Assessing the welfare impacts of changes in recreational fisheries management: A modelling approach for European sea bass
Assessing the welfare impacts of changes in recreational fisheries management: A modelling approach for European sea bass
复制标题
评估休闲渔业管理变化对福利的影响:欧洲鲈鱼建模方法
DOI:
10.1016/j.marpol.2022.105408
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发表时间:
2023
期刊:
影响因子:
3.8
通讯作者:
Cevenini F
中科院分区:
文献类型:
--
作者:
Cevenini F
Economic appraisal of the impacts of fisheries policies is a legal requirement in many global fisheries and is crucial for effective policy making. Some changes in costs and benefits attributable to policy can be assessed through impacts on markets, whilst others are more difficult, as markets may not exist. Analysis is needed to assess value generated by fisheries under different management regimes. The northern stock of European sea bass (Dicentrarchus labrax) has declined rapidly over the past decade leading to management of both the commercial and recreational sectors. Bag limits, closed seasons, and minimum sizes were introduced for recreational fishers, but the social impact of management measures has not been quantified. Here, we use sea bass to demonstrate a method for assessing the impact of management measures on recreational fisher welfare. Through combining stock assessments, individual catch data, and estimates of welfare from a recent choice experiment, differences in welfare under a range of regulatory scenarios were compared. Nine scenarios based on management implemented between 2014 and 2020 were tested. Highest welfare estimates were found with the lowest levels of restrictions and lowest estimates for a no-take fishery, which was driven by retained fish having a higher value than released fish. There was a difference of £ 22.03 million in estimated welfare generated by the recreational sector between these extremes, with the remaining scenarios amid those values. Our results highlight the need for policy makers to carefully consider management options that protect both fish stocks and the welfare of recreational fishers.
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影响因子:
3.7
作者:
Andrew Carr;S. Steinback
通讯作者:
S. Steinback
影响因子:
3.7
作者:
Zachary Radford;K. Hyder;L. Zarauz;E. Mugerza;K. Ferter;R. Prellezo;H. Strehlow;B. Townhill;Wolf;M. S. Weltersbach
通讯作者:
M. S. Weltersbach
影响因子:
1.2
作者:
W. Potts;T. Jordan;A. Childs
通讯作者:
A. Childs
影响因子:
1.6
作者:
L. Hunt
通讯作者:
L. Hunt
影响因子:
6.7
作者:
Jennings, Simon;Rice, Jake
通讯作者:
Rice, Jake