Estimating the economic benefits of urban trees using contingent valuation method

Urban trees provide multitude of tangible and intangible services, which include provisionary, regulatory, cultural and support services to the community. Urban trees are important in reducing the environmental quality impacts such as air pollution. Unfortunately, to set a monetary value on these said services is challenging to say the least. Thus, there is a lack of economic benefits of urban trees study in Malaysia. Ignorance of such monetary value is unintentional and mainly due to lack of awareness and the absence of monetary value of the services itself. Hence, the quality of these urban trees degrades over time as no one appreciates its monetary value. In light of this situation, a study was initiated to determine the economic benefits of urban trees that have been planted surrounding the Tasik Perdana (TP) area. TP is selected to be the study site not just because it is the oldest green park (urban park) and a famous recreation place as recommended by the tourism industry, but also to value the economic benefit of its urban trees. Local government is willing to spend about RM 5.6 million to manage and maintain urban green especially the urban trees. It is clear that local government attaches the importance of these urban trees. However, there is no proof or study to define the value of urban tress in this park. To value the economic benefit of the urban trees, a total of 313 respondents were interviewed in the TP area using the contingent valuation method (CVM). The payment vehicle used in the study was an additional fund for preserving the urban trees in TP. The objective of this study is to elicit willingness to pay (WTP) for urban trees conservation. The WTP represents the willingness of a person to pay in onetary terms to secure and sustain these urban trees. Seven bid prices were used and distributed to respondents – RM 1.00, RM 5.00, RM 10.00, RM 15.00, RM 20.00, RM 25.00 and RM 30.00. Logit and linear regression models were applied to predict the maximum, mean, and median WTP.

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