全球海洋保护区的扩大和捕捞努力的重新分配（英）2025

Policy Research Working Paper Global Expansion of Marine Protected Areasand the Redistribution of Fishing Effort Gavin McDonaldJennifer BoneChristopher CostelloGabriel EnglanderJennifer Raynor Policy Research Working Paper11030 Abstract difference between these scenarios represents the predictedchange in fishing effort resulting from MPA expansion. Theresults show that, regardless of the MPA network’s objec-tive or size, fishing effort would decrease inside the MPAs,though by much less than 100%. Moreover, this reduc-tion in fishing effort within MPAs does not simply shiftoutside—fishing effort outside MPAs also declines. Theoverall magnitude of the predicted decrease in global fishing The expansion of marine protected areas (MPAs) is a corefocus of global conservation efforts, with the “30x30” ini-tiative to protect 30% of the ocean by 2030 serving as aprominent example of this trend. This paper examines aseries of proposed MPA network expansions of various sizesand forecasts the impact that increased protection couldhave on global patterns of fishing effort. This is accom-plished using a predictive machine learning model trained The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmentissues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry thenames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely thoseof the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and Globalexpansionofmarineprotectedareasandthe GavinMcDonald1,2,3*,JenniferBone1,2,3,ChristopherCostello1,2,3,GabrielEnglander4,JenniferRaynor1MarineScienceInstitute,UniversityofCalifornia,SantaBarbara,USA.2BrenSchoolofEnvironmentalScienceandManagement,UniversityofCalifornia,SantaBarbara,USA.3EnvironmentalMarketsLab,UniversityofCalifornia,SantaBarbara,USA.4DevelopmentResearchGroup,TheWorldBank,USA.5DepartmentofForestandWildlifeEcology,UniversityofWisconsin-Madison,USA. *Correspondingauthor(s).E-mail(s):gmcdonald@bren.ucsb.edu;Contributingauthors:jbone@bren.ucsb.edu;ccostello@bren.ucsb.edu;aenglander@worldbank.org;jraynor@wisc.edu; 1 Introduction Simulation methods developed in the fisheries lit-erature [10] and location choice models developedin the economics literature [6, 11–14] are help-ful for understanding the structure of individualbehavior, but are unlikely to apply when con-sideringcomplex interactions between multiplefleets at the global scale. They also often requiredetailed vessel-level data, which are rarely avail-able globally even with modern satellite tracking.Causal inference methods have been effective inexamining regional effects of individual marineprotectionpolicies[15–18],but these methodsrequire an unaffected control group, which by def-inition does not exist for a policy that induces The expansion of marine protected areas (MPAs)is a crucial part of global conservation efforts [1].The “30x30” initiative, for example, aims to pro-tect at least 30% of the world’s oceans by 2030through a combination of fully protected areas(noextractive activities allowed)and partiallyprotected areas (some activities remain permit-ted) [2, 3]. Currently, fully protected MPAs coverless than 3% of the world’s oceans, but this isexpected to increase [4, 5]. As fully protectedMPAs expand, it is crucial to understand theirimpact on global fishing effort. The creation of tected MPA expansion depends on how fishingeffortresponds.If fishing effort simply moveselsewhere, it could increase fishing intensity andthreaten biodiversity outside of MPAs, possiblyeven reversing the presumed biodiversity bene-fits of protection [7]. However, if fishing effortdecreases (e.g., due to increased competition and We develop the first data-driven, predictivebehavioral model of global fishing effort responsefollowing large-scale spatial closures. We begin bycompiling a global dataset of fishing effort forall industrial fishing vessels that used AutomaticIdentification System (AIS) transponders between2016 and 2021 [24], which is our outcome vari-able (Fig. 1). We then generate 42 model featuresthatinclude spatial and temporal informationon the geographic distribution of fully protectedMPAs, environmental and economic conditions,andgeographic and governance characteristics;we also assess AIS reception quality, which canaffect perceived fishing effort from AIS transpon-ders (see Materials and Methods for a complete For example, two of the largest fully protectedMPAs ever created, Phoenix Islands ProtectedArea and Palau National Marine Sanctuary, wererecently re-opened to fishing because of their per-ceived negative economic effects. In fact, protectedarea downgrading, downsizing, and degazettementof MPAs has been observed in dozens of MPA