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ECONOMIC EFFICIENCY OF INTEGRATING FISHERIES MANAGEMENT AND ENVIRONMENTAL CONSERVATION: INTERNATIONAL EXPERIENCE AND PROSPECTS FOR UKRAINE

2025· article· en· W7117100539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBaltic Journal of Economic Studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Science and Water Management
Canadian institutionsnot available
FundersEuropean Commission
KeywordsFisheries managementSustainabilityFisheries lawWater Framework DirectivePopulationContext (archaeology)Food securityWetland

Abstract

fetched live from OpenAlex

The article examines the integration of fisheries management and environmental conservation as an innovative model for the sustainable use of aquatic bioresources in both national and international contexts. The subject of the present study is the mechanisms that combine the economic interests of the fisheries sector with ecological objectives, including biodiversity conservation, population recovery, and maintaining the stability of aquatic ecosystems. The relevance of the research is driven by global challenges, including overfishing, poaching, degradation of spawning grounds, climate change, and water pollution. These issues are particularly acute for Ukraine, where the fisheries sector is of both food-related and strategic socio-economic importance, and is characterised by high levels of shadow activity and weak enforcement. The methodological framework underpinning this study combines comparative, content, and case-study approaches. The research compares Ukrainian and international models of fisheries management, taking into account the experiences of the European Union, Canada, Japan, and the Baltic States. It also analyses international conventions and directives, such as the Convention on Biological Diversity, the EU Water Framework Directive and the FAO Code of Conduct for Responsible Fisheries, as well as Ukraine's national legislation. System analysis methods are also employed to integrate ecological, economic and social factors into a unified model. Case studies include Ukrainian protected areas such as the Danube Biosphere Reserve, the Lower Dniester National Nature Park and the Ramsar wetlands of the Dniester Delta, as well as international fish stock restoration practices. The study aims to identify effective instruments for integrating fisheries management with conservation mechanisms, and to develop recommendations for adapting them to Ukrainian conditions. The article discusses international models such as community-based co-management in Canada, aquaculture and marine protected area development in Japan, fish passage use in the Baltic States, and legal harmonisation of environmental and economic goals within the EU. The main findings confirm that a holistic approach ensures the simultaneous achievement of three sets of objectives: ecological (population and biodiversity restoration), economic (increasing the profitability of the fisheries sector and developing aquaculture and recreational fishing tourism) and social (local community involvement and improved governance transparency). Priority areas for Ukraine include aligning legislation with EU environmental directives, developing innovative monitoring technologies (such as eDNA and satellite systems), legalising the shadow sector, and expanding co-management practices involving local communities and fisheries co-operatives. The study concludes that integrating fisheries management with environmental conservation is essential for Ukraine to transition to a sustainable model of aquatic bioresource use. This approach enables both the ecological resilience of water bodies and the economic efficiency of the sector. Adopting the best global practices, from EU environmental directives to Japan’s integration of aquaculture and marine protected areas, can enhance the competitiveness of Ukraine’s fisheries sector and facilitate its integration into the international market.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.251
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it