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Coal mining in Indonesia: forecasting by the growth curve method


  • Fadhila Achmadi Rosyid

    (Akita University)

  • Tsuyoshi Adachi

    (Akita University)


Coal, a fossil fuel energy resource, plays an important role in the economic development of a country and thus, research on the sustainability of long-term coal supply is a continuous challenge, be it the amount of future supply and time of peak production. This study focuses on the outlook and forecasting of Indonesian coal production. The scope of analysis includes current and future resources, reserves, and production of Indonesian coal. To forecast coal production, this study uses two types of growth curves: logistic and Gompertz curves. To obtain more realistic forecasting, the data applied are based on data from each economic coal basin instead of country-level data. Moreover, production forecasting is carried out by applying two scenarios of ultimate recoverable reserves: scenario 1, calculated based on quoted reserves data, and scenario 2, estimated based on the Hubbert linearization theory. The forecasting results are analyzed to choose the most realistic result considering depletion rate and supply anticipation of future coal demand. The most realistic forecast is provided by the Gompertz curve and scenario 1 using quoted reserves data. Coal production is forecast to peak at 485 million tons in 2026.

Suggested Citation

  • Fadhila Achmadi Rosyid & Tsuyoshi Adachi, 2016. "Coal mining in Indonesia: forecasting by the growth curve method," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 29(2), pages 71-85, December.
  • Handle: RePEc:spr:minecn:v:29:y:2016:i:2:d:10.1007_s13563-016-0091-6
    DOI: 10.1007/s13563-016-0091-6

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    References listed on IDEAS

    1. Patzek, Tadeusz W. & Croft, Gregory D., 2010. "A global coal production forecast with multi-Hubbert cycle analysis," Energy, Elsevier, vol. 35(8), pages 3109-3122.
    2. Vikström, Hanna & Davidsson, Simon & Höök, Mikael, 2013. "Lithium availability and future production outlooks," Applied Energy, Elsevier, vol. 110(C), pages 252-266.
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    1. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).

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