Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 765805, 6 pages.

Grey Game Model for Energy Conservation Strategies

Si-Huan Li, Wei-Guo Zhang, and Lian-Sheng Tang

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Abstract

A grey game model is constructed for enterprise carbon emissions reduction and government policies on energy conservation, which explains the interaction equilibrium strategies between government and enterprises with resource constraints. The MATLAB tools are used to simulate the game process, and the result shows that the optimal strategy is consistent with the operation result. Finally, macroscopic countermeasures and suggestions are proposed for government and enterprise.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 765805, 6 pages.

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1412177576

Digital Object Identifier
doi:10.1155/2014/765805

Zentralblatt MATH identifier
07010749

Citation

Li, Si-Huan; Zhang, Wei-Guo; Tang, Lian-Sheng. Grey Game Model for Energy Conservation Strategies. J. Appl. Math. 2014, Special Issue (2014), Article ID 765805, 6 pages. doi:10.1155/2014/765805. https://projecteuclid.org/euclid.jam/1412177576


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References

  • J. P. Rodrigue, C. C. Tois, and B. Slack, The Geography of Trans-port Systems, Routledge, New York, NY, USA, 2nd edition, 2009.
  • http://www.gov.cn/zwgk/2012-08/21/content_2207867.htm.
  • H. D. Waisman, C. Guivarch, and F. Lecocq, “The transportation sector and low-carbon growth pathways: modeling urban, in-frastructure, and spatial determinants of mobility,” Climate Pol-icy, vol. 13, pp. 106–129, 2013.
  • C. Rizet, M. Browne, E. Cornelis, and J. Leonardi, “Assessing carbon footprint and energy efficiency in competing supply chains: review–-case studies and benchmarking,” Transportation Research D, vol. 17, no. 4, pp. 293–300, 2012.
  • R. Dekker, J. Bloemhof, and I. Mallidis, “Operations Research for green logistics–-an overview of aspects, issues, contributions and challenges,” European Journal of Operational Research, vol. 219, no. 3, pp. 671–679, 2012.
  • P. Zhao, B. Liu, L. Xu, and D. Wan, “Location optimization of multi-distribution centers based on low-carbon constraints,” Discrete Dynamics in Nature and Society, vol. 2013, Article ID 427691, 6 pages, 2013.
  • L. Tang, Y. Zeng, C. Wang, and D. Cao, “The logistics policy simulation of energy saving and emission reduction based on system dynamics,” Systems Engineering, no. 6, pp. 87–94, 2013.
  • K. Ohnishi and Y. Tomikawa, Coriolis flowmeter, US, 684 7l6.[P]. 2004–06.
  • C. C. Wu and N. B. Chang, “Grey input-output analysis and its application for environmental cost allocation,” European Jour-nal of Operational Research, vol. 145, no. 1, pp. 175–201, 2003.
  • C. C. Wu and N. B. Chang, “Corporate optimal production planning with varying environmental costs: a grey compromise programming approach,” European Journal of Operational Re-search, vol. 155, no. 1, pp. 68–95, 2004.
  • Z. G. Fang, Research on the grey game theory and its application in economy [Ph.D. thesis], Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2007. \endinput