ECONOMIC EFFICIENCY OF INTEGRATING FISHERIES MANAGEMENT AND ENVIRONMENTAL CONSERVATION: INTERNATIONAL EXPERIENCE AND PROSPECTS FOR UKRAINE
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".