๐Ÿ‡บ๐Ÿ‡ธIt's the Middle, Stupid! Machine Learning the Impact of Climate Risk Perceptions on Policy Support

Presented at the 8th Annual BayesiaLab Conference on October 30, 2020.

Abstract

While public opinion is a strong driver of policy change in democratic societies, the complex interactions of climate risk perceptions, knowledge and belief in climate science and their combined effects on support for policies aimed at mitigating climate change are not very well understood. This study presents a novel machine learning approach to learn a โ€œprobabilistic structural equation model (PSEM)โ€ for understanding complex interactions among climate risk perceptions, beliefs, knowledge, political ideology, socio-demographics, geographic representation and their combined effects on support for mitigation policies. With foundations in Bayesian Network theory and information theory, PSEMs use the principle of Kulback-Leibler divergence to rank order the relative importance of factors that explain structural drivers measured as latent variables and dynamics of support for climate policies among different segments of populations. The PSEM is derived from publicly available mixed-pool โ€œClimate Change in the American Mindโ€ (CCAM) dataset collected between 2008 and 2018 (N=22,416). The estimated PSEM predicts that 27.38% of the US population strongly supported climate policy action, while 59.46% are lukewarm supporters and 13.15% strongly oppose climate policy interventions. Predicted posterior probabilities of opposers, lukewarm supporters and strong supporters of climate policy action conditional upon beliefs, concern, global warming risk perceptions, ideology and other predictors in the network can be estimated from the PSEM. Consistent with theoretical expectations, we find that the strong supporters of climate policy are more likely to be strong believers, highly concerned, alarmed and tend to be very liberal or somewhat liberal. In contrast, strong opposers of climate policy are more likely to be climate deniers, skeptics or doubtful, not concerned, risk deniers and very conservative or somewhat conservative. The conditional probability distributions of lukewarm policy supporters (the largest group among the US population at 59.46%) display probably the most novel and revealing findings of this PSEM: Lukewarm supporters are more likely to be ambivalent and moderate believers and less likely to be strong believers. Further, their likelihood of being not concerned about climate change is slightly higher compared with the population mean. The lukewarm policy supporters also contain fewer people who perceive high risk from climate change. Finally, from ideology standpoint, lukewarm supporters represent relatively larger segment of moderate/independents. Poor adoption of climate policy proposals in the US can be attributed to this silent majority of lukewarm supporters who perceive little to moderate risk from climate change and remain ambivalent about human-induced climate change.

Presentation Video

About the Presenter

Asim Zia, Ph.D. Professor of Public Policy & Computer Science: Department of Community Development and Applied Economics & Department of Computer Science at the University of Vermont Director: Institute for Environmental Diplomacy and Security Co-Director: Social Ecological Gaming and Simulation Lab Fellow: Gund Institute for Environment 146 University Place, Morrill 208E, Burlington VT 05405 USA Phone: +1 802-656-4695 Asim.Zia@uvm.edu https://www.uvm.edu/cals/cdae/profiles/asim_zia http://www.uvm.edu/~azia/

Asim Zia has made substantive scientific and policy contributions towards advancing the Sustainability and Resilience of Human Environmental Systems. He is an internationally known leader in developing computational models of Social Ecological Systems, Complex Adaptive Systems and Governance Networks. He has published 58 journal articles, 19 book chapters and 3 books, totaling 80 peer-reviewed publications. He has served as a Principal Investigator, Co-Principal Investigator, or Senior Personnel on 22 research grants worth more than $60 Million sponsored by NSF, USDA, US DoD, US DoT and MacArthur Foundation. He has a Ph.D. in Public Policy from the Georgia Institute of Technology.

Previous Presentations

Machine learning how human risk perceptions shape behavior (Nashville, 2016)

Last updated

Logo

Bayesia USA

info@bayesia.us

Bayesia S.A.S.

info@bayesia.com

Bayesia Singapore

info@bayesia.com.sg

Copyright ยฉ 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.