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Green and Greenback: The Behavioral Effects of Environmental Attitudes in Low-Cost and High-Cost Situations
Abstract
The low-cost hypothesis predicts that the strength of effects of environmental concern on environmental behavior diminishes with increasing behavioral costs. Thus, environmental concern influences environmental behavior primarily in situations and under conditions connected with low costs and little inconvenience for individual actors. In a first step, we develop and specify this hypothesis. Referring to two procedures, we then test it on the basis of an environmental survey of a random sample of 2307 respondents from the German population. The empirical evidence is positive. The low-cost hypothesis is not confined to the area of environmental research. It points to general limits of attitude-research (in high-cost situations) and to general limits of rational-choice theory (in low-cost situations), and suggests a strategy for integrating research in social psychology, sociology, and economics.
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Olson, M. (1965) · doi:10.4159/9780674041660 · Cited by 14,191
Influences on Attitude-Behavior Relationships: A Natural Experiment with Curbside Recycling
Guagnano, G. A., Stern, P. C., & Dietz, T. (1995) · Environment and Behavior · doi:10.1177/0013916595275005 · Cited by 1,457
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Metadata
- Authors
- Diekmann, A., & Preisendörfer, P.
- Venue
- Rationality and Society
- Publisher
- SAGE Publications
- Published
- 2003-11
- Volume
- 15
- Issue
- 4
- Pages
- 441-472
- ISSN
- 1043-4631 (ISSN-L)
- 1461-7358
- OpenAlex
- W2161304601
- Cited by
- 738
- References
- 2
- FWCI
- 22.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Diekmann, A., & Preisendörfer, P. (2003). Green and Greenback: The Behavioral Effects of Environmental Attitudes in Low-Cost and High-Cost Situations. Rationality and Society. https://doi.org/10.1177/1043463103154002
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.