Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes

Abstract

In this paper, we seek to identify robust predictors of individuals’ attitudes towards climate change and environmental degradation. While much of the extant literature has been devoted to the individual explanatory potential of individuals’ characteristics, we focus on the extent to which these characteristics provide robust predictions of climate and environmental attitudes. Thereby, we adjudicate the relative predictive power of psychological and sociodemographic characteristics, as well as the predictive power of combinations of these attributes. To do so, we use a popular machine learning technique, Random Forests, on three surveys fielded in China, Switzerland, and the USA, using a variety of outcome variables. We find that a psychological construct, the consideration of future consequences (CFC) scale, performs well in predicting attitudes, across all contexts and better than traditional explanations of climate attitudes, such as income and education. Given recent advances suggesting potential psychological barriers of behavioural change Public (Weaver, Adm Rev 75:806–816, 2015) and the use of psychological constructs to target persuasive messages (Abrahamse et al., J Environ Psychol 265–276, 2007; Hirsh et al., Psychol Sci 23:578–581, 2012), identifying important predictors, such as the CFC may allow to better understand public’s appetite for climate and environmental policies and increase demand for these policies, in an area where existing efforts have shown to be lacking (Bernauer and McGrath, Nat Clim Chang 6:680–683, 2016; Chapman et al., Nat Clim Chang 7:850–852, 2017).

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Notes

  1. 1.

    For a notable exception see Lee et al. (2015).

  2. 2.

    Full item wording is located in the supplementary information (see Section SI.1).

  3. 3.

    The properties of these sample are summarised in the supplementary information (see Section SI.2). For a comparison of sample and population, see Section SI.3 in the supplementary information.

  4. 4.

    For full description of these variables see Section SI.1. Due to the political landscape in China, we could neither ask party identification nor political ideology.

  5. 5.

    Our test and train data are constructed by randomly splitting our data in half.

  6. 6.

    In a bivariate regression, the R2 is equal to Pearson’s r, squared.

  7. 7.

    One can think of these being the “settings” of the model.

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Acknowledgments

We are grateful to the comments and suggestions by two anonymous reviewers and the editors that have greatly improved this manuscript. We would like to thank Thomas Bernauer for his contribution to the data collection in the China, Switzerland, and the US. We also thank Brilé Anderson for her contribution to the data collection in Switzerland. We would also like to thank Dennis Atzenhofer and Michael Hudecheck for their research assistance and the Strassenverkehrsamt Zürich for the cooperation throughout the data collection in Zurich.

Funding

The research for this article was funded by the ERC Advanced Grant ‘Sources of Legitimacy in Global Environmental Governance’ (Grant: 295456) and supported by ETH Zürich.

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Correspondence to Liam F. Beiser-McGrath.

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Beiser-McGrath, L.F., Huber, R.A. Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes. Climatic Change 149, 335–347 (2018). https://doi.org/10.1007/s10584-018-2260-9

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