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- W2199804951 abstract "Landscape pattern has long been hypothesized to influence automobile dependency. This paper extends this inquiry of quantifying landscape pattern to predict automobile use by drawing on satellite data in order to gain greater spatial coverage of household profiles. Furthermore, because choices about land development tend to have long-lasting impacts that span over decades, understanding the magnitude of this influence is critical to the design of policies to reduce emissions and other negative externalities associated with car use. Combining household survey data from Germany with satellite imagery and other geo-referenced data sources, the authors address this topic with an econometric analysis of the relationship between landscape pattern and automobile dependency. Specifically, the authors employ a two-part model to investigate two dimensions of car use, the discrete decision to own a car and, conditional upon ownership, the continuous decision of how far to drive. Results indicate that landscape pattern, as captured by measures of both land cover (e.g. the extent of open space and landscape diversity) and land use (e.g. the density and composition of regional businesses) are important predictors of car ownership and use. Other policy-relevant variables, such as fuel prices and public transit infrastructure, are also identified as important correlates. Based on the magnitude of the estimates the authors conclude that carefully considered land development measures, ones that encourage dense development and mixed use, can have beneficial impacts in reducing car dependency that extend far into the future." @default.
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- W2199804951 date "2013-01-01" @default.
- W2199804951 modified "2023-09-23" @default.
- W2199804951 title "Landscape Pattern and Car Use: Evidence Linking Household Data with Satellite Imagery" @default.
- W2199804951 hasPublicationYear "2013" @default.
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