An important policy question receiving considerable attention concerns the risk perception-risk mitigation process that guides how individuals choose to address natural hazard risks. This question is considered in the context of wildfire. We analyze the factors that influence risk reduction behaviors by homeowners living in the wildland-urban interface. The factors considered are direct experience, knowledge of wildfire risk, locus of responsibility, fulltime/seasonal status, and self-efficacy. Survey data from three homeowner associations in the western U.S. are used to estimate the direct and indirect effects of this relationship. Our results indicate that the effects of knowledge and locus of responsibility are mediated by homeowners' risk perceptions. We also find that beliefs of self-efficacy and fulltime/seasonal status have a direct influence on risk reduction behaviors. Finally, we find, surprisingly, that direct experience with wildfire does not directly influence the risk perception-risk mitigation process.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jenvman.2009.09.007 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFWeb application using machine learning to predict cardiovascular disease and hypertension in mine workers.
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFExploring optimal control strategies in a nonlinear fractional bi-susceptible model for Covid-19 dynamics using Atangana-Baleanu derivative.
Sci Rep
December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
View Article and Find Full Text PDFFuture increase in compound soil drought-heat extremes exacerbated by vegetation greening.
Nat Commun
December 2024
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Compound soil drought and heat extremes are expected to occur more frequently with global warming, causing wide-ranging socio-ecological repercussions. Vegetation modulates air temperature and soil moisture through biophysical processes, thereby influencing the occurrence of such extremes. Global vegetation cover is broadly expected to increase under climate change, but it remains unclear whether vegetation greening will alleviate or aggravate future increases in compound soil drought-heat events.
View Article and Find Full Text PDFContinuous decoupled redox electrochemical CO capture.
Nat Commun
December 2024
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University & Shenzhen University, Chengdu, P.R. China.
Electrochemical CO capture driven by renewable electricity holds significant potential for efficient decarbonization. However, the widespread adoption of this approach is currently limited by issues such as instability, discontinuity, high energy demand, and challenges in scaling up. In this study, we propose a scalable strategy that addresses these limitations by transforming the conventional single-step electrochemical redox reaction into a stepwise electrochemical-chemical redox process.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!