Energy Consumption in China: Past Trends and Future Directions
AbstractIn 2003 China’s energy consumption amounted to 1678 million tonnes coal equivalent (MTCE), making China the world’s second largest consumer behind only the United States. China is now also one of the largest oil importers in the world. With an economy which is expected to maintain a rate of growth of 7 to 8 per cent for decades, China’s role in the world energy market becomes increasingly influential. This makes it important to predict China’s future demand for energy. The objective of this paper is to apply the Bayesian vector autoregressive methodology to forecast China’s energy consumption and to discuss potential implications. The results of this paper suggest that total energy consumption should increase to 2173 MtCE in 2010, an annual growth rate of 3.8 per cent which is slightly slower than the average rate in the past decade. The slower growth reflects an expected slower economic growth and the decline in energy consumption due to structural changes in the Chinese economy.
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Bibliographic InfoPaper provided by The University of Western Australia, Department of Economics in its series Economics Discussion / Working Papers with number 04-22.
Length: 21 pages
Date of creation: 2004
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China; Energy consumption; Bayesian vector autoregression;
Other versions of this item:
- Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
- Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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- Sinton, Jonathan E. & Fridley, David G., 2000. "What goes up: recent trends in China's energy consumption," Energy Policy, Elsevier, vol. 28(10), pages 671-687, August.
- Christopher A. Sims, 1993.
"A Nine-Variable Probabilistic Macroeconomic Forecasting Model,"
in: Business Cycles, Indicators and Forecasting, pages 179-212
National Bureau of Economic Research, Inc.
- Christopher A. Sims, 1992. "A Nine Variable Probabilistic Macroeconomic Forecasting Model," Cowles Foundation Discussion Papers 1034, Cowles Foundation for Research in Economics, Yale University.
- Christopher A. Sims, 1989. "A nine variable probabilistic macroeconomic forecasting model," Discussion Paper / Institute for Empirical Macroeconomics 14, Federal Reserve Bank of Minneapolis.
- John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
- Rossana Galli, 1998. "The Relationship Between Energy Intensity and Income Levels: Forecasting Long Term Energy Demand in Asian Emerging Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 85-105.
- Summers, Peter M., 2001. "Forecasting Australia's economic performance during the Asian crisis," International Journal of Forecasting, Elsevier, vol. 17(3), pages 499-515.
- Sims, Christopher A & Zha, Tao, 1998.
"Bayesian Methods for Dynamic Multivariate Models,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-68, November.
- Robert B. Litterman, 1985.
"Forecasting with Bayesian vector autoregressions five years of experience,"
274, Federal Reserve Bank of Minneapolis.
- Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
- Hing Lin Chan & Shu Kam Lee, 1996. "Forecasting the Demand for Energy in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 19-30.
- Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, issue Q 1, pages 16-29.
- von Hirschhausen, Christian & Andres, Michael, 2000. "Long-term electricity demand in China -- From quantitative to qualitative growth?," Energy Policy, Elsevier, vol. 28(4), pages 231-241, April.
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986.
"Forecasting and conditional projection using realistic prior distribution,"
93, Federal Reserve Bank of Minneapolis.
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
- Huang, Jin-ping, 1993. "Industry energy use and structural change : A case study of The People's Republic of China," Energy Economics, Elsevier, vol. 15(2), pages 131-136, April.
- Fisher-Vanden, Karen & Jefferson, Gary H. & Liu, Hongmei & Tao, Quan, 2004. "What is driving China's decline in energy intensity?," Resource and Energy Economics, Elsevier, vol. 26(1), pages 77-97, March.
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