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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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.
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’.
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.
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.
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.
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.
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.
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.
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).
All models and results are available upon request from the authors.
Models are available from the authors upon request.
See Svenningsen (2019) for an empirical study using a real donation mechanism to investigate distributional social preferences for climate policy.
Alberini A, Krupnick A (2000) Cost-of-illness and willingness-to-pay estimates of the benefits of improved air quality: evidence from Taiwan. Land Econ 76(1):37–53
Alló M, Loureiro ML (2014) The role of social norms on preferences towards climate change policies: a meta-analysis. Energy Policy 73:563–574
Andreoni J (1990) Impure altruism and donations to public goods: a theory of warm-glow giving. Econ J 100(401):464–477
Andreoni J, Harbaugh WT, Vesterlund L (2008) Altruism in experiments. New Palgrave Dict Econ 1–8:134–138
Anthoff D, Tol RSJ (2010) On international equity weights and national decision making on climate change. J Environ Econ Manag 60(1):14–20
Anthoff D et al (2009) Equity weighting and the marginal damage costs of climate change. Ecol Econ 68(3):836–849
Baranzini A et al (2016) Carbon offsets out of the woods? The acceptability of domestic vs. international reforestation programmes. Grantham Research Institute on Climate Change and the Environment, London. Working paper No. 257
Bardsley N, Sugden R (2006) Human nature and sociality in economics. In: Kolm SC, Ythier JM (eds) Handbook of the economics of giving, altruism and reciprocity, vol 1. North-Holland, Amsterdam, pp 731–765
Berk R, Fovell R (1999) Public perceptions of climate change: a ‘willingness to pay’ assessment. Clim Change 41(3–4):413–446
Buntaine MT, Prather L (2017) Global problems, local solutions: preferences for domestic action over international transfers in global climate policy. University of California, Oakland
Burlando RM, Guala F (2005) Heterogeneous agents in public goods experiments. Exp Econ 8(1):35–54
Cai B et al (2010) Distributional preferences and the incidence of costs and benefits in climate change policy. Environ Resource Econ 46(4):429–458
Cappelen AW et al (2007) The pluralism of fairness ideals: an experimental approach. Am Econ Rev 97(3):818–827
Carlsson F et al (2012) Paying for mitigation: a multiple country study. Land Econ 88(2):326–340
Carson RT, Groves T (2007) Incentive and informational properties of preference questions. Environ Resource Econ 37(1):181–210
ChoiceMetrics (2012) Ngene 1.1.1 user manual & reference guide. Australia
Clément V et al (2015) Perceptions on equity and responsibility in coastal zone policies. Ecol Econ 119:284–291
Dannenberg A et al (2010) Do equity preferences matter for climate negotiators? An experimental investigation. Environ Resource Econ 47(1):91–109
Diederich J, Goeschl T (2014) Willingness to pay for voluntary climate action and its determinants: field-experimental evidence. Environ Resource Econ 57(3):405–429
Diederich J, Goeschl T (2017). Does mitigation begin at home? Discussion paper series no. 634, University of Heidelberg
Engelmann D, Strobel M (2004) Inequality aversion, efficiency, and maximin preferences in simple distribution experiments. Am Econ Rev 94(4):857–869
Fankhauser S et al (1997) The aggregation of climate change damages: a welfare theoretic approach. Environ Resource Econ 10(3):249–266
Fehr E, Schmidt KM (1999) A theory of fairness, competition, and cooperation. Q J Econ 114(3):817–868
Fehr E, Schmidt KM (2006) The economics of fairness, reciprocity and altruism—experimental evidence and new theories. In: Kolm SC, Ythier JM (eds) Handbook of the economics of giving, altruism and reciprocity, vol 1. North-Holland, Amsterdam, pp 615–684
Fischbacher U, Gachter S (2010) Social preferences, beliefs, and the dynamics of free riding in public goods experiments. Am Econ Rev 100(1):541–556
Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res Part B Methodol 37(8):681–698
Grubb M (1995) Seeking fair weather: ethics and the international debate on climate change. Int Aff 71(3):463–496
Hess S et al (2013) Accommodating underlying pro-environmental attitudes in a rail travel context: application of a latent variable latent class specification. Transp Res Part D Transp Environ 25:42–48
Ikeme J (2003) Equity, environmental justice and sustainability: incomplete approaches in climate change politics. Glob Environ Change 13(3):195–206
IPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change R. K. P. a. L. A. Meyer. IPCC, Geneva, Switzerland, 151 pp
Istamto T, Houthuijs D, Lebret E (2014) Willingness to pay to avoid health risks from road-traffic-related air pollution and noise across five countries. Sci Total Environ 497–498:420–429
Johansson-Stenman O (2005) Distributional weights in cost-benefit analysis: should we forget about them? Land Econ 81(3):337–352
Johnson E, Nemet GF (2010) WIllingness to pay for climate policy: a review of estimates. Working paper series, La Follette School
Konow J (2001) Fair and square: the four sides of distributive justice. J Econ Behav Organ 46(2):137–164
Kverndokk S et al (2014) The trade-off between intra- and intergenerational equity in climate policy. Eur Econ Rev 69:40–58
Lancaster KJ (1966) A new approach to consumer theory. J Polit Econ 74(2):132–157
Lange A et al (2007) On the importance of equity in international climate policy: an empirical analysis. Energy Econ 29(3):545–562
Layton DF, Brown G (2000) Heterogeneous preferences regarding global climate change. Rev Econ Stat 82(4):616–624
Longo A et al (2012) Willingness to pay for ancillary benefits of climate change mitigation. Environ Resource Econ 51(1):119–140
Loomis J (2011) What’s to know about hypotheical bias in stated preference valuation studies? J Econ Surv 25(2):363–370
MacKerron GJ et al (2009) Willingness to pay for carbon offset certification and co-benefits among (high-)flying young adults in the UK. Energy Policy 37(4):1372–1381
McFadden D (1973) Conditional logit analysis of qualitative choice behaviour. Academic Press, New York
Navrud S (2001) Valuing health impacts from air pollution in Europe. Environ Resource Econ 20(4):305–329
Pearce D (2003) The social cost of carbon and its policy implications. Oxf Rev Econ Policy 19(3):362–384
Rodríguez-Entrena M et al (2014) The role of ancillary benefits on the value of agricultural soils carbon sequestration programmes: evidence from a latent class approach to Andalusian olive groves. Ecol Econ 99:63–73
Scarpa R, Thiene M (2005) Destination choice models for rock climbing in the Northeastern Alps: a latent-class approach based on intensity of preferences. Land Econ 81(3):426–444
Svenningsen LS (2019) Social preferences for distributive outcomes of climate policy. Clim Change. https://doi.org/10.1007/s10584-019-02546-y
Torres AB et al (2015) ‘Yes-in-my-backyard’: spatial differences in the valuation of forest services and local co-benefits for carbon markets in México. Ecol Econ 109:130–141
Vermunt JK, Magidson J (2015) Technical guide for latent GOLD 5.1: basic, advanced, and syntax. Statistical Innovations Inc., Belmont
Viscusi WK, Zeckhauser R (2006) The perception and valuation of the risks of climate change: a rational and behavioural blend. Clim Change 77(1–2):151–177
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 was provided by Københavns Universitet.
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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
- Choice experiment
- Social preferences
- Inequity aversion
- Climate change impacts
- Latent class
- Social cost of carbon