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Industrial coal demand in China: A provincial analysis

Author

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  • Cattaneo, Cristina
  • Manera, Matteo
  • Scarpa, Elisa
Abstract
The general concern on the environmental implications of the rising demand for coal registered in China during the last few years has induced considerable research effort to produce accurate forecasts of China's energy requirements. Nevertheless, no previous study has modelled the coal demand in China at provincial level. The aim of this paper is twofold. First, we estimate and forecast the Chinese demand for coal using panel data disaggregated by provinces and accounting for spatial heterogeneity. Second, given the spatial nature of the data, we explicitly capture the spatial autocorrelation among provinces using spatial econometrics. In particular, we specify the Chinese industrial coal demand at provincial level with fixed-effect spatial models. The empirical results show that the fixed-effect spatial ADL model is able to capture the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of nearly 2%.

Suggested Citation

  • Cattaneo, Cristina & Manera, Matteo & Scarpa, Elisa, 2011. "Industrial coal demand in China: A provincial analysis," Resource and Energy Economics, Elsevier, vol. 33(1), pages 12-35, January.
    • Handle: RePEc:eee:resene:v:33:y:2011:i:1:p:12-35
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    Cited by:

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    4. Shudong Wang & Qinfeng Xing & Xiangqian Wang & Qian Wu, 2023. "Demand forecasting model of coal logistics based on drosophila-grey neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 807-815, April.
    5. Teng, Meixuan & Burke, Paul J. & Liao, Hua, 2019. "The demand for coal among China's rural households: Estimates of price and income elasticities," Energy Economics, Elsevier, vol. 80(C), pages 928-936.
    6. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "Estimation of potential savings from reducing unburned combustible losses in coal-fired systems," Applied Energy, Elsevier, vol. 87(12), pages 3792-3799, December.
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    8. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
    9. Salisu, Afees A. & Adediran, Idris A., 2019. "Assessing the inflation hedging potential of coal and iron ore in Australia," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    10. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    11. Xin, Haihui & Tian, Wenjiang & Zhou, Banghao & Qi, Xu-yao & Li, Jianfeng & Wu, Jinfeng & Wang, De-ming, 2023. "Pore structure evolution and oxidation characteristic change of coal treated with liquid carbon dioxide and liquid nitrogen," Energy, Elsevier, vol. 268(C).
    12. Lei Jiang & Ling Bai, 2017. "Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
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    More about this item

    Keywords

    Energy demand in China Coal demand in China Chinese provinces Panel data Spatial econometrics Forecasting;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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