State Implementation of Federal Environmental Policy

Explaining Leveraging in the Clean Water State Revolving Fund

Dados Bibliográficos

ID6328294
AutoresRick Travis, John C Morris (0000-0001-9820-5618), Elizabeth D Morris
Ano2004
Volume32
Fascículo3
Páginas461-480
Data de publicação2004-08-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPolicy Studies Journal (JOURNAL)
Identificadores do periódicoISSN: 0190-292X • E-ISSN: 1541-0072
EditoraWiley (PUBLISHER • GB)
DOI10.1111/j.1541-0072.2004.00075.x
OpenAlexW2087205511
IdiomaEN
Citações recebidas17
Referências citadas18

The Clean Water State Revolving fund (CWSRF) program provides states with significant discretion to design, implement, and administer the program to meet their water quality needs. An important aspect of the program is the decision whether to leverage existing funds to generate additional capital for loans. This article seeks to explain the leveraging decision process, using three models as explanators. We conceive leveraging as a two‐stage process—(1) the decision to leverage and (2) the decision of how much to leverage. By employing regression techniques, we find that the Environmental Protection Agency's (EPA’s) stated reasons for leveraging—environmental needs and demand for loans—are not significant in the decision to leverage, although needs are significant at the second stage. Likewise, there is limited support for Lester's (1994) “capacity/commitment” model of environmental implementation. A third model employing a larger range of independent variables is the best predictor of both the decision to leverage and the amount of leveraging. Furthermore, the factors important at each stage of the decision process differ substantially, reinforcing our conception of a two‐stage decision process

Business · Decision process · Discretion · Economics · Environmental economics · Leverage (statistics · Political science · Process management · Climate Change Policy and Economics · Computer Science · Economic and Environmental Valuation · Water resources management and optimization