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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chen, C. I., Hsin, P. H., & Wu, C. S. (2010). Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model. Expert Systems with Applications, 37(12), 7557–7562.
Deng, J. L. (1982). Control problems of grey systems. Systems and Control Letters, 1(5), 288–294.
Deng, J. L. (2002). The elements of grey theory. Wuhan: HUST Press.
Energy Research Institute of the National Development and Reform Commission of China. (2009). The low carbon development path of China: scenario analysis of energy demand and carbon emission. Bei**g: Sciences Press.
Fu, J., **, J. L., Wei, Y. M., & Jiang, S. M. (2010). Logistic prediction model for Chinese clean energy demand based on genetic algorithm. Water Resources and Power, 28(9), 175–178.
Grimaud, A., & Rougce, L. (2005). Polluting non-renewable resources, innovation and growth: Welfare and environmental policy. Resource and Energy Economics, 27(2), 109–129.
Hao, Y. H., Wang, Y. J., & Zhao, J. J. (2011). Grey system model with time lag and application to simulation of karst spring discharge. Grey Systems: Theory and Application, 1(1), 47–56.
Hsu, L. C. (2009). Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert Systems with Applications, 36(4), 7898–7903.
Kumar, U., & 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, 35(4), 1709–1716.
Lee, Y. S., & Tong, L. I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1), 147–152.
Lee, Y. S., & Tong, L. I. (2012). Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Applied Energy, 94(3), 251–256.
Liang, Q. M., Wei, Y. M., & Fan, Y. (2004). A model for scenario analysis of energy demand and energy intensity and its application in China. Chinese Journal of Management, 1(1), 62–66.
Liu, S. F., Dang, Y. G., & Fang, Z. G. (2004). Grey system theory and its applications. Bei**g: Sciences Press.
Pao, H. T., Fu, H. C., & Tsen, C. L. (2012). Forecasting of \(\text{ CO }_{2}\) emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400–409.
Song, Y. C., & Zhang, Z. Q. (2012). Prediction on energy requirements of China “12th Five Planning” based on ARIMA model. Coal Engineering, 44(1), 76–79. 2012.
Tien, T. L. (2005). The indirect measurement of tensile strength of material by the grey prediction model GMC (1, n). Measurement Science Technology., 16(6), 1322–1328.
Tien, T. L. (2012). A research on the grey prediction model GM (1, n). Applied Mathematics and Computation, 218(9), 4903–4916.
Wang, H. T., & Mu, S. R. (2010). Study on the energy consumption structure and carbon emissions trend in Bei**g. Urban Studies, 17(9), 55–60.
Wang, Z. X. (2014). A GM (1, N)-based economic cybernetics model for the high-tech industries in China. Kybernetes, 43(5), 672–685.
**ng, X. J., & Zhou, D. Q. (2008). The predictive function of Chinese energy demand: Co-integration analysis assisted by principal components. Journal of Applied Statistics and Management, 27(6), 945–951.
Acknowledgments
The author thanks the National Natural Science Foundation of China (Grant Nos. 71101132; 71373226; 71301061), and the Philosophy and Social Science Foundation of Zhejiang Province, China (Grant No. 13ZJQN029YB).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, ZX. A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model. Comput Econ 46, 437–453 (2015). https://doi.org/10.1007/s10614-015-9488-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10614-015-9488-5