title: Spatial disparities of the willingness of the residents to pay for the wetland restoration of Taihu Lake and its integration into decision making: a case study on Wuxi, China.
abstract: Many wetland restoration projects (WRPs) have been conducted to restore the wetland ecosystem since a massive bloom of cyanobacteria in Taihu Lake in the summer of 2007. The willingness to pay (WTP) of residents for wetland restoration was expected to exhibit spatial disparities that were used for decision making. In this study, correlation analysis, binary logistic regression, and linear regression were used to assess 400 questionnaires collected through a contingent valuation survey. Results indicated that WTP amounts and the probability of positive WTP decrease as the distance from the wetlands increases. Knowledge of WRPs was the only factor that influences whether the respondents pay in the close distance (<3 km) and medium distance districts (>3 and <10 km), whereas in the remote distance district (>10 km), gender and income also affected WTP decision aside from the knowledge of WRPs. In terms of WTP amounts, income was the determining factor in the three districts, whereas age was a factor in the medium distance area. Results also showed that the growth rate of the water price should vary according to the distance of the area from the location of the WRPs. The water price was recommended to be increased by US$ 0.522/ton, 0.520/ton, and 0.509/ton in the close, medium, and remote distance districts, respectively.
authors.affiliation: State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, 210023, Nanjing, People's Republic of China.
authors.forename: Jia
authors.initials: J
authors.lastname: He
authors.forename: Xiang
authors.initials: X
authors.lastname: Sun
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zhu
mesh_terms: D002681:China; D003247:Conservation of Natural Resources / Q000191:economics*; D003657:Decision Making; D004784:Environmental Monitoring; D005260:Female; D006801:Humans; D007182:Income; D060106:Lakes; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D012737:Sex Factors; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires; D053833:Wetlands