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Preferences for Distributional Impacts of Climate Policy

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Abstract

What role do people think distributional aspects should play in the design of climate policy? The literature assessing climate policies has shown that assumptions regarding peoples’ distributional preferences for climate change policy impacts are central for policy assessment, but empirical evidence for such preferences is lacking. We design a discrete choice experiment that varies how climate policies affect the income of future generations living in three geographical regions, with distinctly different current and predicted future income levels. The experiment is implemented on a sample of the Danish population and preferences are modelled in a latent class model. Our results show that a small majority of the sample (60%) hold preferences consistent with inequity aversion with respect to future income effects of climate policies across regions. For the same group, we find that preferences for co-benefits for current generations reflect a form of altruism, but not inequity aversion. In both cases, the altruistic aspects are moderated by an element of preferences for positive outcomes in own region too. The remaining classes display preferences with a varying focus on impacts in their own region or simply no support for further climate policy. Our results provide some support for the inclusion of social preferences regarding distributional effects of climate change policies in policy assessments, and hence for the significant impact on policy, this inclusion will have.

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Notes

  1. http://unfccc.int/paris_agreement/items/9444.php.

  2. Other authors have studied what determines the public’s willingness to pay to mitigate climate change, such as the paper by Diederich and Goeschl (2014), but the authors do not focus on the explicit question of intergenerational equity and the choice of climate policy.

  3. Anthoff and Tol (2010) did not explicitly label their categorization as social preferences, but as different attitudes towards equity and justice. They introduced different concerns regarding the distributional impacts in other regions; which they called ‘sovereignty’, ‘altruism’, ‘good-neighbour’ and ‘compensation’.

  4. Examples could be cleaner or safer energy, resulting in improved health outcomes, or it may be changed land uses reducing erosion issues or biodiversity losses.

  5. We acknowledge that this is a narrow definition of the general income allocation problem that agents face. The general allocation of income between different goods could be handled in a two-stage budgeting model which can handle that agents have a range of different goods they wish to allocate their overall income on. However, as we wish to develop a model of preferences in relation to climate policy, we abstract from the general, underlying income allocation problem and focus directly on the income allocated to climate policy, thus assuming that the general allocation of income to different goods (among here climate policy) has already taken place.

  6. An inherent challenge is that overall income is expected to rise towards 2100 by a non-trivial amount. This means that people living now, across the three regions selected, are on average poorer than we predict people living in the same regions in 2100 to be. This could affect their choices even if they hold social preferences. In the survey, respondents were informed about this fact in the attribute-explanation section, and total per capita income for each region was displayed in each alternative in all choice sets.

  7. The members of the panel earn points when they answer a survey for the company. These points can be exchanged for gift certificates to a wide variety of non-food and food stores or used to enter lotteries and as donations to good causes. For a survey like ours, members typically earn 0.50 Euro.

  8. The survey included two splits, of which only one is used in this paper. Across both splits a total of 14,831 respondents were invited to the survey, of which 1634 had completed the survey. The survey was closed once a target population of representative respondents had replied. Thus a standard response rate cannot be estimated.

  9. Chi square tests indicate that the difference between age, income and educational levels of the sample and the general population is statistically significant. In Sect. 5.5 we comment on how these differences influence our ability to generalize our results, based on auxiliary analyses that investigate how age and educational level influence results.

  10. The different criteria do not suggest the same number as classes and furthermore, we see that the optimal number of classes, according to these criterions is rather high. This can result in classes of very small size and several insignificant parameters (Scarpa and Thiene 2005).

  11. All models and results are available upon request from the authors.

  12. Models are available from the authors upon request.

  13. However, other studies have found a reversed or no effect of the locational preference for receiving the benefits of mitigation in the participants own region (Baranzini et al. 2016; Diederich and Goeschl 2017).

  14. See Svenningsen (2019) for an empirical study using a real donation mechanism to investigate distributional social preferences for climate policy.

  15. See Fankhauser et al. (1997), Pearce (2003), Johansson-Stenman (2005), Anthoff et al. (2009) and Anthoff and Tol (2010).

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Acknowledgements

The authors are very grateful to two reviewers and the associate editor for helpful comments that improved the paper. We thank Frank Jensen for inspiring the theoretical framework and participants at EAERE 2017, Athens, for several good suggestions. Thorsen acknowledges the support of the Danish National Research Foundation (DNRF Grant 96) for Center for Macroecology, Evolution and Climate.

Funding

Funding was provided by Københavns Universitet.

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Authors and Affiliations

  1. Sustainability, Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark

    Lea S. Svenningsen

  2. Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark

    Bo Jellesmark Thorsen

  3. Center for Macroecology, Evolution and Climate Change, University of Copenhagen, Copenhagen, Denmark

    Bo Jellesmark Thorsen

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  1. Lea S. Svenningsen
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  2. Bo Jellesmark Thorsen
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Correspondence to Lea S. Svenningsen.

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Appendix

Appendix

Table 6 Performance of selection criterions for models with 2–10 classes, n = 813
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Table 7 Wald test of equality of regression coefficients
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Svenningsen, L.S., Thorsen, B.J. Preferences for Distributional Impacts of Climate Policy. Environ Resource Econ 75, 1–24 (2020). https://doi.org/10.1007/s10640-019-00386-z

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