The future of natural gas consumption in Beijing, Guangdong and Shanghai: An assessment utilizing MARKAL
AbstractNatural gas could possibly become a si0gnificant portion of the future fuel mix in China. However, there is still great uncertainty surrounding the size of this potential market and therefore its impact on the global gas trade. In order to identify some of the important factors that might drive natural gas consumption in key demand areas in China, we focus on three regions: Beijing, Guangdong, and Shanghai. Using the economic optimization model MARKAL, we initially assume that the drivers are government mandates of emissions standards, reform of the Chinese financial structure, the price and available supply of natural gas, and the rate of penetration of advanced power generating and end-use. The results from the model show that the level of natural gas consumption is most sensitive to policy scenarios, which strictly limit SO2 emissions from power plants. The model also revealed that the low cost of capital for power plants in China boosts the economic viability of capital-intensive coal-fired plants. This suggests that reform within the financial sector could be a lever for encouraging increased natural gas use.
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Bibliographic InfoArticle provided by Elsevier in its journal Energy Policy.
Volume (Year): 36 (2008)
Issue (Month): 9 (September)
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Web page: http://www.elsevier.com/locate/enpol
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- Auffhammer, Maximilian & Carson, Richard T., 2007.
"Forecasting the Path of China's CO2 Emissions Using Province Level Information,"
Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series
qt6d28j8rg, Department of Agricultural & Resource Economics, UC Berkeley.
- Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
- Auffhammer, Maximilian & Carson, Richard Taylor, 2004. "Forecasting the path of China's CO2 emissions using province level information," CUDARE Working Paper Series 0971, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy, revised 2007.
- Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
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