| Summary: | 碩士 === 國立中興大學 === 農業經濟學系 === 90 === Since 1995, after the Council of Agriculture listed all the cetacean species as protected wildlife, cetacean in Taiwan no longer has been used as one of the fishery harvests. The prosperity of whale-watching activities brought a brand new utilizing style to this unique wildlife. Since the weekly two-days-off policy has been carried out in this country, whale-watching has turned out to be the latest highly potential recreation activity, which especially reflects on the prosperous development of the whale-watching business in Taiwan. Lacking of economic evaluation on whale-watching activities, the authorities is in need of more information on making policy priorities of conservation schemes and management plans for the cetacean. This research chose five of the location with the most abundant cetacean resources on Taiwan’s eastern coast, and focused on recreational benefit derived from whale-watching activities. Encountering multiple whale-watching sites on Taiwan’s eastern coast, this paper applied random utility model (RUM). The five whale-watching sites studied in this research are: Gung-Fong and Su-Auh Harbors in I-lan County, Hualian and Shih-Tih Harbors in Hualian County, and Cheng-Kung Harbor in Taitung County.
The evaluation framework of whale-watching recreational demand first estimated a multinomial logit function of the random utility model and examined the independence of irrelevant alternatives (IIA) test. A two-level nested structure assumes the whale-watching tourists would first choose the whale-watching area and then the sites. Empirical results are stated as follow:
1. According to the IIA test, substitution exists among all the whale-watching sites. Results of the Two-level nested multinomial logit model show that the convenience of whale-watching information and transportation condition are influential when choosing whale-watching area. Meanwhile, for whale-watching sites selection, travel cost turns out to be the main variable to affect the tourists’ choices. After choosing the area, the closest whale-watching site from home would be the favorite one.
2. In the two-stage decision mode, the “inclusive value” of nested multinomial logit model was significant and the value is between 0 and 1, implying the classification to the whale-watching site is adequate. The assumption of the two-stage decision behavior is reasonable. Because the “inclusive value” is closed to 0, the substitution effects among the whale-watching sites in the same area are very strong.
Recreational benefits estimates are based on three policy scenarios. The estimation results are as following:
1. When cetacean at all whale-watching sites simultaneously increases 10%, 20%, and 50%, the average recreational benefit could increase to NT$ 730.11, 1463.18, and 3678.00, respectively, per person per trip.
2. When cetacean at each whale-watching sites individually increases 10%, 20%, and 50%, the average recreational benefit could increase to NT$ 170.15, 389.30, and 1330.74, respectively, per person per trip.
3. Individual closure of whale-watching sites would cause NT$ 297.13 loss per person per trip. Among the sites, Gung-fong Harbor losses the most, NT$888.94. The highest loss of area closures would be in I-lan area, followed by Hualian and Taitung areas, suggesting the priority of allocating management resources for cetacean would be I-lan area as the first, Hualian area the second, and Taitung area the last.
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