The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Regulatory Competition and Environmental Enforcement: Is There a Race to the Bottom?
Open accessAbstract
This article examines several of the key hypotheses suggested by the race to the bottom theory in environmental regulation. The research studies annual state‐level enforcement of federal air, water, and hazardous waste pollution control regulation, covering the period from 1985 to 2000. Specifically, the study estimates a series of strategic interaction models to examine whether a state's environmental regulatory behavior is influenced by the regulatory behavior of the states with which it competes for economic investment. While there is clear evidence of strategic interaction in state environmental regulatory behavior, states do not respond in the asymmetric manner suggested by the race to the bottom theory.
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Cites
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Regression Analysis of Count Data
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Cited by
Lv, C., Shao, C., & Lee, C.-C. (2021) · Energy Economics · doi:10.1016/j.eneco.2021.105237 · Cited by 769
Truby, J. (2018) · Energy Research & Social Science · doi:10.1016/j.erss.2018.06.009 · Cited by 499
Cheng, Y., Awan, U., Ahmad, S., & Tan, Z. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120398 · Cited by 401
Climate Regulatory Risk and Corporate Bonds
Seltzer, L., Starks, L., & Zhu, Q. (2022) · National Bureau of Economic Research · doi:10.3386/w29994 · Cited by 380
Peng, X. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139200 · Cited by 292
Metadata
- Authors
- Konisky, D. M.
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2007-10
- Volume
- 51
- Issue
- 4
- Pages
- 853-872
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W2092050244
- Cited by
- 489
- References
- 39
- FWCI
- 18.16
- Type
- journal article
- Language
- en
- Open access
- green
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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.
