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A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model

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  • Zheng-Xin Wang

Abstract

To accurately predict the consumption of clean energy in China, a grey dynamic model is constructed by taking economic growth and environmental regulation as exogenous variables. The Nash equilibrium idea-based optimization method is proposed to solve the parameters of the model so as to obtain better modeling effects than that of the traditional model. The empirical results show that: (1) a spontaneous increasing mechanism of the clean energy consumption has not yet formed in China; (2) both GDP and effluent charge play a positive role in accelerating clean energy consumption in China, but effluent charge has a stronger effect than GDP; (3) clean energy consumption in China is expected to stably increase at an annual rate of 5.73 % averagely in 2012–2020. By 2020, clean energy consumption in China is expected to reach 454.55 million tons of standard coal. The study also provides some policy suggestions of promoting clean energy consumption based on the empirical analysis conclusions. Copyright Springer Science+Business Media New York 2015

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  • Zheng-Xin Wang, 2015. "A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 437-453, October.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:3:p:437-453
    DOI: 10.1007/s10614-015-9488-5
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    1. Grimaud, Andre & Rouge, Luc, 2005. "Polluting non-renewable resources, innovation and growth: welfare and environmental policy," Resource and Energy Economics, Elsevier, vol. 27(2), pages 109-129, June.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    4. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
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