JOURNAL ARTICLE
How anger and fear influence policy narratives: Advocacy and regulation of oil and gas drilling in Colorado.
Published In: Review of Policy Research, 2024, v. 41, n. 1. P. 12 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Pierce, Jonathan J.; Miller‐Stevens, Katrina; Hicks, Isabel; Castaneda Zilly, Dova; Rangaraj, Saigopal; Rao, Evan 3 of 3
Abstract
When advocating for policy change, coalitions rely on various elements and strategies of policy narratives, including emotions. However, past research on the Narrative Policy Framework, and more broadly on the policy process, has largely ignored the role of emotions. This paper argues that emotions, such as anger and fear, are central to how coalitions advocate for policy change. It explores the role of anger and fear in policy narratives by examining the oral testimony (n = 474) given over four legislative committee hearings in March 2019 concerning Colorado Senate Bill 19‐181. This bill changed the mission of the Colorado Oil and Gas Conservation Commission to prioritize protecting the environment and public health over oil and gas development. This research finds the coalition that successfully supported the bill used anger towards the oil and gas industry, while those that opposed the bill relied more on fear of the uncertain consequences of the bill. It also finds the coalition that opposed the bill relied on self‐characterization as heroes and victims, which was a failed strategy. The implications for this research on the Narrative Policy Framework and, more broadly, for the policy process and advocacy are discussed. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Review of Policy Research. 2024/01, Vol. 41, Issue 1, p12
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2024
- ISSN:1541-132X
- DOI:10.1111/ropr.12519
- Accession Number:174818597
- Copyright Statement:Copyright of Review of Policy Research 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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