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The benefits of improving urban lakes in mega cities: a revealed and stated preference approach applied to the Hussain Sagar in Hyderabad, India

Published online by Cambridge University Press:  01 June 2017

Prajna Paramita Mishra*
Affiliation:
School of Economics, University of Hyderabad, Hyderabad – 500 046, India. E-mail: prajnasujit@gmail.com

Abstract

In this study, the author estimates the demand for improvements in the site quality of Hussain Sagar, a large lake in metropolitan Hyderabad, India. Using both revealed and stated preference approaches, it is estimated that the park provides recreational benefits of US$35 per person for on-site respondents and US$14 for off-site respondents per visit to the park. Given that over one million people visit the lake and its parks every year, based on different scenarios, the annual estimated amenity value of the lake ranges from INR1.76bn (US$29m) to INR3.48bn (US$58m). Thus it is recommended that park authorities double the access fee to the park from the current INR10 (US$0.16). With this increase, the government can potentially earn US$0.36–1.48m in revenues per year, which will make it possible to improve the quality of the lake and its surroundings.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

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References

Adamowicz, W., Fletcher, J.J., and Graham-Tomasi, T. (1989), ‘Functional form and the statistical properties of welfare measures’, American Journal of Agricultural Economics 71(2): 414420.Google Scholar
Adamowicz, W., Louviere, J., and Williams, M. (1994), ‘Combining revealed and stated preference methods for valuing environmental amenities’, Journal of Environmental Economics and Management 26(3): 271292.CrossRefGoogle Scholar
Alberini, A. and Longo, A. (2006), ‘Combining the travel cost and contingent behavior methods to value cultural heritage sites: evidence from Armenia’, Journal of Cultural Economics 30(4): 287304.Google Scholar
Bharali, A. and Mazumder, R. (2012), ‘Application of travel cost method to assess the pricing policy of public parks: the case of Kaziranga National Park’, Journal of Regional Development and Planning 1(1): 4150.Google Scholar
Bockstael, N.E., Hanemann, W.M., and Kling, C.L. (1987), ‘Estimating the value of water quality improvements in a recreational demand framework’, Water Resources Research 23(5): 951960.Google Scholar
Bockstael, N.E., McConnell, K.E., and Strand, I.E. (1989), ‘Measuring the benefits of improvement in water quality: the Chesapeake Bay’, Marine Resource Economics 6(1): 118.Google Scholar
Cameron, T.A. (1992), ‘Combining contingent valuation and travel cost data for the valuation of nonmarket goods’, Land Economics 68(3): 302317.Google Scholar
Chaudhry, P. and Tewari, V.P. (2008), ‘Tourism recreational value of Rock Garden Chandigarh’, e-Review of Tourism Research 6(2): 3644.Google Scholar
Cullinan, J., Hynes, S., and O'Donoghue, C. (2007), ‘Aggregating consumer surplus values in travel cost modelling using spatial microsimulation and GIS techniques’, Rural Economy Research Centre Working Paper Series No. 08-WP-RE-07, Carlow, Ireland.Google Scholar
De, U.K. and Devi, A. (2011), ‘Valuing recreational and conservational benefits of a natural tourist site: case of Cherrapunjee’, Journal of Quantitative Economics 9(2): 154172.Google Scholar
Englin, J. and Cameron, T.A. (1996), ‘Augmenting travel cost models with contingent behavior data, Poisson regression analyses with individual panel data’, Environmental and Resource Economics 7(2): 133147.CrossRefGoogle Scholar
Englin, J. and Shonkwiler, J.S. (1995), ‘Estimating social welfare using count data models: an application to long-run recreation demand under conditions of endogenous stratification and truncation’, Review of Economics and Statistics 77(1): 104112.Google Scholar
EPTRI (Environmental Protection Training and Research Institute) (1996), State of Environment for Hyderabad Urban Agglomeration, Hyderabad: EPTRI.Google Scholar
Gupta, V. and Mythili, G. (2010), ‘Valuation of urban environmental amenities: a case study’, International Journal of Ecological Economics and Statistics 19(F10): 2032.Google Scholar
Hanley, N., Bell, D., and Alvarez-Farizo, B. (2003), ‘Valuing the benefits of coastal water quality improvements using contingent and real behavior’, Environmental and Resource Economics 24(3): 273285.Google Scholar
Hellerstein, D.M. and Mendelsohn, R. (1993), ‘A theoretical foundation for count data models’, American Journal of Agricultural Economics 75(3): 604611.Google Scholar
Hussain, M.A. (1976), ‘Preliminary observations on pollution of Lake Hussain Sagar caused by industrial effluents’, Indian Journal of Environmental Health 18(3): 227232.Google Scholar
Kamath, G. (2008), ‘Bioremediation of lakes: myths and realities’, in M. Sengupta and R. Dalwani (eds), Proceedings of Taal2007: The 12th World Lake Conference, pp. 44–49.Google Scholar
Kaoru, Y. (1995), ‘Measuring marine recreation benefits of water-quality improvements by the nested random utility model’, Resource and Energy Economics 17(2): 119136.Google Scholar
Kido, A. and Seidl, A. (2008), ‘Optimizing protected area entry fees across stakeholders: the Monarch Butterfly Biosphere Reserve, Michoacan, Mexico’, Environment and Development Economics 13(2): 229243.Google Scholar
Kling, C.L. (1997), ‘The gains from combining travel cost and contingent valuation data to value nonmarket goods’, Land Economics 73(3): 428439.Google Scholar
Layman, R.C., Boyce, J.R., and Criddle, K.R. (1996), ‘Economic valuation of the Chinook salmon sports fishery of the Gulkana River, Alaska, under current and alternative management plans’, Land Economics 71(1): 113128.Google Scholar
Manoharan, T.R. (1996), ‘ Economics of protected areas: a case study of Periyar Tiger Reserve ’, PhD thesis, Forest Research Institute University, Dehradun, India.Google Scholar
Morgan, O.A. and Huth, W.L (2011), ‘Using revealed and stated preference data to estimate the scope and access benefits associated with cave diving’, Resource and Energy Economics 33(1): 107118.CrossRefGoogle Scholar
Needelman, M.S. and Kealy, M.J. (1995), ‘Recreational swimming benefits of New Hampshire lake water quality policies: an application of a repeated discrete choice model’, Agricultural and Resource Economics Review 24(1): 7887.Google Scholar
Parsons, G. and Kealy, M. (1992), ‘Randomly drawn opportunity sets in a random utility model of lake recreationLand Economics 68(1): 93106.Google Scholar
Pendleton, L. and Mendelsohn, R. (2000), ‘Estimating recreation preferences using hedonic travel cost and random utility models’, Resource and Environmental Economics 17(1): 89108.Google Scholar
Ramachandraiah, C. and Prasad, S. (2004), ‘Impact of urban growth on water bodies: the case of Hyderabad’, Working Paper No. 60, Centre for Economic and Social Studies, Hyderabad.Google Scholar
Rao, E.N. (2008a), ‘From poison ponds to pleasure spots: the restoration of Hyderabad Lakes’, Annals of New York Academy of Sciences 1140(1): 129134.Google Scholar
Rao, P.P. (2008b), ‘Impact and feedback study on BPPA gardens’, Project Report, Centre for Economic and Social Studies, Hyderabad.Google Scholar
Shaw, D. (1988), ‘On-site samples regression: problems of non-negative integers, truncation and endogenous stratification’, Journal of Econometrics 37(2): 211223.Google Scholar
Verma, M. and Negandhi, D. (2011), ‘Valuing ecosystem services of wetlands – a tool for effective policy formulation and poverty alleviation’, Hydrological Sciences Journal 56(8): 16221639.Google Scholar
Whitehead, J.C., Timothy, C.H., and Huang, J.C. (2000), ‘Measuring recreation benefits of quality improvements with revealed and stated behavior data’, Resource and Energy Economics 22(4): 339354.Google Scholar