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Oceanic Influence and Sustainable Development of Large Ocean States.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Oceanic Influence and Sustainable Development of Large Ocean States./
Author:
Hume, Andrew Christer.
Description:
1 online resource (147 pages)
Notes:
Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:
Dissertations Abstracts International84-09B.
Subject:
Cultural organizations. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30306157click for full text (PQDT)
ISBN:
9798374472820
Oceanic Influence and Sustainable Development of Large Ocean States.
Hume, Andrew Christer.
Oceanic Influence and Sustainable Development of Large Ocean States.
- 1 online resource (147 pages)
Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Thesis (Ph.D.)--Stanford University, 2022.
Includes bibliographical references
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 multiple ocean-based international sustainable development priorities, including the United Nations Sustainable Development Goals. 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 illustrate 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.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798374472820Subjects--Topical Terms:
3689769
Cultural organizations.
Index Terms--Genre/Form:
542853
Electronic books.
Oceanic Influence and Sustainable Development of Large Ocean States.
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Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
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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 multiple ocean-based international sustainable development priorities, including the United Nations Sustainable Development Goals. 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 illustrate 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.
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click for full text (PQDT)
based on 0 review(s)
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