Abstract
The Taiwanese government has actively proposed numerous policies in response to problems encountered by distant-water fisheries. However, an indicator framework for evaluating the performance of distant-water fishery policies does not exist. Therefore, this study established an indicator framework to evaluate the performance of the policies regarding the Taiwanese distant-water fishery. Based on a literature review and current fishery policies, we proposed 61 indicators. Three rounds of questionnaires using the Modified Delphi method were conducted that involved experts from industry, the government, and academia and 43 policy indicators were selected in the ecological, economic, social, and institutional dimensions according to the results. Because of limited data access, 30 indicators were selected as the final indicators. The analytic hierarchy process (AHP) was used to analyze the weight value of each indicator before the indicators were aggregated. Finally, relevant indicators were used to analyze the performance of distant-water fishery policies. The results indicated that ecological policies performed exceptionally, achieving the policy goals of establishing a biological database, practically developing fishery resources, strengthening observation systems, and conducting international scientific research exchanges. The effects of the economic policies were not significant, indicating that the performance of this policy in promoting the marketing of distant-water fish catches and relevant fishery costs and subsidies require further improvement. Additionally, the effects of the social policies were not significant, suggesting that the performance of this policy in cultivating fishery experts and enhancing fisherman requires strengthening. Finally, the institutional policies achieved the goals of adjusting the structure of the distant-water fishery, expanding international cooperation, and maintaining regulations regarding international waters, thereby indicating that the institutional policy performance was optimal.
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