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Can Defaults Save the Climate? Evidence from a Field Experiment on Carbon Offsetting Programs

Environmental and Resource Economics volume 54, pages 613–626 (2013)Cite this article

Abstract

Individual preferences for environmental policies can be influenced by the frame in which choices and decisions are presented. In this paper we present results of a field experiment on the contributions to carbon offsetting programs under two alternative treatments for the default option. The opt-in treatment asked subjects to pay for the policy proposal while the opt-out treatment asked subjects if they wanted to be excluded from payment for the policy proposal. The results show that the frame of the default option had a significant effect on the amount of money paid for the policy proposal. Subjects were more likely to accept the policy proposal if the default option was the opt-out treatment. The results have implications for the design of environmental policies.

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Notes

  1. In practice, the efforts of companies to offer customers the option of paying to offset environmental costs do not have the same success in real markets (BBC 2007). For instance, Gössling and Schumacher (2010) found that only 5 % respondents claimed to have offset their flight (Gössling et al. 2007). Thus, hypothetical stated preference surveys may not approximate real market results, leading to “hypothetical bias” or “intentions versus actions bias”.

  2. For a review of previous applications of field experiments and its properties to implement robust empirical tests outside the lab see Harrison and List (2004). This methodology collects data in the least obtrusive way while still maintaining the necessary control to execute a clean comparison between samples (List 2003). This highlights the naturalness and realism of human decision-making.

  3. There are a number of companies offering consumers the chance to contribute through voluntary carbon offsetting payments. Gössling et al. (2007) found out that there are relevant differences in the approaches that these organizations use in the calculations of emissions, compensation measures, prices, company structures and evaluation processes. The authors conclude that atmosfair “seems to use a scientifically sound and holistic approach to compensation”.

  4. A Referee to this paper raised concern about the unspecified amount of \(\text{ CO}_{2}\) impacts in the valuation scenario posed to potential travellers. In focus groups, we tried various scenarios in which subjects were presented with the actual amounts of \(\text{ CO}_{2}\) emissions caused by their air flight trip. However, we found that this complication was unnecessary since subjects were not concerned with the actual amount emitted by their flight but with a policy to offset the amount actually emitted. That is, subjects were found to be insensitive to the amount of damage caused by their own flight. On the other hand, as we referred earlier the amount of \(\text{ CO}_{2}\) emissions varies according to the calculations made by different organizations and institutions. In addition, we also found that there was not great variability in emissions for potential visitors coming by air from Europe to the Canary Islands.

  5. Note that if the subject decides to buy the offset option, the disutility for generating CO2 and the cost of generating the beliefs about the right amount of consumption disappear. That is, \(\text{ D}(\text{ X}-\text{ Y})=\text{ C}(\text{ Y})=0\)

  6. Some previous examples of how misleading can be standard parametric approaches in estimating welfare analysis using stated preference data are Kriström (1990), Araña and León (2005), (2006); Araña and León (2007) and Watanabe (2011), for single bounded dichotomous choice data. For examples in the mainstream statistics literature see Yatchew and Griliches (1984) or Horowitz (1993).

  7. For a very intuitive and non-technical description of the methodology see Carson et al. (2004, appendix F, section 2, pp. 171–176). For a formal mathematical derivation of the mean and variance WTP estimator see Hanemann and Kanninen (1999) or Carson et al. (2004, app. F, section 4, pp. 178–183).

  8. The lower bound mean WTP can be estimated by multiplying the lower bound of each interval by the fraction of sample estimated to lie in each interval and summing the resulting column of numbers. For instance, E(WTP_Opt-OUT) = (0*0.19) +  (10*0.17) + (20*0.19) + (40*0.07) + (60*0.38) = 31.29. In order to calculate the interval confidence for the lower bound mean WTP one needs to incorporate additional assumptions about the nature of the latent WTP distribution. Here we employ the conventional assumptions of normal distribution and calculate a 95 % bootstrap-based confidence interval using the percentile method (Effron and Tibshirani 1993).

  9. An alternative way to include covariates and maintaining a Turnbull-type approach is to impose a parametric structure to the link function that relates the dependent variable and its predictors. Some early examples in labor economics are Heckman and Singer (1984) and Araña and León (2005) in the context of contingent valuation. A competitive alternative is to adopt a Bayesian non-parametric approach, which uses a flexible mixture of distributions as a link function. This approach allows researchers to maintain the flexibility of a non-parametric model, incorporating covariates and accounting for preferences heterogeneity at the same time. Some recent examples are Geweke (forthcoming) in the context of choice experiments and León and Araña (2012) in the context of environmental valuation. Following reviewers’ recommendation and since the nature of the field experiment did not allow us to have a large list of potential covariates and main results were not sensitive to the econometric approach adopted, a simple Turnbull and probit analysis are presented for this specific application.

  10. This result can be interpreted as supporting the idea of arbitrary anchoring effects (e.g. Ariely et al. 2003; Bateman et al. 2008).

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Acknowledgments

The authors greatly acknowledge financial support by the projects VEM2004-08558 and ECO2009-12629 of the Spanish Ministry of Education and project 200801000381 of the Agencia Canaria de Investigación (ACIISI). Useful comments and discussion with W. Michael Hanemann, Jordan Louviere, Bart Frischknecht helped to improve the piece. This paper was finished while Carmelo León was visiting the Center for the Study of Choice (CenSoc) at University of Technology of Sydney. Acknowledgment for access to an excellent and inspiring working environment is also recognized. Only the authors are responsible for the opinions expressed and potential errors in the content.

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Authors and Affiliations

  1. Institute of Tourism and Sustainable Development Economics (TIDES), Universidad de Las Palmas de Gran Canaria, Edificio de Economicas, Modulo D.3.13, Las Palmas de Gran Canaria, 35017,  Las Palmas, Spain

    Jorge E. Araña & Carmelo J. León

  2. Center for the Study of Choice (CenSoc), University of Technology, Sydney, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Jorge E. Araña

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  1. Jorge E. Araña
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Correspondence to Jorge E. Araña.

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Araña, J.E., León, C.J. Can Defaults Save the Climate? Evidence from a Field Experiment on Carbon Offsetting Programs. Environ Resource Econ 54, 613–626 (2013). https://doi.org/10.1007/s10640-012-9615-x

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