Psychological Barriers to Bipartisan Public Support for Climate Policy
Abstract
Psychological scientists have the expertise-and arguably an obligation-to help understand the political polarization that impedes enactment of climate policy. Many explanations emphasize Republican skepticism about climate change. Yet results from national panel studies in 2014 and 2016 indicate that most Republicans believe in climate change, if not as strongly as Democrats. Political polarization over climate policy does not simply reflect that Democrats and Republicans disagree about climate change but that Democrats and Republicans disagree with each other. The results of a national panel experiment and of in-depth interviews with four former members of Congress suggest that Democrats and Republicans-both ordinary citizens and policymakers-support policies from their own party and reactively devalue policies from the opposing party. These partisan evaluations occur both for policies historically associated with liberal principles and politicians (cap-and-trade) and for policies associated with conservative principles and politicians (revenue-neutral carbon tax). People also exaggerate how much other Democrats and Republicans are swayed by partisanship. This foments false norms of partisan opposition that, in turn, influence people's personal policy support. Correcting misperceived norms of opposition and decoupling policy evaluation from identity concerns would help overcome these seemingly insurmountable barriers to bipartisan support for climate policy.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
'Us versus them' dynamics can result in stereotyping and false or exaggerated beliefs about the out-group.
“As noted above, ‘us versus them’ dynamics can result in stereotyping and false or exaggerated beliefs about the out-group132,146–149.”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [147]
Online groups that disagree on a given topic are also more likely to be hostile toward each other.
“Social scientific research examines how these phenomena are interconnected across a variety of contexts, such that online groups that disagree on a given topic are also more likely to be hostile toward each other [10].”
From Affective Polarization in Online Climate Change Discourse on Twitter (2020) · cites this paper as [10]
The citation lists haven’t been fetched yet
References 80 · Cited by 228
- Authors
- Van Boven, L., Ehret, P. J., & Sherman, D. K.
- Affiliations
- Department of Psychology and Neuroscience, University of Colorado Boulder
- University of California, Santa Barbara (US)
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2018-07
- Volume
- 13
- Issue
- 4
- Pages
- 492-507
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- OpenAlex
- W2811444565
- Cited by
- 228
- References
- 80
- Citations by year
- 2026: 20 · 2025: 23 · 2024: 30 · 2023: 27 · 2022: 29 · 2021: 45 · 2020: 31 · 2019: 19 · 2018: 4
- FWCI
- 56.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Perspectives on Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (552120, 1049125)
- Topics
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Keywords
- climate policy, political polarization, partisan attitudes, bipartisan support, climate change skepticism, Republicans, Democrats, emissions trading, carbon tax, partisan identity, policy support, public opinion
- MeSH terms
- Attitude, Climate, Group Processes, Humans, Mental Processes, Politics, Public Opinion, United States, Policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review