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Industrial Coal Demand in China: A Provincial Analysis

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  • Cattaneo, Cristina
  • Manera, Matteo
  • Scarpa, Elisa

Abstract

In recent years, concerns regarding the environmental implications of the rising coal demand have induced considerable efforts to generate long-term forecasts of China’s energy requirements. Nevertheless, none of the previous empirical studies on energy demand for China has tackled the issue of modelling coal demand in China at provincial level. The aim of this paper is to fill this gap. In particular, we model and forecast the Chinese demand for coal using time series data disaggregated by provinces. Moreover, not only does our analysis account for heterogeneity among provinces, but also, given the nature of the data, it captures the presence of spatial autocorrelation among provinces using a spatial econometric model. A fixed effects spatial lag model and a fixed effects spatial error model are estimated to describe and forecast industrial coal demand. Our empirical results show that the fixed effect spatial lag model better captures the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of 4 percent.

Suggested Citation

  • Cattaneo, Cristina & Manera, Matteo & Scarpa, Elisa, 2008. "Industrial Coal Demand in China: A Provincial Analysis," International Energy Markets Working Papers 44425, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemie:44425
    DOI: 10.22004/ag.econ.44425
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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

    Resource /Energy Economics and Policy;

    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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