RESUMO
The smoke produced by wildfires can travel great distances and lead to respiratory and/or cardiovascular health impacts through inhalation. Individuals can reduce exposure by implementing smoke mitigation measures in their homes and beyond. In this article, we examine household level survey data (n = 543) on wildfire smoke mitigation in response to the September 2020 wildfires that occurred in the state of Oregon (and beyond). The air quality was hazardous for about 10 days in many affected regions. This study assessed the implementation of six commonly referenced approaches to reducing exposure to smoke: staying indoors; keeping doors and windows closed, turning on HVAC; using air purifiers; replacing air filters, and wearing face masks. We found high levels of implementation of staying indoors and keeping doors and windows closed; however, statistical analysis of socioeconomic demographics suggests that respondents vary in the implementation of the other measures. Income, number of exposure days, and access to information on smoke mitigation were positively associated with the implementation. Given the importance of information access for implementation for three of the measures, we also present data on how different age groups prefer to be contacted about air quality and smoke mitigation. For example, participants above 65 years of age prefer local TV as opposed to social media, whereas text messages were favored by all age groups. These survey results will help to inform the design of campaigns to engage community members differentially and potentially affect best communication practices and other assistance/preparation for smoke mitigation across demographics.
RESUMO
Climate change uncertainty makes decisions for adaptation investments challenging, in particular when long time horizons and large irreversible upfront costs are involved. Often the costs will be immediate and clear, but the benefits may be uncertain and only occur in the distant future. Robust decision-making methods such as real options analysis (ROA) handle uncertainty better and are therefore useful to guide decision-making for climate change adaptation. ROA allows for learning about climate change by developing flexible strategies that can be adjusted over time. Practical examples of ROA to climate change adaptation are still relatively limited and tend to be complex. We propose an application that makes ROA more accessible to policy-makers by using the user-friendly and freely available UK climate data of the UKCP09 weather generator, which provides projections of future rainfall, deriving transition probabilities for the ROA in a straightforward way and demonstrating how the analysis can be implemented in spreadsheet format using backward induction. The application is to afforestation as a natural flood management measure (NFM) in a rural catchment in Scotland. The applicability of ROA to broadleaf afforestation as a NFM has not been previously investigated. Different ROA strategies are presented based on varying the damage cost from flooding, fixed cost and the discount rate. The results illustrate how learning can lower the overall investment cost of climate change adaptation but also that the cost structure of afforestation does not lend itself very well to ROA.
