. Y., Zhu, X. H., & Jiang, F. T. (2019). Environmental regulations, environmental governance efficiency and the green transformation of China’s iron and steel enterprises. Ecological Economics, 165, 1–14, 106397. https://doi.org/10.1016/j.ecolecon.2019.106397

Lima, L. S., & Santos, G. K. C. (2018). Stochastic process with multiplicative structure for the dynamic behavior of the financial market. Physical A, 512, 222–229. https://doi.org/10.1016/j.physa.2018.08.049

Liu, T. Y., & Gong, X. (2020). Analyzing time-varying volatility spillovers between the crude oil markets using a new method. Energy Economics, 87, 104711. https://doi.org/10.1016/j.eneco.2020.104711

Ma, J. H., & Ji, W. Z. (2009). Complexity of repeated game model in electric power triopoly. Chaos, Solitons and Fractal, 40(4). 1735–1740. https://doi.org/10.1016/j.chaos.2007.09.058

Northey, S. A., Mudd, G. M., & Werner, T. T. (2017). Unresolved complexity in assessments of mineral resource depletion and availability. Natural Resources Research, 27, 241–255. https://doi.org/10.1007/s11053-017-9352-5

Prior, T., Giurco, D., Mudd, G., Mason, L., & Behrisch, J. (2012). Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change, 22(3), 577–587. https://doi.org/10.1016/j.gloenvcha.2011.08.009

Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica, 50(1). 13–21. https://doi.org/10.2307/1912531

Sarjiya, Budi, R. F. S., & Hadi, S. P. (2019). Game theory for multi-objective and multi-period framework generation expansion planning in deregulated markets. Energy, 174, 323–330. https://doi.org/10.1016/j.energy.2019.02.105

Song, W. T., Huang, J. B., Zhong, M. R., & Wen, F. H. (2019a). The impacts of nonferrous metal price shocks on the macroeconomy in China form the perspective of resource security. Journal of Cleaner Production, 213, 688–699. https://doi.org/10.1016/j.jclepro.2018.12.037

Song, Y. J., Ji, Q., Du, Y. J., & Geng, J. B. (2019b). The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets. Energy Economics, 84, 104564. https://doi.org/10.1016/j.eneco.2019.104564

Tošović, R., Dašić, P., & Ristović, I. (2016). Sustainable use of metallic mineral resources of serbia from an environmental perspective. Environmental Engineering & Management Journal, 15(9), 2075–2084. https://doi.org/10.30638/eemj.2016.224

Tse, P. K. (2011). China’s rare-earth industry. Reston: US Department of the Interior, US Geological Survey.

Tsionas, M. G., & Michaelides, P. G. (2017). Neglected chaos in international stock markets: Bayesian analysis of the joint return-volatility dynamical system. Physica A, 482, 95–107. https://doi.org/10.1016/j.physa.2017.04.060

Wang, Z. T., Zheng, L., Zhao, T., & Jian, J. F. (2019). Mitigation strategies for over use of Chinese bikesharing systems based on game theory analyses of three generations worldwide. Journal of Cleaner Production, 227, 447–456. https://doi.org/10.1016/j.jclepro.2019.04.100

Yang, Q., Zhang, L., & Wang, X. (2017). Dynamic analysis on market structure of China’s coal industry. Energy Policy, 106, 498–504. https://doi.org/10.1016/j.enpol.2017.04.001

Zhang, H., Zhu, X., Guo, Y., & Liu, H. (2018). A separate reduced-form volatility forecasting model for nonferrous metal market: Evidence from copper and aluminum. Journal of Forecasting, 37(7), 754–766. https://doi.org/10.1002/for.2523

Zhang, W., Liu, Z., Wang, M. J., & Yang, X. S. (2009). Research status and development trend of smart grid. Power System Technology, 13, 1–11.

Zhong, M. R., Chen, J. Y., Zhu, X. H., & Huang, J. B. (2013). Strategic equilibrium price analysis and numerical simulation of preponderant high-tech metal mineral resources. Transactions of Nonferrous Metals Society of China, 23(10), 3153−3160. https://doi.org/10.1016/S1003-6326(13)62846-0

Zhong, M. R., He, R. F., Chen, J. Y., & Huang, J. B. (2019). Timevarying effects of international nonferrous metal price shocks on China’s industrial economy. Physical A, 528, 121299. https://doi.org/10.1016/j.physa.2019.121299

Zhong, M. R., Zeng, A. Q., & Huang, J. B. (2016). The analysis of pricing power of preponderant metal mineral under the perspective of intergenerational equity and social preferences – An analytical framework based on Cournot Equilibrium Model. Chinese Journal of Management Science, 24(1), 47–55.