JOURNAL ARTICLE
Explaining state efforts to create Total Maximum Daily Load (TMDL) agreements.
Published In: Social Science Quarterly (Wiley-Blackwell), 2024, v. 105, n. 5. P. 1776 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Mayer, Martin K.; Morris, John C.; McNamara, Madeleine W.; Zhang, Xiaodan 3 of 3
Abstract
Objectives: With the rejuvenated emphasis on nonpoint pollution under the Water Quality Act (WQA) of 1987, Environmental Protection Agency (EPA) began to face an onslaught of lawsuits designed to pressure the EPA to enforce the requirements of Section 319 of the WQA to address nonpoint pollution. Known as Total Maximum Daily Load (TMDL) agreements, the purpose of these plans was to limit the amount of polluted runoff reaching a state's waterways. While some states took a proactive stance on these plans, other states resisted the implementation of Section 319. This article seeks to understand state choices in the development and implementation of TMDL agreements. Methods: Utilizing a data set spanning state‐level data from 2000 to 2020, we test a novel cross‐sectional time series model employing the number agreements entered into by a state as the dependent variable. Results: We find that both political and need explanations are generally supported, while policy need explanations are somewhat more promising. Conclusions: Taken together, the models offer several insights into state choices around TMDL creation. The political model is the weakest, suggesting that TMDLs are not overtly political. Policy needs seem to play a more critical role in the preponderance of TMDL agreements than partisan politics. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Social Science Quarterly (Wiley-Blackwell). 2024/09, Vol. 105, Issue 5, p1776
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2024
- ISSN:0038-4941
- DOI:10.1111/ssqu.13444
- Accession Number:180088570
- Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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