Estimation of the value of dam flushing by using Bayesian analysis - the case of Chungju dam

Joo-Suk Lee; Han-Joo Choi; Seung-Hoon Yoo

doi:10.3741/JKWRA.2017.50.7.467

All Issue

2017 Vol.50, Issue 7 Preview Page Next Page

Estimation of the value of dam flushing by using Bayesian analysis - the case of Chungju dam 베이지안 추정법을 활용한 댐 추가방류수의 경제적 가치 추정 - 충주댐 사례

31 July 2017. pp. 467-473

PDF XML

Abstract

Recently as algae phenomenon has been intensified, the need for additional dam flushing has been raised. To establish the more rational policies concerning the dam flushing, it is necessary to evaluate the dam flushing. This paper attempts to examine households’ willingness to pay (WTP) for dam flushing by using a contingent valuation (CV). Especially, unlike other CV studies which used maximum likelihood estimation (MLE), this study employed Bayesian approach. This study surveyed a randomly selected sample of 1,000 households nation-widely, and asked respondents questions in person-to-person interviews about how they would be willing to pay for the additional dam flushing. Respondents overall accepted the contingent market and were willing to contribute a significant amount (1,909.4 won), on average, per household per year. The aggregate value amounts to approximately 35.7 billion won per year.

Keywords

Dam flushing

Contingent valuation

Bayesian approach

Willingness to pay

최근 녹조현상이 심화되면서 이를 해결하기 위한 방안으로 댐의 추가방류에 대한 필요성이 제기되고 있다. 따라서 보다 합리적인 댐 추가방류와 관련된 정책 수립을 위해서 댐의 추가방류수에 대한 경제적 가치 측정의 필요성이 제기되고 있다. 이에 본 논문에서는 녹조 저감과 수질개선을 위한 댐의 추가방류에 대한 경제적 가치를 도출하기 위하여 일반가구의 지불의사액을 분석하고자 한다. 한편 연구방법론적인 측면에서 조건부 가치측정법(CVM)을 적용하되, 최우추정법(Maximum Likelihood Estimation)을 이용하는 기존의 CVM 연구와 달리 베이지안(Bayesian) 추정법을 적용하였다. 본 연구는 전국의 1,000가구를 대상으로 충주댐의 추가방류를 현재보다 200% 증가시켰을 경우를 가정하여 댐 추가방류의 사회적 편익 즉, 조류저감 효과를 산정하였다. 분석결과 가구당 연간 WTP는 1,909.3원으로 이를 연간 총편익으로 계산하면 357.1억 원, 10.56원/m³이다.

키워드

댐 추가방류

조건부 가치측정법

지불의사액

베이지안 추정법

References

Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., and Schuman, H. (1993). “Report of the NOAA panel on contingent valuation.” Federal Register, Vol. 58, pp. 4601-4614.

Atkins, J. P., Burdon, D., and Allen, J. H. (2007). “An application of contingent valuation and decision tree analysis to water quality improvements.” Marine Pollution Bulletin, Vol. 55, pp. 591-602.

Brox, J. A., Kumer, R. C., and Stollery, K. R. (2003). “Estimating willingness to pay for improved water quality in the presence of non-response bias.” American Journal of Agricultural Economics, Vol. 85, No. 2, pp. 414-428.

Gelfand, A. E., Hills, S. E., Racine-Poon, A., and Smith, A. F. (1990). “Illustration of Bayesian inference in normal data models using Gibbs sampling.” Journal of the American Statistical Association, Vol. 85, No. 412, pp. 972-985.

Genius, M., Hatzaki, E., Kouromichelaki, E. M., Kouvakis, G., and Nikiforaki, S. (2008). “Evaluating consumers’ willingness to pay for improved potable water quality and quantity.” Water Resources Management, Vol. 22, No. 12, pp. 1825-834.

Geweke, J. (1991). “Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints and the evaluation of constraint probabilities.” In Computing science and statistics: Proceedings of the 23rd symposium on the interface, pp. 571-578.

Hanemann, W. M. (1984). “Welfare evaluations in contingent valuation experiments with discrete responses.” American Journal of Agricultural Economics, Vol. 66, pp. 332-341.

Hanemann, W. M., and Kanninen, B. J. (1999). “The statistical analysis of discrete-response CV data”, in Bateman, I. J., and Willis, K. E. (Eds.), Valuing Environmental Preferences: Theory and Practice of the Contingent Valuation Method in the US, EU, and Developing Countries. Oxford, Oxford University Press.

Korea Development Institute (2012). Guideline of Contingent Valuation Method for Preliminary Feasibility Studies, Public and Private Infrastructure Investment Management Center.

Krinsky, I., and Robb, A. L. (1986). “On approximating the statistical properties of elasticities.” Review of Economics and Statistics, Vol. 68, pp. 715-719.

Kriström, B. (1997). “Spike models in contingent valuation.” American Journal of Agricultural Economics, Vol. 79, pp. 1013-1023.

Lee, J. S., Yoo, S. H., and Kwak, S. J. (2007). “Measuring the economic benefits of water quality improvement of Nakdong- river -using one one-half bound dichotomous choice model.” Economic Review, Vol. 25, No. 2, pp. 111-129 (in Korean).

Lee, S. B. (2002). “An empirical study on the environmental facilities management utilizing the water quality improvement value.” The Korea Spatial Planning Review, Vol. 34. pp. 45-60 (in Korean).

Raje, D. V., Dhobe, P. S., and Deshpande, A. W. (2002). Consumer’s willingness to pay for municipal supplied water: a case study.” Ecological Economics, Vol. 42, pp. 391-400.

Shin, Y. C. (1997). “Measuring the benefits of water quality improve-ment in Han River using CV data from a DCF Questionaire.” Environmental and Resource Economics Review, Vol. 6, No. 1, pp. 33-39 (in Korean).

Tanner, M. A., and Wong, W. H. (1987). “The calculation of posterior distributions by data augmentation.” Journal of the American Statistical Association, Vol. 82, No. 398, pp. 528-540.

Yoo, S. H. (2002). “Using bayesian estimation technique to analyze a dichotomous choice contingent valuation data.” Environmental and Resource Economics Review, Vol. 11, No. 1, Vol. 99-120 (in Korean).

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 50
No :7
Pages :467-473
Received Date : 2017-03-02
Revised Date : 2017-04-28
Accepted Date : 2017-05-24
DOI :https://doi.org/10.3741/JKWRA.2017.50.7.467

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

All Issue