Application of life cycle assessment in the mining industry

LCA METHODOLOGY

Abstract

Background, aim, and scope

In spite of the increasing application of life cycle assessment (LCA) for engineering evaluation of systems and products, the application of LCA in the mining industry is limited. For example, a search in the Engineering Compendex database using the keywords “life cycle assessment” results in 2,257 results, but only 19 are related to the mining industry. Also, mining companies are increasingly adopting ISO 14001 certified environmental management systems (EMSs). A key requirement of ISO certified EMSs is continual improvement, which can be better managed with life cycle thinking. This paper presents a review of the current application of LCA in the mining industry. It discusses the current application, the issues, and challenges and makes relevant recommendations for new research to improve the current situation.

Main features

The paper reviews the major published articles in the literature pertaining to LCA methodology as applied in the mining industry. The challenges associated with LCA applications in mining are discussed next. Finally, the authors present recommended research areas to increase the application of LCA in the mining industry.

Results

The literature review shows a limited number of published mining LCA studies. The paper also shows the variation in functional unit definition for mining LCA studies. The challenges and research needed to address the problems are highlighted in the discussions.

Discussion

The limited number of mining LCAs may be due to the lack of life cycle thinking in the industry. The paper, however, highlights the major contributions in the literature to LCA practice in the mining industry. This paper discusses the lack of LCA awareness and tools for mining LCAs, issues relating to functional unit and scoping of mining product systems, defining adequate and appropriate impact categories, and challenges with uncertainty and sensitivity analysis. The authors recommend that future research focus on the development of a mining-specific LCA framework, data uncertainty characterization, and software development to increase the application of LCA in mining.

Conclusions

LCA presents beneficial insights to the mining industry as it seeks to develop world-class EMSs and environmentally sustainable projects. However, to take full advantage of this technique, further research is necessary to improve the level of LCA application in mining. Major challenges have been identified, and recommended research areas have been proposed to improve the situation. The paper outlines the benefits of increased application of LCA in the mining industry to LCA databases and all practitioners.

Recommendations and perspectives

It is recommended that additional research be undertaken through industry–academia partnerships to develop a more rigorous mining-specific LCA framework. Such a framework should allow for sensitivity and uncertainty analysis while allowing for suitable data collection that still covers the temporal and spatial dimensions of mining. Research should also be carried out to develop objective ways of characterizing the uncertainty introduced in a LCA study due to the use of secondary data (emissions factors) from prior studies. Finally, new software or GUIs that address the peculiarities of mining should be developed to help mining professionals with basic LCA knowledge to undertake LCA studies of their systems and mines.

Keywords

LCA methodology Life cycle assessment Mining Sensitivity analysis Uncertainty analysis 

References

  1. Amatayakul W, Ramnas O (2001) Life cycle assessment of a catalytic converter for passenger cars. J Clean Prod 9:395–403CrossRefGoogle Scholar
  2. Aquilonius K, Hallberg HB, Bergstrom D, Lechon U, Cabal H, Saez RMS, Lepicard T, Ward S, Hamacher D, Korhonen T (2001) Sensitivity and uncertainty analyses in external cost assessments of fusion power. Fusion Eng Des 58-59:1021–1026CrossRefGoogle Scholar
  3. Awuah-Offei K, Checkel D, Askari-Nasab H (2008a) Evaluation of belt conveyor and truck haulage systems in an open pit mine using life cycle assessment. CIM Bulletin, Vol. 102, Paper 8, pp 1–6Google Scholar
  4. Awuah-Offei K, Checkel D, Askari-Nasab H (2008b) Environmental life cycle assessment of belt conveyor and truck haulage systems in an open pit mine. SME Annual Conference, 24–27 Feb 2008, Salt Lake City, UtahGoogle Scholar
  5. Basset-Mens C, Van der Werf HMG, Robin P, Morvan TH, Hassouna M, Paillat J-M, Vertès F (2007) Methods and data for the environmental inventory of contrasting pig production systems. J Clean Prod 15:1395–1405CrossRefGoogle Scholar
  6. Battisti R, Corrado A (2005) Environmental assessment of solar thermal collectors with integrated water storage. J Clean Prod 13:1295–1300CrossRefGoogle Scholar
  7. Benetto E, Dujet C, Rousseaux P (2006) Fuzzy-sets approach to noise impact assessment. Int J LCA 11(4):222–228CrossRefGoogle Scholar
  8. BHP Billiton (2006) BHP Billiton sustainability report. BHP Billiton, Australia, p 522Google Scholar
  9. Bovea M-D, Saura Ú, Ferrero JL, Giner J (2007) Cradle-to-gate study of red clay for use in the ceramic industry. Int J of LCA 12(6):439–447CrossRefGoogle Scholar
  10. Canals LM, Bauer C, Depestele J, Dubreuil A, Knuchel RF, Gaillard G, Michelsen O, Müller-Wenk R, Rydgren B (2007) Key elements in a framework for land use impact assessment within LCA. Int J of LCA 12(1):5–15CrossRefGoogle Scholar
  11. Center of Environmental Science (CML) (2001) Life cycle assessment—an operational guide to ISO standards, version 2.02. Center of Environmental Science, The NetherlandsGoogle Scholar
  12. Chapin FS III, Zavaleta ES, Eviner VT, Naylor RT, Vitousek PM, Reynolds HL, Hooper DU, Lavorel S, Sala OE, Hobbie SE, Mack MC, Diaz S (2000) Consequences of changing biodiversity. Nature 405:234–242CrossRefGoogle Scholar
  13. Chaya W, Gheewala SH (2007) Life cycle assessment of MSW-to-energy schemes in Thailand. J Clean Prod 15:1463–1468CrossRefGoogle Scholar
  14. Chevalier J-L, Le Téno J-F (1996) Life cycle analysis with ill-defined data and it's application to building products. Int J of LCA 1(2):90–96CrossRefGoogle Scholar
  15. COM (2002) Towards a thematic strategy for soil protection. COM 179. Commission of the European Communities, BelgiumGoogle Scholar
  16. Durucan S, Korre A, Munoz-Melendez G (2006) Mining life cycle modelling: a cradle-to-gate approach to environmental management in the minerals industry. J Clean Prod 14:1057–1070CrossRefGoogle Scholar
  17. EEA, UNEP (2000) Down to earth: soil degradation and sustainable development in Europe, vol 16, Environmental issue series. European Environment Agency, CopenhagenGoogle Scholar
  18. Energy Information Administration (2008) Electric power monthly—November 2009, Report No. DOE/EIA-0226 (2009/11), p 14Google Scholar
  19. Forbes P, von Blottnitz H, Gaylard P, Petrie JG (2000) Environmental assessment of base metal processing: nickel refining case study. J South Afr Inst Mining Metal 100:347–353Google Scholar
  20. Goedkoop M (1995) The ecoindicator '95: final report. PRé Consultants BV, The NetherlandsGoogle Scholar
  21. Goedkoop M, Spriensma R (2000) The ecoindicator '99: a damage oriented method for life cycle impact assessment: methodology report. PRé Consultants BV, The NetherlandsGoogle Scholar
  22. ISO TC 207 (2004) ISO 14001: 2004 environmental management systems—requirements with guidance for use. ISO, SwitzerlandGoogle Scholar
  23. ISO TC 207 (2006) ISO 14040: 2006 environmental management—life cycle assessment—principles and framework. ISO, SwitzerlandGoogle Scholar
  24. Jassbi J, Serra P, Ribeiro RA, Donati A (2006) Comparison of Mamdani and Sugeno fuzzy inference systems for a space fault detection application. Proceedings of the 2006 World Automation Congress (WAG 2006), HungaryGoogle Scholar
  25. Jassbi J, Alavi SH, Serra PJA, Ribeiro RA (2007) Transformation of a Mamdani FIS to first order Sugeno FIS. IEEE 2007 Imperial College, LondonGoogle Scholar
  26. Lloyd SM, Ries R (2007) Characterizing, propagating and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Indust Ecol 11(1):161–179CrossRefGoogle Scholar
  27. Lo S-C, Ma H-W, Lo S-L (2005) Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. Sci Total Environ 340:23–33CrossRefGoogle Scholar
  28. Mangena SJ, Brent AC (2006) Application of a life cycle impact assessment framework to evaluate and compare environmental performances with economic values of supplied coal products. J Clean Prod 14:1071–1084CrossRefGoogle Scholar
  29. Müller-Wenk R (1998) Land use—the main threat to species. How to include land use in LCA. IWÖ—Diskussionsbeitrag No. 64. Universität St. Gallen, SwitzerlandGoogle Scholar
  30. Pimentel D, Harvey C, Resusodarmo P, Sinclair K, Kurz D, Mcnair M, Crist S, Schpritz L, Fitton L, Saffouri R, Blair R (1995) Environmental and economic costs of soil erosion and conservation benefits. Science 267:1117–1123CrossRefGoogle Scholar
  31. PRé (2008) SIMAPRO 7.1. PRé Consultants B.V. Amersfoort, The NetherlandsGoogle Scholar
  32. Raynolds M, Fraser R, Checkel D (2000) The relative mass-energy-economic value (RMEE) method for system boundary selection—part I: a means to systematically and quantitatively select LCA boundaries. Int J LCA 5:96–104CrossRefGoogle Scholar
  33. Rio Tinto (2006) Rio Tinto minerals 2006 sustainable development report. Rio Tinto, Australia, p 24Google Scholar
  34. Ross S, Evans D, Weber M (2002) How LCA studies deal with uncertainty. Int J of LCA 7(1):47–52CrossRefGoogle Scholar
  35. Sala OE, Chapin FS III, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, LeRoy PN, Sykes MT, Walker BH, Walker M, Wall DH (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1774CrossRefGoogle Scholar
  36. Socolof ML, Jonathan G, Overly JG, Geibig JR (2005) Environmental life-cycle impacts of CRT and LCD desktop computer displays. J Clean Prod 13:1281–1294CrossRefGoogle Scholar
  37. Spitzley DV, Tolle DA (2004) Evaluating land-use impacts: selection of surface area metrics for life-cycle assessment of mining. J Indust Ecol 8(1–2):11–21Google Scholar
  38. Steen B (1999a) A systematic approach to environmental priority strategies in product development (EPS): version 2000—general system characteristics. CPM report 1999:4. Chalmers University of Technology, GöteborgGoogle Scholar
  39. Steen B (1999b) A systematic approach to environmental strategies in product development (EPS): version 2000—models and data of the default methods. CPM Report 1999:5. Chalmers University of Technology, GöteborgGoogle Scholar
  40. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science, USA, p 269Google Scholar
  41. Suppen N, Carranza M, Hueta M, Hernandez MA (2006) Environmental management and life cycle approaches in the Mexican mining industry. J Clean Prod 14:1101–1115CrossRefGoogle Scholar
  42. Tan RR, Culaba AB, Purvis MRI (2002) Application of possibility theory in the life-cycle inventory assessment of biofuels. Int J Energy Res 26(8):737–745CrossRefGoogle Scholar
  43. Udo de Haes HA (2005) Land-use impacts of mining in the life cycle initiative. In: Dubreuil A (ed) Life cycle assessment of metals: issues and research directions. SETAC, USA, pp 159–163Google Scholar
  44. US EPA (1995) Guidelines for assessing the quality of life-cycle inventory analysis. US EPA, USA, p 118Google Scholar
  45. US EPA (2003) Draft guidance on the development, evaluation and application of regulatory environmental models. US EPA, USA, p 60Google Scholar
  46. US EPA (2006) Life cycle assessment: principles and practice. US EPA, USA, p 88Google Scholar
  47. Van Zyl DJA (2005) Towards improved environmental indicators for mining using life-cycle thinking. In: Dubreuil A (ed) Life cycle assessment of metals: issues and research directions. SETAC, USA, pp 117–122Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Mining and Nuclear EngineeringMissouri University of Science and TechnologyRollaUSA
  2. 2.Department of Mathematics and StatisticsMissouri University of Science and TechnologyRollaUSA

Personalised recommendations