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The Impacts of the “Hunker Down” order in AnchorageThis brief models the COVID-19 epidemic in Anchorage Alaska to better understand the impact of the Municipality of Anchorage (MOA) “Hunker Down” order and provide insight into the potential benefit of the State of Alaska (SOA) “Stay at Home” order. The economic benefits of the hunker down order are measured in avoided mortality, based on the EPA value of a statistical life of $7.5 million. The benefits are for the epidemic to date based on confirmed cases and a simulation of an Anchorage epidemic based on epidemiological parameters from the scientific literature. Modeling suggests ~5400 deaths were avoided to date. Using a value of a statistical life of $7.5 million, the hunker down order is estimated to have avoided $40.5 billion in mortality due to COVID-19 to date. The economic costs of the shutdown are estimated based on the expected loss of GDP in Alaska, at roughly $4 billion to date. The long run economic costs are not estimated in this report, and will be heavily influenced by efforts by individuals to avoid infection. The estimates of the economic cost are also an upper bound estimate, as many of the costs may have happened regardless of the hunker down order as individuals avoided public spaces to protect themselves.
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Farming along desire lines: Collective action and food systems adaptation to climate change.1. We examine collective action in the food system of the Canadian Maritimes to determine its effect on the resilience and adaptive capacity of food producers, distributors, retailers and governance institutions. 2. Our data suggest that beyond their immediate benefits for their participants, expressions of collective action generate higher-level impacts which often translate into drivers of adaptive capacity. 3. Drawing on a metaphor from urban design, we suggest that collective action should be considered a desire line for food systems adaptation: rather than building adaptation strategies based on top-down design, collective action emerges from farmers’ needs and capacities to build financial resilience, enhance human and social capital and strengthen institutional agency within the system.
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Year Seven Peer‐Evaluation Report, University of Alaska Southeast, Juneau, Alaska, April 23-26, 2019A confidential report of findings prepared for the Northwest Commission on Colleges and Universities
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Effects of Reading Text While Driving: A Driving Simulator StudyAlthough 47 US states make the use of a mobile phone while driving illegal, many people use their phone for texting and other tasks while driving. This research project summarized the large literature on distracted driving and compared major outcomes with those of our study. We focused on distraction due to reading text because this activity is most common. For this research project, we collected simulator observations of 203 professional taxi drivers (175 male, and 28 female) working at the same Honolulu taxi company, using the mid-range driving simulator VS500M by Virage. After a familiarization period, drivers were asked to read realistic text content relating to passenger pick up displayed on a 7-inch tablet affixed to the dashboard. The experimental scenario was simulated on a two-lane rural highway having a speed limit of 60 mph and medium traffic. Drivers needed to follow the lead vehicle under regular and text-reading conditions. The large sample size of this study provided a strong statistical base for driving distraction investigation on a driving simulator. The comparison between regular and text-reading conditions revealed that the drivers significantly increased their headway (20.7%), lane deviations (354%), total time of driving blind (352%), maximum duration of driving blind (87.6% per glance), driving blind incidents (170%), driving blind distance (337%) and significantly decreased lane change frequency (35.1%). There was no significant effect on braking aggressiveness while reading text. The outcomes indicate that driving performance degrades significantly by reading text while driving. Additional analysis revealed that important predictors for maximum driving blind time changes are sociodemographic characteristics, such as age and race, and past behavior attributes.
