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Abstract: 

Certain developing countries disproportionately rely on the ocean for their sustainable development. Yet, the technical and financial assistance they receive from the international development community through country classifications, a mechanism that groups countries based on common development constraints, falls short of addressing their specific ocean-based needs. This is resulting in ineffective investments and weak coordination at a time when most countries are struggling to achieve their United Nations Sustainable Development Goals, including multiple ocean-based targets. In three chapters, this dissertation examines these issues by drawing on global and publicly available datasets to propose a way forward. The first chapter identifies critical ocean policy gaps with current country classifications within the context of countries increasingly self-identifying as ‘large ocean states’ to demonstrate the need for a new Large Ocean State country classification. The second chapter investigates how climate change is impacting fisheries, a key sector for many ocean-based developing countries, by analyzing the plausible causal effect of changing ocean seasonality on global fisheries harvests using fixed effect regression. The third chapter builds on the previous chapters to articulate a new Large Ocean State country classification using unsupervised machine learning. Collectively, the dissertation provides actionable solutions to support large ocean states and promote a healthier ocean.