Exploring the Blue Economy Nexus: Government, Industry, and Market’s Perspectives on Seafood
Notice bibliographique
Résumé
Seafood plays a pivotal role in global economies, livelihoods, and nutritional security. However, climate change and global pandemics pose significant threats to seafood harvests, production, supply chains, and marketing channels. The focus of my thesis is to understand the impact of external factors on our seafood resources and explore adaptive strategies in the face of uncertainties. We utilize economics techniques to study human-nature systems by zooming into social elements (government agencies, industry stakeholders, and fish farmers/fishermen) and aquatic resources. The three essays of my thesis delve into this inquiry from the perspectives of government, industry, and market, accordingly.The first chapter in my thesis, <i>Climate Change and Snow Crab Harvest - Applying Random Effect Estimators with Instrumental Variable</i>, estimates the snow crab harvest function with unbalanced panel data of eastern Bering Sea snow crab, Canadian snow crab, Japanese snow crab, and Barents Sea snow crab. Specifically, we analyze the relationship between snow crab biomass, stock, and catch. To address the endogeneity of stock in the harvest function, climate change indicators are selected as instrumental variables. We identify that the Arctic Sea ice extent is effective in addressing the endogeneity and the random effects instrumental variable model with error components two stage least squares estimator performs the best to control heterogeneity. We find that a 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock-harvest elasticity and provide supporting evidence for government fishery agencies to prioritize stock enhancement in policy designs.The second chapter, <i>Online Media Sentiment Analysis of Shrimp and Salmon in the United States</i>, employs online media analytics on shrimp and salmon in the US to provide insights into consumer perceptions and potential demand signals for seafood. Search hits and mentions are quantified for top sources, domains, and prevalent terms. In addition, sentiment drivers and sentiment values are identified and calculated using natural language processing tools. The results reveal that the occurrence of peak mentions does not necessarily coincide with the peak of net sentiment, and farmed seafood consistently exhibits lower net sentiments compared to their wild counterparts. Autoregressive modeling is conducted to predict the dynamics of seafood’s net sentiments. The regional analysis demonstrates that public attitudes toward both farmed shrimp and salmon in the East North Central region exhibit a more positive net sentiment, while the New England and Middle Atlantic regions tend to have a lower net sentiment for farmed shrimp and salmon, respectively. The fitted forecast model serves as a supplementary tool for industry stakeholders to quickly respond to future public perceptions. Regional statistics also help the seafood industry tailor business strategies to different regions.In the third chapter, <i>Comparative Case Study of Small-Scale Fish Processing for Local Seafood Supply</i><i>,</i> we examine the feasibility of utilizing a shared-use commercial kitchen and on-farm kitchen to support small-scale local fish processing, which helps diversify marketing channels in the US Midwest and supply seafood to local food systems. A case study of each facility type is assessed for economic viability for fish farmers. The financial analysis suggests farmers interested in processing tilapia or rainbow trout from 2,500 lbs to 5,000 lbs per year utilize rental commercial kitchens. A minimum of 15% markup and processing of 10,000 lbs/year tilapia is required to make the on-farm kitchen option more viable. For farmers who process rainbow trout, 10,000 lbs/year with a 10% markup using an on-farm kitchen is a better choice. Factoring in the stochastic variability of raw product prices, rental rates, and set-up costs, we provide simulated ranges for economic metrics including profitability index, payback period, and net present values. The reports of estimated costs, revenues, and breakeven prices, provide fish farmers with suggested selling prices, kitchen choices, and production levels to achieve optimum profits under risks.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».