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Rebecca Elliott

Researcher at London School of Economics and Political Science

Publications -  17
Citations -  328

Rebecca Elliott is an academic researcher from London School of Economics and Political Science. The author has contributed to research in topics: Flood insurance & Flood myth. The author has an hindex of 6, co-authored 16 publications receiving 203 citations. Previous affiliations of Rebecca Elliott include University of California, Berkeley.

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The taste for green: The possibilities and dynamics of status differentiation through “green” consumption

TL;DR: The authors employ a logistic regression analysis on national data from a 2008 ABC News/Discovery Channel/Stanford University survey of American adults to show that an increasing level of education is positively related to the odds of finding green consumption desirable being female, having children under the age of 18 years old in the home and identifying as an environmentalist are also positive predictors.
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The Sociology of Climate Change as a Sociology of Loss

TL;DR: The sociology of loss as discussed by the authors is a project for sociological engagement with climate change, one that breaks out of environmental sociology as the conventional silo of research and bridges to other subfields.
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Who Pays for the Next Wave? The American Welfare State and Responsibility for Flood Risk:

Rebecca Elliott
- 20 Jun 2017 - 
TL;DR: This paper examined the renegotiation of that social contract through the lens of contested efforts to reform the massively indebted US National Flood Insurance Program (NFIP) from 2011 to 2014, and defined "deservingness" in terms of ability to pay for risk exposure, qualifying an individualization of responsibility for addressing the problem of flood loss.
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Scarier than another storm: values at risk in the mapping and insuring of US floodplains

TL;DR: An investigation of the social life of the flood insurance rate map, the central technology of the U.S. National Flood Insurance Program, as it grafts a new landscape of 'value at risk' onto the physical and social world of New York City in the aftermath of Hurricane Sandy is investigated.
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Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: exploring the roles of topography, minority populations, and political dissimilarity

TL;DR: This research indicates that quantitative incorporation of social data can improve flooding exposure estimates, and Random Forest regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US.