Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model

Hsiao-Tien Pao*, Hsin Chia Fu, Cheng Lung Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

216 Scopus citations

Abstract

Analyses and forecasts of carbon emissions, energy consumption and real outputs are key requirements for clean energy economy and climate change in rapid growth market such as China. This paper employs the nonlinear grey Bernoulli model (NGBM) to predict these three indicators and proposes a numerical iterative method to optimize the parameter of NGBM. The forecasting ability of NGBM with optimal parameter model, namely NGBM-OP has remarkably improved, compared to the GM and ARIMA. The MAPEs of NGBM-OP for out-of-sample (2004-2009) are ranging from 1.10 to 6.26. The prediction results show that China's compound annual emissions, energy consumption and real GDP growth is set to 4.47%, -0.06% and 6.67%, respectively between 2011 and 2020. The co-integration results show that the long-run equilibrium relationship exists among these three indicators and emissions appear to be real output inelastic and energy consumption elastic. The estimated values cannot support an EKC hypothesis, and real output is significantly negative impact on emissions. In order to promote economic and environmental quality, the results suggest that China should adopt the dual strategy of increasing energy efficiency, reducing the loss in power transmission and distribution and stepping up energy conservation policies to reduce any unnecessary wastage of energy.

Original languageEnglish
Pages (from-to)400-409
Number of pages10
JournalEnergy
Volume40
Issue number1
DOIs
StatePublished - 1 Jan 2012

Keywords

  • China
  • Co-integration technique
  • Grey prediction model
  • Nonlinear grey Bernoulli model

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