Abstract:

 Based on survey data in China Meteorological Administration in 2008, the impact factors about willingness to pay (WTP) for public meteorological service were analyzed by the methods of a binary Logistic regression, an optimal scaling regression and a classification regression tree (CRT). The results show that the individual factors such as age and education level have significant influence on WTP including whether to pay (WTP1) and how much to pay (WTP2), while gender has only significant influence on WTP2 but not on WTP1. The income has significant influence to WTP. The four factors including concerning degree for weather forecast, accuracy and timeliness of prediction, overall satisfaction on meteorological service have significant influence on WTP2, while the impact is very small. The individual factors and income have greater impact on WTP compared with the psychological factors.

Key words: Willingness to pay, Logistic regression, Optimal scaling regression, Classification regression tree (CRT) method