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Industrial Coal Demand in China: A Provincial Analysis

Author

Listed:
  • Matteo Manera

    (University of Milano-Bicocca)

  • Cristina Cattaneo

    (Fondazione Eni Enrico Mattei, Milan and University of Sussex)

  • Elisa Scarpa

    (Edison Trading)

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 a fixed-effect spatial lag model and a fixed-effect spatial error model. The fixedeffect spatial lag model seems to better capture the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of 4 percent.

Suggested Citation

  • Matteo Manera & Cristina Cattaneo & Elisa Scarpa, 2008. "Industrial Coal Demand in China: A Provincial Analysis," Working Papers 2008.8, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2008.8
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    Cited by:

    1. Burke, Paul J. & Liao, Hua, 2015. "Is the price elasticity of demand for coal in China increasing?," China Economic Review, Elsevier, vol. 36(C), pages 309-322.
    2. 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.
    3. Zheng Zheng Li & Chi-Wei Su, 2023. "How does real estate market react to the iron ore boom in Australian capital cities?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(2), pages 517-537, October.
    4. 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.
    5. 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.
    6. Sun, Sizhong & Anwar, Sajid, 2015. "R&D status and the performance of domestic firms in China's coal mining industry," Energy Policy, Elsevier, vol. 79(C), pages 99-103.
    7. 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.
    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. 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.
    10. 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).
    11. Fang, Zheng & Chen, Yang, 2017. "Human capital and energy in economic growth – Evidence from Chinese provincial data," Energy Economics, Elsevier, vol. 68(C), pages 340-358.
    12. Lei Jiang & Henk Folmer & Minhe Ji & Jianjun Tang, 2017. "Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(2), pages 301-319, March.
    13. 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.
    14. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
    15. Zhang, Kun & Zhang, Zong-Yong & Liang, Qiao-Mei, 2017. "An empirical analysis of the green paradox in China: From the perspective of fiscal decentralization," Energy Policy, Elsevier, vol. 103(C), pages 203-211.

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    More about this item

    Keywords

    Energy demand in China; Coal demand in China; Chinese provinces; Panel data; Spatial econometrics; Forecasting;
    All these keywords.

    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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