Coupling Fishery Dynamics, Human Health And Social Learning In A Model Of Fish-Borne Pollution Exposure
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Pollution-induced illnesses are caused by toxicants that result from human activity and are often entirely preventable. However, where industrial priorities have undermined responsible governance, exposed populations must reduce their exposure by resorting to voluntary protective measures and demanding emissions abatement. This paper presents a coupled human-environment system model that represents the effects of water pollution on the health and livelihood of a fishing community. The model is motivated by an incident from 1949 to 1968 in Minamata, Japan, where methylmercury effluent from a local factory poisoned fish populations and humans who ate them. We model the critical role of risk perception in driving both social learning and the protective feedbacks against pollution exposure. These feedbacks are undermined in the presence of social misperceptions such as stigmatization of the injured. Through numerical simulation and scenario analysis, we compare our model results with historical datasets from Minamata, and find that the conditions for an ongoing pollution epidemic are highly unlikely without social misperception. We also find trade-offs between human health outcomes, the viability of the polluting industry and the survival of the fishery. We conclude that an understanding of human-environment interactions and misperception effects is highly relevant to the resolution of contemporary pollution problems, and merits further study.
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Michael Yodzis, Chris T. Bauch, Madhur Anand (2016). Coupling Fishery Dynamics, Human Health And Social Learning In A Model Of Fish-Borne Pollution Exposure. UWSpace. http://hdl.handle.net/10012/13880