Rural versus urban preferences for renewable energy developments
By A. Bergmann, S. Colombo, N. Hanley. 2008
276 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management, Monitoring, Policy and Law, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/e1b15cc8e86cd41c17054eeda5f0ce9dca344275
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- S. Garcia
- J. Abildtrup
- Marieke Oteman
- J. Helderman
- Nicholas H. Johnson
- M. Wiering
- Barry D. Solomon
- A. Stenger
In Management, Monitoring, Policy and Law
- S. Garcia
- J. Abildtrup
- Nicholas H. Johnson
- Barry D. Solomon
- A. Stenger
- M. Ollikainen
- S. B. Olsen
- A. Kosenius
In Sociology and Political Science
Citations in the corpus
- Valuation of environmental and societal trade-offs of renewable energy sources (2013, methodology): “Previous similar studies are especially Hanley and Nevin (1999), Bergmann et al. (2006, 2008), and Scarpa and Willis (2010).”
- The institutional space of community initiatives for renewable energy: a comparative case study of the Netherlands, Germany and Denmark (2014, background): “Furthermore, the degree of urbanization influences the preferences for RE developments and the readiness to take local action [18].”
- Valuing climate protection through willingness to pay for biomass ethanol (2009, background): “The other dummy variable was for rural vs. urban or suburban residence, with the hypothesis that rural residents would have a greater WTP because of the potential to profit off of rural resources and to increase employment opportunities (Bergmann et al., 2008, pp. 622–623).”
- Spatial preference heterogeneity in forest recreation (2013, background): “Other environmental valuation studies have addressed spatial preference heterogeneity on a rather coarse scale by, for example, including regional dummies in the estimated choice model or estimating separate models for different locations (e.g., Bergmann et al., 2008; Brouwer et al., 2010).”