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Ocean Health Production Functions Using Tobit and Rank Regression Models Public Deposited

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  • Ocean health is a major concern for coastal communities dependent on the ocean for their livelihood. Recent changes in in ocean health force researchers to examine the factors that influence the decline in ocean health. The paper developed production functions using the Global Ocean Health Index (OHI) for 2014 and UN environmental and climatic databases. The study useda two-stage regression model to determine factors that influence OHI. The use of accurate and relevant methods such as the Tobit model and/or rank-based regression helped improve the reliability of the statistical coefficients by uncovering nonlinear covariate effects and improving the performance of the decision models. We employed Ocean Health Statistics for 151 countries plus data from the Human Development Index (HDI) of 2014. Model results showed for the Tobit model, the variable biodiversity had a major effect on ocean health and contributed succinctly to the variation in OHI. Other goals that affect OHI are livelihoods and economies, sense of place, clean water, and artisanal fisheries. The rank regression model showed HDI and Marine Protected Areas (MPAs) significantly influenced OHI. HDI is positively related to OHI: countries with high HDI, like many European countries and the US, also have high OHI. Policy makers should note that biodiversity and MPA increases may have the greatest effect on OHI, and its improvement may be within the reach of even the poorest country. Key words: Ocean, Health, Tobit, Rank, Regression.
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  • Seattle, Washington, USA
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