Risk evaluation method based on set pair analysis applied to overseas mining investment
Z. Ma 1
,
 
G. Li 1
,
 
N. Hu 1
,
 
D. Liu 1
 
 
 
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University of Science & Technology Beijing
 
 
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2018;34(4):145-164
 
KEYWORDS
ABSTRACT
Overseas mining investment generally faces considerable risk due to a variety of complex risk factors. Therefore, indexes are often based on conditions of uncertainty and cannot be fully quantified. Guided by set pair analysis (SPA) theory, this study constructs a risk evaluation index system based on an analysis of the risk factors of overseas mining investment and determines the weights of factors using entropy weighting methods. In addition, this study constructs an identity-discrepancy-contrary risk assessment model based on the 5-element connection number. Both the certainty and uncertainty of the various risks are treated uniformly in this model and it is possible to mathematically describe and quantitatively express complex system decisions to evaluate projects. Overseas mining investment risk and its changing trends are synthetically evaluated by calculating the adjacent connection number and analyzing the set pair potential. Using an actual overseas mining investment project as an example, the risk of overseas mining investment can be separated into five categories according to the risk field, and then the evaluation model is quantified and specific risk assessment results are obtained. Compared to the field investigation, the practicability and effectiveness of the evaluation method are illustrated. This new model combines static and dynamic factors and qualitative and quantitative information, which improves the reliability and accuracy of risk evaluation. Furthermore, this evaluation method can also be applied to other similar evaluations and has a certain scalability.
METADATA IN OTHER LANGUAGES:
Polish
Metoda oceny ryzyka oparta na analizie par zbiorów w stosowaniu w zagranicznych inwestycjach wydobywczych
analiza zestawów par, ocena ryzyka, powiązanie 5 elementów, sąsiedni element połączenia
Zagraniczne inwestycje wydobywcze są narażone na znaczne ryzyko z powodu różnych czynników mających wpływ na taką działalność. Stosowane wskaźniki często zawierają elementy niepewności i nie można ich w pełni skwantyfikować. Kierując się teorią analizy par (set par analysis), badanie to tworzy system indeksu oceny ryzyka oparty na analizie czynników ryzyka zagranicznych inwestycji górniczych i określa wagi czynników z zastosowaniem entropii. Ponadto w artykule przedstawiono model oceny ryzyka związanego z identyfikacją rozbieżności, oparty na powiązaniu pięciu elementów. Zarówno pewność, jak i niepewność różnych ryzyk są traktowane jednolicie w tym modelu i możliwe jest matematyczne opisanie i ilościowe wyrażenie złożonych decyzji systemowych w celu oceny projektów. Ryzyko inwestycji zagranicznych i ich zmieniające się trendy są oceniane syntetycznie poprzez obliczanie sąsiedniego elementu i analizowanie ustalonego potencjału dla tej pary. Przykładem może być faktyczny zagraniczny projekt inwestycyjny dotyczący górnictwa, gdzie ryzyko inwestycji zagranicznych można podzielić na pięć rodzajów zgodnie z rachunkiem ryzyka, a następnie dokonuje się oceny modelu i uzyskuje się konkretne wyniki oceny ryzyka. Na przykładzie przedstawiono aspekty praktyczne i skuteczność tej metody oceny. Ten nowy model łączy czynniki statyczne i dynamiczne oraz informacje jakościowe i ilościowe, co poprawia wiarygodność i dokładność oceny ryzyka. Co więcej, ta metoda oceny może być również zastosowana do innych podobnych zagadnień i ma pewną skalowalność.
 
REFERENCES (43)
1.
Achzet, B. and Helbig, C. 2013. How to evaluate raw material supply risks-an overview. Resources Policy 38, pp. 435–447.
 
2.
Amiri et al. 2014 – Amiri, V., Rezaei, M. and Sohrabi, N. 2014. Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environmental Earth Sciences 72, pp. 3479–3490.
 
3.
Armstrong et al. 2016 – Armstrong, M., D’arrigo, R., Petter, C. and Galli, A. 2016. How resource-poor countries in Asia are securing stable long-term reserves: Comparing Japan’s and South Korea’s approaches. Resources Policy 47, pp. 51–60.
 
4.
Basiri, M.H. and Azad, A. 2015. Risk Analysis in Mining Projects by using Fuzzy Synthetic Evaluation Technique.
 
5.
Boloori, F. 2016. A slack based network DEA model for generalized structures: An axiomatic approach. Computers & Industrial Engineering 95, pp. 83–96.
 
6.
Chong et al. 2017 – Chong, T., Yi, S. and Che, H. 2017. Application of set pair analysis method on occupational hazard of coal mining. Safety Science 92, pp. 10–16.
 
7.
Dong et al. 2017 – Dong, L., Shu, W., Li, X., Zhou, Z., Gong, F. and Liu, X. 2017. Quantitative Evaluation and Case Study of Risk Degree for Underground Goafs with Multiple Indexes considering Uncertain Factors in Mines. Geofluids.
 
8.
Eboli et al. 2016 – Eboli, L., Fu, Y. and Mazzulla, G. 2016. Multilevel Comprehensive Evaluation of the Railway Service Quality. Procedia Engineering 137, pp. 21–30.
 
9.
Feng et al. 2014 – Feng, L.H., Sang, G.S. and Hong, W.H. 2014. Statistical Prediction of Changes in Water Resources Trends Based on Set Pair Analysis. Water Resources Management 28, pp. 1703–1711.
 
10.
Guo et al. 2014 – Guo, E., Zhang, J., Ren, X., Zhang, Q. and Sun, Z. 2014. Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China. Natural Hazards 74, pp. 947–965.
 
11.
Hou et al. 2017 – Hou, Z., Lu, W., Xue, H. and Lin, J. 2017. A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization. Journal of Contaminant Hydrology 203.
 
12.
Hu, J. and Yang, L. 2011. Dynamic stochastic multi-criteria decision making method based on cumulative prospect theory and set pair analysis. Systems Engineering Procedia 1, pp. 432–439.
 
13.
Iloiu et al. 2013 – Iloiu, M., Csiminga, D.C. and Iloiu, S.R. 2013. Relevant aspects in risk assessment for mining investment projects. SGEM Geoconference on Science and Technologies in Geology, Exploration and Mining, Sgem 2013, Vol. I.
 
14.
Jinghua et al. 2018 – Jinghua, R., Yong, C., Xiao, X., Gan, Y., Ranran, H. and Zuohua, M. 2018. Fuzzy comprehensive evaluation of ecological risk based on cloud model: taking Chengchao iron mine as example. IOP Conference Series: Earth and Environmental Science 111, 012005 (6 pp.)–012005 (6 pp.).
 
15.
Kan et al. 2012 – Kan, Y., Jiang, W. and Ji, X. 2012. Intrusion Detection Model Based on Set Pair Analysis Theory [In:] Zhang, Y. ed. Future Communication, Computing, Control and Management: Volume 1. Berlin, Heidelberg: Springer Berlin Heidelberg.
 
16.
Ke et al. 2012 – Ke, L., Shen, X., Tan, Z. and Guo, W. 2012. Grey Clustering Analysis Method for Overseas Energy Project Investment Risk Decision. Systems Engineering Procedia 3, pp. 55–62.
 
17.
Kusi-Sarpong et al. 2015 – Kusi-Sarpong, S., Bai, C., Sarkis, J. and Wang, X. 2015. Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology. Resources Policy 46, pp. 86–100.
 
18.
Li et al. 2016a – Li, C., Lian, S., Jia, J., Cai, Y. and Xuan, W. 2016a. Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan, China. Science of the Total Environment, pp. 307–316.
 
19.
Li et al. 2016b – Li, S.C., Zhou, Z.Q., Li, L.P., Lin, P., Xu, Z.H. and Shi, S.S. 2016b. A new quantitative method for risk assessment of geological disasters in underground engineering: Attribute Interval Evaluation Theory (AIET). Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research 53, pp. 128–139.
 
20.
Li et al. 2017 – Li, H., Dong, K., Jiang, H., Sun, R., Guo, X. and Fan, Y. 2017. Risk Assessment of China’s Overseas Oil Refining Investment Using a Fuzzy-Grey Comprehensive Evaluation Method. Sustainability 9, pp. 696–713.
 
21.
Memon et al. 2015 – Memon, M.S., Lee, Y.H. and Mari, S.I. 2015. Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory. Expert Systems with Applications 42, pp. 7951–7959.
 
22.
Ministry of commerce of the people’s Republic of China, national bureau of statistics of the people’s Republic of China & state administration of foreign exchange 2017. 2016 Statistical Bulletin of China’s Outward Foreign Direct Investment, China Statistics Press, Beijing.
 
23.
Pan et al. 2017 – Pan, Z., Wang, Y., Jin, J. and Liu, X. 2017. Set pair analysis method for coordination evaluation in water resources utilizing conflict. Physics & Chemistry of the Earth Parts A/b/c, 101.
 
24.
Pera, K. 2008. Application of VaR concept in risk assessment of a mineral investment project. Gospodarka Surowcami Mineralnymi – Mineral Resources Management 24, pp. 273–289.
 
25.
Ren et al. 2013 – Ren, B., Sun, Y., Zhou, Z., Cheng, Z. and Hu, X. 2013. Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis. Transactions of Tianjin University 19, pp. 25–28.
 
26.
Shahabinejad, H. and Sohrabpour, M. 2017. A novel neutron energy spectrum unfolding code using particle swarm optimization. Radiation Physics & Chemistry 136, pp. 9–16.
 
27.
Shannon, C.E.A. 2001. A mathematical theory of communication. AT&T Tech J. Acm Sigmobile Mobile Computing & Communications Review 5, pp. 3–55.
 
28.
Shemshadi et al. 2011 – Shemshadi, A., Shirazi, H., Toreihi, M. and Tarokh, M.J. 2011. A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications 38, pp. 12160–12167.
 
29.
Sobczyk et al. 2017 – Sobczyk, E.J., Kicki, J., Sobczyk, W. and Szuwarzyński, M. 2017. Support of mining investment choice decisions with the use of multi-criteria method. Resources Policy 51, pp. 94–99.
 
30.
Tang-Lee, D. 2016. Corporate social responsibility (CSR) and public engagement for a Chinese state-backed mining project in Myanmar – Challenges and prospects. Resources Policy 47, pp. 28–37.
 
31.
Tang et al. 2017 – Tang, B.J., Zhou, H.L., Chen, H., Wang, K. and Cao, H. 2017. Investment opportunity in China’s overseas oil project: An empirical analysis based on real option approach. Energy Policy 105, pp. 17–26.
 
32.
Tao et al. 2014 – Tao, J., Fu, M., Sun, J., Zheng, X., Zhang, J. and Zhang, D. 2014. Multifunctional assessment and zoning of crop production system based on set pair analysis-A comparative study of 31 provincial regions in mainland China. Communications in Nonlinear Science & Numerical Simulation 19, pp. 1400–1416.
 
33.
Wang et al. 2014 – Wang, M.W., Xu, P., Li, J. and Zhao K.Y. 2014. A novel set pair analysis method based on variable weights for liquefaction evaluation. Natural Hazards 70, pp. 1527–1534.
 
34.
Wang, Y. and Yang, J. 2010. Risk Assessment for Overseas Mining Investment Based on Fuzzy Neural Network.
 
35.
Wang et al. 2013 – Wang, Z., Zheng, J. and Li, H. 2013. The Risk Evaluation Model of Mining Project Investment Based on Fuzzy Comprehensive Method [In:] Tang, X., Zhong, W., Zhuang, D., Li, C. and Liu, Y. eds. Progress in Environmental Protection and Processing of Resource, Pts 1–4.
 
36.
Wang et al. 2015 – Wang, T., Chen, J.S., Wang, T. and Wang, S. 2015. Entropy weight-set pair analysis based on tracer techniques for dam leakage investigation. Natural Hazards 76, pp. 747–767.
 
37.
Wei et al. 2016 – Wei, C., Dai, X., Ye, S., Guo, Z. and Wu, J. 2016. Prediction analysis model of integrated carrying capacity using set pair analysis. Ocean & Coastal Management 120, pp. 39–48.
 
38.
Yue et al. 2014 – Yue, W., Cai, Y., Rong, Q., Li, C. and Ren, L. 2014. A hybrid life-cycle and fuzzy-set-pair analyses approach for comprehensively evaluating impacts of industrial wastewater under uncertainty. Journal of Cleaner Production 80, pp. 57–68.
 
39.
Zhang et al. 2016 – Zhang, S., Wang, B., Li, X. and Chen, H. 2016. Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine. Procedia Cirp 56, pp. 471–475.
 
40.
Zhao, K. 1989. Set pair and set pair analysis-a new concept and systematic analysis method. Proceedings of the national conference on system theory and regional planning 5.
 
41.
Zhao, K. 2000. Set pair analysis and its preliminary applications, Science and Technology Press of Zhejiang, Hangzhou.
 
42.
Zhao, K. and Zhao, S. 2014. Marvelous Connection Number, Intellectual property press, Beijing.
 
43.
Zou et al. 2016 – Zou, Q., Zhou, J., Zhou, C., Song, L. and Guo, J. 2013. Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stochastic Environmental Research & Risk Assessment 27, pp. 525–546.
 
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