Revealed Preference and Effectiveness of Public Investment in Ecological River Restoration Projects: An Application of the Count Data Model
Abstract
:1. Introduction
2. Model Selection
2.1. Theoretical Model
2.2. Poisson and Negative Binomial Model
2.3. Zero-Truncation and Endogenous Stratification
2.4. Estimates of Consumer Welfare
3. Study Area and Data
3.1. Study Area
3.2. Data
4. Empirical Results
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Postel, S.; Richter, B. Rivers for Life: Managing Water for People and Nature; Island Press: Washington, DC, USA, 2008. [Google Scholar]
- Young, R.A.; Loomis, J.B. Determining the Economic Value of Water: Concepts and Methods; Routledge: London, UK, 2014. [Google Scholar]
- Henry, C.P.; Amoros, C.; Roset, N. Restoration ecology of riverine wetlands: A 5-year post-operation survey on the Rhone River, France. Ecol. Eng. 2002, 18, 543–554. [Google Scholar] [CrossRef]
- Ormerod, S.J. Restoration in applied ecology: Editor’s introduction. J. Appl. Ecol. 2003, 40, 44–50. [Google Scholar] [CrossRef]
- Palmer, M.A.; Bernhardt, E.S.; Allan, J.D.; Lake, P.S.; Alexander, G.; Brooks, S.; Carr, J.; Clayton, S.; Dahm, C.N.; Shah, J.F.; et al. Standards for ecologically successful river restoration. J. Appl. Ecol. 2005, 42, 208–217. [Google Scholar] [CrossRef]
- Malakoff, D. Profile—Dave rosgen—The river doctor. Science 2004, 305, 937–939. [Google Scholar] [CrossRef] [PubMed]
- Beechie, T.; Pess, G.; Roni, P. Setting river restoration priorities: A review of approaches and a general protocol for identifying and prioritizing actions. N. Am. J. Fish. Manag. 2008, 28, 891–905. [Google Scholar] [CrossRef]
- Johansson, M.E.; Nilsson, C. Responses of riparian plants to flooding in free-flowing and regulated boreal rivers: An experimental study. J. Appl. Ecol. 2002, 39, 971–986. [Google Scholar] [CrossRef]
- Loomis, J.B.; Walsh, R.G. Recreation Economic Decisions: Comparing Benefits and Costs, 2nd ed.; Venture Publishing Inc.: State College, PA, USA, 1997. [Google Scholar]
- Loomis, J. Quantifying recreation use values from removing dams and restoring free-flowing rivers: A contingent behavior travel cost demand model for the lower Snake River. Water Resour. Res. 2002. [Google Scholar] [CrossRef]
- Timmins, C.; Murdock, J. A revealed preference approach to the measurement of congestion in travel cost models. J. Environ. Econ. Manag. 2007, 53, 230–249. [Google Scholar] [CrossRef]
- Amoako-Tuffour, J.; Martinez-Espineira, R. Leisure and the net opportunity cost of travel time in recreation demand analysis: An application to gros morne national park. J. Appl. Econ. 2012, 15, 25–49. [Google Scholar] [CrossRef]
- Rolfe, J.; Dyack, B. Valuing recreation in the coorong, australia, with travel cost and contingent behaviour models. Econ. Rec. 2011, 87, 282–293. [Google Scholar] [CrossRef]
- El-Bekkay, M.; Moukrim, A.I.; Benchakroun, F. An economic assessment of the Ramsar site of Massa (Morocco) with travel cost and contingent valuation methods. Afr. J. Environ. Sci. Technol. 2013, 7, 441–447. [Google Scholar]
- Bockstael, N.E.; McConnell, K.E. Environmental and Resource Valuation with Revealed Preferences: A Theoretical Guide to Empirical Models; Springer Netherlands: Dordrecht, The Netherlands, 2007. [Google Scholar]
- Melstrom, R.T.; Lupi, F.; Esselman, P.C.; Stevenson, R.J. Valuing recreational fishing quality at rivers and streams. Water Resour. Res. 2015, 51, 140–150. [Google Scholar] [CrossRef]
- Freeman, A.M. The Measurement of Environmental and Resource Values: Theory and Methods; Resources for the Future: Washington, DC, USA, 1993. [Google Scholar]
- Kolstad, C.D. Environmental Economics; Oxford University Press: New York, NY, USA, 2000. [Google Scholar]
- Braden, J.B.; Kolstad, C.D. Measuring the Demand for Environmental Quality; Elsevier Science Pub. Co.: North-Holland, The Netherlands, 1991. [Google Scholar]
- Greene, W.H. Econometric Analysis, 6th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2008. [Google Scholar]
- Cameron, A.C.; Trivedi, P.K. Econometric models based on count data. Comparisons and applications of some estimators and tests. J. Appl. Econ. 1986, 1, 29–53. [Google Scholar] [CrossRef]
- Haab, T.C.; McConnell, K.E. Valuing Environmental and Natural Resources: The Econometrics of Non-Market Valuation; Edward Elgar Publishing: Cheltenham, UK, 2002. [Google Scholar]
- Shaw, D. On-site samples regression: Problems of non-negative integers, truncation, and endogenous stratification. J. Econ. 1988, 37, 211–223. [Google Scholar] [CrossRef]
- Englin, J.; Shonkwiler, J. Modeling Recreation Demand in the Presence of Unobservable Travel Cost: Toward a Travel Price Model. J. Environ. Econ. Manag. 1995, 29, 368–377. [Google Scholar] [CrossRef]
- Hellerstein, D.; Mendelsohn, R. A theoretical foundation for count data models. Am. J. Agric. Econ. 1993, 75, 604–611. [Google Scholar] [CrossRef] [Green Version]
- Seoul Development Institute (SDI). Feasibility Study and Master Plan of Cheonggyecheon Restoration: Mid-term Report; Seoul Development Institute: Seoul, Korea, 2003. [Google Scholar]
- Hwang, K.Y. Restoring Cheonggyecheon Stream in the Downtown Seoul; Seoul Development Institute: Seoul, Korea, 2004. [Google Scholar]
- Cho, M.R. The politics of urban nature restoration the case of cheonggyecheon restoration in Seoul, Korea. Int. Dev. Plan. Rev. 2010, 32, 145–165. [Google Scholar] [CrossRef]
- Lee, J.Y.; Anderson, C.D. The restored cheonggyecheon and the quality of life in Seoul. J. Urban Technol. 2013, 20, 3–22. [Google Scholar] [CrossRef]
- Chung, E.-S.; Lee, K.S. Identification of spatial ranking of hydrological vulnerability using multi-criteria decision making techniques: Case study of Korea. Water Resour. Manag. 2009, 23, 2395–2416. [Google Scholar] [CrossRef]
- Kim, Y.; Chung, E.-S.; Jun, S.-M.; Kim, S.U. Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy topsis. Resour. Conserv. Recycl. 2013, 73, 23–32. [Google Scholar] [CrossRef]
- Kong, K.; Park, D.H.; Yoo, J.C. Estimating of social preference of the watershed resident about the anyangcheon watershed water quality improvement. J. Korea Water Resour. Assoc. 2008, 41, 315–324. [Google Scholar] [CrossRef]
- Ismail, N.; Jemain, A.A. Handling overdispersion with negative binomial and generalized Poisson regression models. In Casualty Actuarial Society Forum. Winter 2007. Including the Ratemaking Call Papers; Casualty Actuarial Society: Arlington, VA, USA, 2007; pp. 103–158. [Google Scholar]
- Sileshi, G. Selecting the right statistical model for analysis of insect count data by using information theoretic measures. Bull. Entomol. Res. 2006, 96, 479–488. [Google Scholar]
- Lee, Y.; Chang, H.; Hong, Y. Is a costly river restoration project beneficial to the public? Empirical evidence from the Republic of Korea. Desalination Water Treat. 2015, 54, 3696–3703. [Google Scholar] [CrossRef]
Visit | Mean | Std. Err. | 95% Conf. Interval | |
---|---|---|---|---|
Anyangcheon | 2.271 | 0.1197 | 2.035 | 2.506 |
Cheonggyecheon | 3.507 | 0.2781 | 2.959 | 4.054 |
Variables | Description | Anyangcheon Means | Cheonggyecheon Means |
---|---|---|---|
TC | Total round-trip travel cost associated with travel time opportunity cost (in US dollars) | 16.72 | 15.08 |
(0.54) | (1.28) | ||
Gender | 1 if survey respondent is male, 0 otherwise | 0.493 | 0.479 |
(0.030) | (0.030) | ||
Job | 1 if survey respondent is an office worker, 0 otherwise | 0.264 | 0.250 |
(0.026) | (0.026) | ||
Education | Years of schooling | 13.81 | 14.43 |
(0.166) | (0.146) | ||
Dwelling | 1 if survey respondent lives in an apartment, 0 otherwise | 0.750 | 0.559 |
(0.026) | (0.029) | ||
Age | Survey respondent’s age | 45.81 | 40.57 |
(0.878) | (0.895) | ||
Income | Monthly household income (in US dollars) | 3657.62 | 3972.85 |
(55.69) | (89.55) |
Variables | Anyangcheon | Cheonggyecheon | |||||||
---|---|---|---|---|---|---|---|---|---|
Poisson | NB | ZTNB | TSNB | Poisson | NB | ZTNB | TSNB | ||
Intercept | 0.7034 * | 0.6986 * | 0.1800 | −0.9354 | 1.287 * | 1.249 * | 0.9649 | −14.10 | |
(0.4059) | (0.4513) | (0.7454) | −0.9354 | (0.2612) | (0.4005) | (0.6070) | (553.63) | ||
TC | −0.0181 * | −0.0180 * | −0.0339 * | −0.0358 * | −0.0306 * | −0.0200 * | −0.0523 * | −0.0532 * | |
(4.43 × 10−6) | (4.82 × 10−6) | (8.19 × 10−6) | (8.27 × 10−6) | (3.15 × 10−6) | (3.60 × 10−6) | (7.76 × 10−6) | (7.46 × 10−6) | ||
Gender | 0.0672 | 0.0617 | 0.0984 | 0.1040 | −0.0193 | −0.0154 | −0.0655 | −0.0659 | |
(0.0859) | (0.0953) | (0.1549) | (0.1575) | (0.0626) | (0.1026) | (0.1626) | (0.1505) | ||
Job | 0.1520 | 0.1505 | 0.2290 | 0.2432 | 0.0842 | 0.0926 | 0.0677 | 0.0581 | |
(0.1001) | (0.1112) | (0.1809) | (0.1843) | (0.0848) | (0.1314) | (0.2090) | (0.1937) | ||
Education | 0.0091 | 0.0101 | 0.0232 | 0.0247 | −0.0196 | −0.0214 | −0.0202 | −0.0196 | |
(0.0188) | (0.0208) | (0.0337) | (0.0342) | (0.0139) | (0.0220) | (0.0330) | (0.0305) | ||
Dwelling | 0.0639 | 0.0602 | 0.0969 | 0.1012 | −0.2465 * | −0.2101 * | −0.2599 | −0.2596 | |
(0.0957) | (0.1057) | (0.1707) | (0.1735) | (0.0644) | (0.1065) | (0.1680) | (0.1555) | ||
Age | −0.0041 | −0.0039 | −0.0064 | −0.0068 | 0.0163 * | 0.0157 * | 0.0183 * | 0.0178 * | |
(0.0032) | (0.0035) | (0.0058) | (0.0059) | (0.0024) | (0.0037) | (0.0056) | (0.0052) | ||
Income | 0.00009 * | 0.0180 * | 1.45 × 10−4 * | 1.53 × 10−4 * | 1.76 × 10−5 | −1.19 × 10−5 | −1.14 × 10−5 | −1.07 × 10−5 | |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | ||
# Obs. | 288 | 300 | |||||||
LR chi2 | 30.56 | 19.99 | 28.15 | 29.72 | 325.83 | 94.47 | 93.95 | 104.28 | |
Prob. > chi2 | 0.0001 | 0.0028 | 0.0002 | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
LL | −508.91 | −500.89 | −434.29 | −432.11 | −822.62 | −660.20 | −556.73 | −557.33 | |
Theta (θ) | 0.0965 | 0.5236 | 1.487 | 0.4350 | 1.314 | 3.875 | |||
(0.0316) | (0.1657) | (0.7152) | (0.0527) | (0.3518) | (0.0000) |
Site | Model | Annual CS | Value (US$) | p > | 95% Conf. Interval | |
---|---|---|---|---|---|---|
Anyangcheon | NB | /person | 113.86 | 0.000 | 54.25 | 173.48 |
/person/visit | 50.14 | 0.000 | 23.89 | 76.39 | ||
Total CS | 100,280,000 | |||||
ZTNB | /person | 60.57 | 0.000 | 31.93 | 89.22 | |
/person/visit | 26.67 | 0.000 | 14.06 | 39.29 | ||
Total CS | 53,340,000 | |||||
TSNB | /person | 57.40 | 0.000 | 31.40 | 83.39 | |
/person/visit | 25.27 | 0.000 | 13.82 | 36.72 | ||
Total CS | 50,540,000 | |||||
Cheonggyecheon | NB | /person | 158.94 | 0.000 | 102.73 | 215.15 |
/person/visit | 45.32 | 0.000 | 29.29 | 61.35 | ||
Total CS | 453,200,000 | |||||
ZTNB | /person | 60.71 | 0.000 | 43.04 | 78.39 | |
/person/visit | 17.31 | 0.000 | 12.27 | 22.35 | ||
Total CS | 173,100,000 | |||||
TSNB | /person | 59.65 | 0.000 | 43.26 | 76.03 | |
/person/visit | 17.01 | 0.000 | 12.33 | 21.68 | ||
Total CS | 170,100,000 |
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Lee, Y.; Kim, H.; Hong, Y. Revealed Preference and Effectiveness of Public Investment in Ecological River Restoration Projects: An Application of the Count Data Model. Sustainability 2016, 8, 353. https://doi.org/10.3390/su8040353
Lee Y, Kim H, Hong Y. Revealed Preference and Effectiveness of Public Investment in Ecological River Restoration Projects: An Application of the Count Data Model. Sustainability. 2016; 8(4):353. https://doi.org/10.3390/su8040353
Chicago/Turabian StyleLee, Yoon, Hwansuk Kim, and Yongsuk Hong. 2016. "Revealed Preference and Effectiveness of Public Investment in Ecological River Restoration Projects: An Application of the Count Data Model" Sustainability 8, no. 4: 353. https://doi.org/10.3390/su8040353