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Cited by 445

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

The Environmental Kuznets Curve: Seeking Empirical Regularity and Theoretical Structure

Carson, Richard T.·2010·Review of Environmental Economics and Policy

Abstract

In the early 1990s the attention of economists was captured by empirical evidence suggesting that rising income levels in developing countries could be good rather than bad for the environment. This evidence drove a stake into the heart of those opposing growth on environmental grounds. Ultimately, the view that income growth by itself eventually will be good for the environment also appears to be wrong because a causal relationship between income and environmental quality cannot be demonstrated. The original empirical estimates appear fragile at best compared to estimates using more representative datasets, higher-quality data, and more appropriate econometric techniques. More plausible explanations for the observed data revolve around good government, effective regulation, and diffusion of technological change. These factors tend to be related in a diffuse manner with higher income and suggest it is likely, but not inevitable, that a society will choose to reduce pollution levels as it becomes wealthier.

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Metadata

Authors
Carson, R. T.
Venue
Review of Environmental Economics and Policy
Published
2010-01-01
Volume
4
Issue
1
Pages
3-23
ISSN
  • 1750-6816 (ISSN-L)
  • 1750-6824
OpenAlex
W2149068464
Cited by
445
References
6
FWCI
45.12
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Environmental Kuznets Curve: Seeking Empirical Regularity and Theoretical Structure. https://app.cochat.ai/papers/10.1093/reep/rep021
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Carson, R. T. (2010). The Environmental Kuznets Curve: Seeking Empirical Regularity and Theoretical Structure. Review of Environmental Economics and Policy. https://doi.org/10.1093/reep/rep021

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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.