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Environmental art, prior knowledge about climate change, and carbon offsets

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Abstract

Using a contingent choice survey of US citizens, we investigate the influence of environmental art on individual willingness to purchase voluntary carbon offsets. In a split-sample experiment, we compare the stated preferences of survey respondents in two different treatment groups to the preferences of a control group. One treatment group is shown photographs that illustrate the impacts of climate change; the other is shown animated images that illustrate wind speeds and patterns for extreme weather events. While individuals seeing the photographs show a higher willingness to purchase voluntary offset than the control group, respondents seeing the animated images seem less willing to buy offsets. This result remains stable when accounting for preference heterogeneity related to prior knowledge about climate change issues. We hypothesize that the differential impacts of the two kinds of artistic images are due to a combination of factors influencing individual choices: emotional effect, cognitive interest, and preferences for the prevention of specific climate change impacts, as well as, more generally, internalized and social norms for the mitigation of climate change.

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Notes

  1. These labels correspond to those used by the Canary Project and also related directly to information given to survey respondents about the likely impacts of global climate change.

  2. On the Web site for the Wind Map project, historical images are labeled with both the relevant dates and, for dates on which major events occurred, the names of the events. The animations shown to survey respondents, however, showed only the relevant dates.

  3. Although we collected data on how long respondents viewed the images (as well as other components of the survey), Brookmark Research Services reported doubts about the validity of this information since some respondents pause in the middle of taking the survey and then return to complete it later. Presumably, however, this affects mostly the upper end of the distribution. The median time respondents spent on the images section of the survey was a little under one minute for the Canary Project photos and almost two minutes for the Wind Map Project images.

  4. The cheap talk script included the following text: “Before going through the eight decision situations, please consider the following important instructions! Consider the situations to be real purchase situations: Choose your answers as if you really had to pay the respective amounts and take your monthly income into account when making the decisions.” There is empirical evidence that a cheap talk script can reduce the hypothetical bias in choice experiments, see e.g., Cummings and Taylor (1999) or Carlsson et al. (2005).

  5. The median time respondents took for this section of the survey was around three minutes, but see footnote 3.

  6. All scenarios in the present study represent hypothetical situations: respondents were not asked to actually purchase offsets. Blasch and Farsi (2014) find, however, a high congruency between stated preference responses and actual purchase behavior by the respondents to their survey.

  7. The percentage in the control group (28 %) was slightly higher than in the two treatment groups (24 % for the Canary Project group, 23 % for the Wind Map Project group). The corresponding percentage in Blasch and Farsi (2014) is 15 %.

  8. The median age interval is 36–45; the median income interval is $35,000–49,999 per year. Both are comparable to statistics for the US population.

  9. These differences might be treatment effects, since questions about individual responsibility and family and friends’ expectations were asked after the treatment groups saw images. This would imply that differences in the estimated coefficients on the status quo dummy variable would not correctly measure the total effects of treatment. But, our main measure of treatment effects, the difference in average derivatives with respect to the status quo (see later discussion and tables) incorporates these differences as well as differences in the estimated coefficients on the status quo dummy variable.

  10. This is not literally a derivative since the explanatory variable in question is a dummy variable. So, the “derivative” is calculated as the difference between the predicted values of the probability when the dummy variable equals one and when it equals zero.

  11. The main conclusions were also unchanged when a mixed logit model was used instead of a latent class analysis or when offset prices were used as variables rather than the total costs of purchasing offsets.

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Acknowledgments

This research was undertaken in response to an invitation from Dehlia Hannah to participate in a conference, Mapping the Climatic Imaginary through Art, Science and History, held in November 2013 at the Center for Contemporary History and Policy, Chemical Heritage Foundation, Philadelphia, PA. The authors would like to thank Dehlia and the other participants in that conference for useful comments. Edward Morris of the Canary Project was a conference participant and allowed us to use their photographs. Fernanda Viégas and Martin Wattenberg allowed us to use images from their Wind Map Project, which was featured as part of the art exhibition that accompanied the conference. The authors also received particularly helpful comments from Markus Ohndorf, Takao Kato, April Baptiste, Julia Martinez, and two anonymous reviewers. Any remaining errors are, of course, the responsibility of the authors.

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Authors and Affiliations

  1. Department of Humanities, Social and Political Sciences, ETH Zurich, Clausiusstrasse 37, 8092, Zurich, Switzerland

    Julia Blasch

  2. Department of Economics, Colgate University, 13 Oak Drive, Hamilton, NY, 13346, USA

    Robert W. Turner

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  1. Julia Blasch
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  2. Robert W. Turner
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Correspondence to Robert W. Turner.

Appendix

Appendix

Table 5 Scales and items used in the analysis
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Blasch, J., Turner, R.W. Environmental art, prior knowledge about climate change, and carbon offsets. J Environ Stud Sci 6, 691–705 (2016). https://doi.org/10.1007/s13412-015-0243-y

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