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Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model

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  • Yu, Shiwei
  • Wei, Yi-ming

Abstract

This paper proposes a hybrid model based on genetic algorithm (GA) and system dynamics (SD) for coal production–environmental pollution load in China. GA has been utilized in the optimization of the parameters of the SD model to reduce implementation subjectivity. The chain of “Economic development–coal demand–coal production–environmental pollution load” of China in 2030 was predicted, and scenarios were analyzed. Results show that: (1) GA performs well in optimizing the parameters of the SD model objectively and in simulating the historical data; (2) The demand for coal energy continuously increases, although the coal intensity has actually decreased because of China's persistent economic development. Furthermore, instead of reaching a turning point by 2030, the environmental pollution load continuously increases each year even under the scenario where coal intensity decreased by 20% and investment in pollution abatement increased by 20%; (3) For abating the amount of “three types of wastes”, reducing the coal intensity is more effective than reducing the polluted production per tonne of coal and increasing investment in pollution control.

Suggested Citation

  • Yu, Shiwei & Wei, Yi-ming, 2012. "Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model," Energy Policy, Elsevier, vol. 42(C), pages 521-529.
    • Handle: RePEc:eee:enepol:v:42:y:2012:i:c:p:521-529
    DOI: 10.1016/j.enpol.2011.12.018
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    15. Yu, Shiwei & Gao, Siwei & sun, Han, 2016. "A dynamic programming model for environmental investment decision-making in coal mining," Applied Energy, Elsevier, vol. 166(C), pages 273-281.
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