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Determination of the effect of operating cost uncertainty on mining project evaluation

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  • Dehghani, Hesam
  • Ataee-pour, Majid

Abstract

Mining projects are complex businesses that demand constant risk assessment. This is because several kinds of uncertainties influence the value of a mine project, typically. These uncertainties may be classified as exploration uncertainties, economic uncertainties and engineering uncertainties. The evaluation of a mine project under these uncertainties is a complicated job, which may lead to making a wrong decision by managers and stockholders. Therefore, at first, the engineers must recognize the mining uncertainties before carrying out the project evaluation. The economic uncertainties are the most important factors, which may affect the project evaluation. Among the mentioned uncertainties, the operating cost uncertainty is an important and effective factor, which is ignored to a certain extent.

Suggested Citation

  • Dehghani, Hesam & Ataee-pour, Majid, 2012. "Determination of the effect of operating cost uncertainty on mining project evaluation," Resources Policy, Elsevier, vol. 37(1), pages 109-117.
    • Handle: RePEc:eee:jrpoli:v:37:y:2012:i:1:p:109-117
    DOI: 10.1016/j.resourpol.2011.11.001
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    References listed on IDEAS

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    Cited by:

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    2. Kamel, Ahmed & Elwageeh, Mohamed & Bonduà, Stefano & Elkarmoty, Mohamed, 2023. "Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt," Resources Policy, Elsevier, vol. 80(C).
    3. Michał Patyk & Przemysław Bodziony, 2024. "Empirical Analysis of Mining Costs Amid Energy Price Volatility for Secondary Deposits in Quarrying," Energies, MDPI, vol. 17(3), pages 1-19, February.
    4. Kuangyuan Zhang & Richard Olawoyin & Antonio Nieto & Andrew N. Kleit, 2018. "Risk of commodity price, production cost and time to build in resource economics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(6), pages 2521-2544, December.
    5. Dehghani, Hesam & Ataee-pour, Majid & Esfahanipour, Akbar, 2014. "Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree," Resources Policy, Elsevier, vol. 39(C), pages 124-133.
    6. Guo, Hongquan & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam, 2021. "Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach," Resources Policy, Elsevier, vol. 74(C).
    7. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).
    8. Fan, Ying & Mo, Jian-Lei & Zhu, Lei, 2013. "Evaluating coal bed methane investment in China based on a real options model," Resources Policy, Elsevier, vol. 38(1), pages 50-59.
    9. Evatt, Geoffrey William & Soltan, Mousa Omid & Johnson, Paul V., 2012. "Mineral reserves under price uncertainty," Resources Policy, Elsevier, vol. 37(3), pages 340-345.
    10. Yıldız, Taşkın Deniz, 2022. "Considering the recent increase in license fees in Turkey, how can the negative effect of the fees on the mining operating costs be reduced?," Resources Policy, Elsevier, vol. 77(C).
    11. Savolainen, Jyrki, 2016. "Real options in metal mining project valuation: Review of literature," Resources Policy, Elsevier, vol. 50(C), pages 49-65.
    12. Yıldız, Taşkın Deniz, 2022. "Supervisor fund expectation for the guarantee of salaries in the presence of the effect of permanent supervisor salaries on mining operating costs in Turkey," Resources Policy, Elsevier, vol. 77(C).
    13. Parviz Sohrabi & Hesam Dehghani & Behshad Jodeiri Shokri, 2022. "Determination of optimal production rate under price uncertainty—Sari Gunay gold mine, Iran," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(2), pages 187-201, June.
    14. Rahimi, Esmaeil & Ghasemzadeh, Hasan, 2015. "A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects," Resources Policy, Elsevier, vol. 46(P1), pages 51-63.
    15. Bag, Surajit & Yadav, Gunjan & Wood, Lincoln C. & Dhamija, Pavitra & Joshi, Sudhanshu, 2020. "Industry 4.0 and the circular economy: Resource melioration in logistics," Resources Policy, Elsevier, vol. 68(C).
    16. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    17. Yıldız, Taşkın Deniz, 2023. "Changes in the salaries of mining engineers as they obtain managerial and OHS specialist positions in Turkey: By what criteria can salaries be increased?," Resources Policy, Elsevier, vol. 84(C).
    18. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
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    More about this item

    Keywords

    Economic uncertainty; Metal price; Operating costs; Net present value; Binomial tree technique;
    All these keywords.

    JEL classification:

    • Q39 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Other

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