Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-10T10:13:13.978Z Has data issue: false hasContentIssue false
  • English
  • Français

Accounting for Nonmarket Impacts in a Benefit-Cost Analysis of Underground Coal Mining in New South Wales, Australia

Published online by Cambridge University Press:  19 January 2015

Rob Gillespie  and
Marit E. Kragt
Rob Gillespie
Affiliation:
Gillespie Economics
Marit E. Kragt
Affiliation:
University of Western Australia
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Strategic inquiries into coal mining by Australian Governments advocate increased use of comprehensive benefit cost analyses and nonmarket valuation studies when assessing individual project proposals. The study reported in this paper addresses these Government concerns, by integrating results of a choice experiment into a benefit cost analysis undertaken for a Colliery in the Southern Coalfield of New South Wales, Australia. Results of the study were used to aid the State government in evaluating proposals for continued underground coal mining. We show that impacts of mine subsidence on streams, swamps, and Aboriginal sites negatively affect community wellbeing. Social welfare increases with the length of time that the mine provides direct employment. We demonstrate how implicit price estimates from the choice experiment can be incorporated into a benefit cost analysis of continued mining. Benefit cost analyses were carried out for a range of policy scenarios—including policies that would restrict mining activities at the Colliery and protect environmental and cultural features in the Southern Coalfield. Notwithstanding the environmental impacts generated by mining operations, continued mining is shown to be a more economically efficient course of action.

Keywords

benefit cost analysiscoal miningchoice experimentsmining externalitiesnatural resource managementnonmarket valuation
Type
Article
Information
Journal of Benefit-Cost Analysis , Volume 3 , Issue 2 , 08 May 2012 , pp. 1 - 29
Copyright
Copyright © Society for Benefit-Cost Analysis 2012

References

ABS. 2006. Australian Bureau of Statistics – Census of population and housing; Available online at http://www.censusdata.abs.gov.au.Google Scholar
Alvarez-Farizo, B., Hanley, N., Barberan, R. and Lazaro, A. 2007. Choice modeling at the “market stall”: Individual versus collective interest in environmental valuation. Ecol. Econ. 60(4), 743–751.CrossRefGoogle Scholar
Axsen, J., Mountain, D.C. and Jaccard, M. 2009. Combining stated and revealed choice research to simulate the neighbour effect: the case of hybrid-electric vehicles. Resour. Energy Econ. 31, 221–238.CrossRefGoogle Scholar
Bateman, I., Brouwer, R., Ferrini, S. and Schaafsma, M. 2009. Guidelines for designing and implementing transferable nonmarket valuation studies: a multi-country study of open-access water quality improvements. Paper presented at the 17th Annual conference of the European Association of Environmental and Resource Economists, Amsterdam, The Netherlands, 24–27 June 2009.Google Scholar
Bennett, J. 2008. Defining and managing environmental flows: inputs from society. Econ. Papers 27, 167–183.CrossRefGoogle Scholar
Bennett, J., van Bueren, M. and Whitten, S. 2004. Estimating societies willingness to pay to maintain viable rural communities. Aust. J. Agric. Resour. Econ. 48, 487–512.CrossRefGoogle Scholar
Boxall, P.C., Englin, J. and Adamowicz, W.L. 2003. Valuing aboriginal artifacts: a combined revealed-stated preference approach. J. Environ. Econ. Manag. 45(2), 213–230.CrossRefGoogle Scholar
Burgess, D.F. and Zerbe, R.O. 2011. Appropriate discounting for benefit-cost analysis. J. Benefit Cost Anal. 2(2), Art 2.CrossRefGoogle Scholar
Cameron, A.C. and Trivedi, P.K. 2005. Microeconometrics: methods and applications. Cambridge University Press, New York.CrossRefGoogle Scholar
Colombo, S., Calatrava-Requena, J. and Hanley, N. 2007. Testing choice experiments for benefit transfer with preference heterogeneity. Am. J Agric. Econ. 89(1), 135–151.CrossRefGoogle Scholar
Czajkowski, M. and Scasný, M. 2010. Study on benefit transfer in an international setting. How to improve welfare estimates in the case of the countries’ income heterogeneity? Ecol. Econ. 69, 2409–2416.CrossRefGoogle Scholar
Damigos, D. 2006. An overview of environmental valuation methods for the mining industry. J. Clean. Prod. 14(3–4), 234–247.CrossRefGoogle Scholar
Econometric Software. 2007. NLOGIT 4.0. Econometric Software Inc., Castle Hill.Google Scholar
Gilbert & Associates Pty. Ltd. 2008. Metropolitan coal project, surface water assessment. Milton, Australia, August 2008.Google Scholar
Greene, W.H. and Hensher, D.A. 2007. Heteroscedastic control for random coefficients and error components in mixed logit. Transp. Res. E: Logist. Transp. Rev. 43, 610-623. Hanley, N. and Barbier, E.B. 2009. Pricing nature. Cost-benefit analysis and environmental policy. Edward Elgar, Cheltenham, UK.Google Scholar
Hanley, N., Adamowicz, W. and Wright, R.E. 2005. Price vector effects in choice experiments: an empirical test. Resour. Energy Econ. 27, 227–234.Google Scholar
Hanley, N., Schläpfer, F. and Spurgeon, J. 2003. Aggregating the benefits of environmental improvements: distance-decay functions for use and nonuse value. J. Environ. Manag. 68, 297–304.CrossRefGoogle Scholar
Hanley, N., Wright, R. and Adamowicz, V. 1998. Using choice experiments to value the environment. Environ. Resour. Econ. 11, 413–428.CrossRefGoogle Scholar
Haveman, R.H. and Farrow, S. 2011. Labor expenditures and benefit-cost accounting in times of unemployment. J. Benefit Cost Anal. 2(2), Art 7.CrossRefGoogle Scholar
HCPL. 2008. Metropolitan coal project environmental assessment. Peabody Energy Australia and Helensburgh Coal Pty Limited, Brisbane, Australia.Google Scholar
Hensher, D.A. and Greene, W.H. 2003. The mixed logit model: the state of practice. Transport 30, 133–176.CrossRefGoogle Scholar
Hensher, D.A., Rose, J.M. and Greene, W.H. 2005. Applied choice analysis: a primer. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Hole, A.R. 2008. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. J. Health Econ. 27, 1078–1094.CrossRefGoogle ScholarPubMed
Ivanova, G., Rolfe, J., Lockie, S. and Timmer, V. 2007. Assessing social and economic impacts associated with changes in the coal mining industry in the Bowen Basin, Queensland, Australia. Manag. Environ. Qual. 18, 211.CrossRefGoogle Scholar
Johnson, F. and Desvouges, W. 1997. Estimating stated preferences with rated-pair data: environmental, health and employment effects of energy programs. J. Environ. Econ. Manag. 34, 75–99.CrossRefGoogle Scholar
Kragt, M.E. and Bennett, J. 2012. Attribute framing in choice experiments: How do attribute level descriptions affect value estimates? Environ. Resour. Econ. 51(1), 43–59.CrossRefGoogle Scholar
Krinsky, I. and Robb, A.L. 1986. On approximating the statistical properties of elasticities. Rev. Econ. Stat. 68, 715–719.CrossRefGoogle Scholar
Lambert, D.K. and Shaw, W.D. 2000. Agricultural and recreational impacts from surface flow changes due to gold mining operations. Western J. Agric. Econ. 25(2), 533–546.Google Scholar
Lancaster, K.J. 1966. A new approach to consumer theory. J. Polit. Econ. 74, 132–157.CrossRefGoogle Scholar
Loomis, J. 2011. Incorporating distributional issues into benefit cost analysis: why, how, and two empirical examples using nonmarket valuation. J. Benefit Cost Anal. 2(1), Art 5.CrossRefGoogle Scholar
McFadden, D. and Train, K. 2000. Mixed MNL models for discrete response. J. Appl. Econ. 15, 447–470.3.0.CO;2-1>CrossRefGoogle Scholar
Morrison, M. 2000. Aggregation biases in stated preference studies. Aust. Econ. Pap. 39, 215–230.CrossRefGoogle Scholar
Morrison, M., Bennett, J. and Blamey, R. 1999.Valuing improved water quality using choice experiments. Water Resour. Res. 35(9), 2805–2814.CrossRefGoogle Scholar
Nielsen, J.S. 2011. Use of the Internet for willingness-to-pay surveys: a comparison of face-to-face and web-based interviews. Resour. Energy Econ. 33, 119–129.CrossRefGoogle Scholar
NSW Department of Planning. 2008. Impacts of underground coal mining on natural features in the southern coalfield, strategic review. July 2008, Report for the Minister of Planning, Sydney.Google Scholar
NSW Government. 2009a. Environmental Planning and Assessment Act 1979. No. 203 (NSW). As at 26 October 2009, Minister for Planning, Sydney.Google Scholar
NSW Government. 2009b. State Environmental Planning Policy (Major Development) 2005, New South Wales Government, Sydney.Google Scholar
NSW Treasury. 2007.Treasury guidelines for economic appraisal. Available online at www.treasury.nsw.gov.au.Google Scholar
Olsen, S. 2009. Choosing between Internet and mail survey modes for choice experiment surveys considering non-market goods. Environ. Resour. Econ. 44(4), 591–610.CrossRefGoogle Scholar
Othman, J., Bennett, J. and Blamey, R. 2004. Environmental values and resource management options: a choice experiments experience in Malaysia. Environ. Dev. Econ. 9, 803–824.CrossRefGoogle Scholar
Poe, G.L., Giraud, K.L. and Loomis, J.B. 2005.Computational methods for measuring the difference of empirical distributions. Am. J. Agric. Econ. 87, 353–365.CrossRefGoogle Scholar
Revelt, D. and Train, K. 1998. Mixed logit with repeated choices: households’ choices of appliance efficiency level. Rev. Econ. Stat. 80, 647–657.CrossRefGoogle Scholar
Rigby, D., Balcombe, K. and Burton, M. 2009. Mixed logit model performance and distributional assumptions: preferences and GM foods. Environ. Resour. Econ. 42(3), 279–295.CrossRefGoogle Scholar
Robinson, L.A. and Hammitt, J.K. 2011. Behavioral economics and the conduct of benefit-cost analysis: towards principles and standards. J. Benefit Cost Anal. 2(2), Art 5.CrossRefGoogle Scholar
Scarpa, R., Thiene, M. and Train, K. 2008. Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps. Am. J. Agric. Econ. 90, 994–1010.CrossRefGoogle Scholar
Scarpa, R., Willis, K.G. and Acutt, M. 2007. Valuing externalities from water supply: status quo, choice complexity and individual random effects in panel kernel logit analysis of choice experiments. J. Environ. Plann. Manag. 50, 449–466.CrossRefGoogle Scholar
Train, K.E. 2003. Discrete choice methods with simulation. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Trigg, A.B. and Dubourg, W.R. 1993. Valuing the environmental impacts of opencast coal mining: the case of the Trent Valley in North Staffordshire, CSERGE Working Paper GEC 93-19. Centre for Social and Economic Research on the Global Environment, University of East Anglia, UK.Google Scholar
van Bueren, M. and Bennett, J. 2000. Estimating community values for land and water degradation impacts. Final Report, Project 6.1.4. National Land and Water Resources Audit, Canberra.Google Scholar
Windle, J. and Rolfe, J. 2004. Assessing values for estuary protection with choice modelling using different payment mechanisms, valuing floodplain development in the Fitzroy Basin Research Report No. 10. Faculty of Business and Law, Central Queensland University, Emerald, QLD.Google Scholar
Windle, J. and Rolfe, J. 2011. Comparing responses from Internet and paper-based collection methods in more complex stated preference environmental valuation surveys. Econ. Anal. Policy 41(1), 83–97.CrossRefGoogle Scholar