Politics of problem definition: Comparing public support of climate change mitigation policies using machine learning

Junghwa Choi, Wesley Wehde, Romit Maulik

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Public support is a key contributor to successful policy adoption and implementation. Given the urgency of climate change mitigation, scholars have explored various determinants that affect public support for climate change mitigation policy. However, the relative decisiveness of these factors in shaping public support is insufficiently examined. Therefore, we deploy interpretable machine learning to understand which factors, among many previously investigated, are most decisive for structuring public support for various climate change mitigation policies. In this paper, we particularly look at the decisiveness of problem definition for shaping public support among various factors. Using U.S national survey data, we find that how individuals define the issue of climate change is more decisive for structuring public support for promoting renewable energy and regulating pollutants to mitigate the risks associated with climate change. However, the results also indicate that the most decisive factors associated with public support vary depending on the types of mitigation policy. We conclude that different strategies should be utilized to increase public support for various climate change mitigation policy options. Our findings contribute to a scholarly understanding of the specific politics of problem definition in the context of environmental and climate change policy.

Original languageEnglish (US)
Pages (from-to)104-134
Number of pages31
JournalReview of Policy Research
Volume41
Issue number1
DOIs
StatePublished - Jan 2024

All Science Journal Classification (ASJC) codes

  • Political Science and International Relations
  • Management, Monitoring, Policy and Law
  • Sociology and Political Science
  • Public Administration

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