Assessing U.S. public perceptions of global warming using social survey and climate data

Xiaoxiao Wei; Eve Bohnett; Li An

doi:10.1016/j.mex.2024.103081

Assessing U.S. public perceptions of global warming using social survey and climate data

MethodsX. 2024 Dec 6:14:103081. doi: 10.1016/j.mex.2024.103081. eCollection 2025 Jun.

Authors

Xiaoxiao Wei¹, Eve Bohnett^{2

3}, Li An^{1

2}

Affiliations

¹ College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, United States.
² International Center for Climate and Global Change Research, College of Forestry, Wildlife and Environment, Auburn University, 602 Duncan Drive, Auburn, AL 36849, United States.
³ Department of Landscape Architecture, University of Florida, Gainesville, FL 32611, United States.

Abstract

This paper presents a methodological approach for assessing the relationship between weather patterns, regional climate trends, and public perceptions of global warming in the United States with control of socioeconomic, political, and ideological variables. We combined social survey data from the Gallup Poll Social Series (GPSS) with environmental data from the National Oceanic and Atmospheric Administration (NOAA) and the PRISM Climate Group. Logistic regression models were employed, enhanced by Eigenvector Spatial Filtering (ESF) to address spatial autocorrelation. This approach allowed us to examine how both short-term weather conditions and long-term climate changes impact public concerns about global warming. Notably, the perception of warmer winters emerged as a critical factor influencing attitudes, highlighting the importance of perceived environmental changes in shaping public opinion.•We combined survey data on public perceptions with high-resolution weather and climate data.•We applied logistic regression models with Eigenvector Spatial Filtering to control for spatial autocorrelation.•Our analysis emphasized both physical climate measures and perceived climate changes.

Keywords: Eigenvector Spatial Filtering (ESF) with Logistic Regression; Gallup data; Geospatial modeling; Perception of global warming; Spatial autocorrelation; Weather and climate.