Offsetting Versus Mitigation Activities to Reduce \(\hbox {CO}_{2}\) Emissions: A Theoretical and Empirical Analysis for the U.S. and Germany

Abstract

This paper studies the voluntary provision of public goods that is partially driven by a desire to offset for individual polluting activities. We first extend existing theory and show that offsets allow a reduction in effective environmental pollution levels while not necessarily extending the consumption of a polluting good. We further discuss the impact of an increased environmental preference on purchases of offsets and mitigation activities. Several theoretical results are then econometrically tested using a novel dataset on activities to reduce \(\hbox {CO}_{2}\) emissions for the case of vehicle purchases in the U.S. and Germany. We show that environmental preference triggers the stated use of \(\hbox {CO}_{2}\) offsetting and mitigation channels in both countries. However, we find strong country differences for the stated purchase of \(\hbox {CO}_{2}\) offsets. While such activities are mainly triggered by a high general awareness of the climate change problem in the U.S., the perception that road travel is responsible for \(\hbox {CO}_{2}\) emissions to a large extent is more important for driver’s license holders in Germany.

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Notes

  1. 1.

    For example, the difference may stem from a limited general acceptance of offsets in society.

  2. 2.

    Corresponding notation will be used for the second derivatives (e.g. \(U_{xx} =\partial ^{2}U/\partial x^{2}\) and \(U_{xz} =\partial ^{2}U/\partial x\partial z)\).

  3. 3.

    Note that the normality assumption on \(U(z,x,-r)\) does not imply normality of \(V(z,x)=U(z,x,-x)\) in (zx). The individual desires to spend the additional income on both increased consumption of both x and \(-r\). As they are linked through \(x=r\), however, not both can increase such that the effect of additional income on both x and \(-r\) is ambiguous.

  4. 4.

    Different from Kotchen (2009), our model does not allow to easily define increased environmental concerns as a larger demand for \(-r\) as this demand may interact with the demand for the polluting. When no offsets are available, for example, an individual may consume less of the polluting good not for environmental concerns, but just because of a lesser preference for the private good.

  5. 5.

    While the interviews were conducted during the first phase of the global financial crisis, the survey time period is clearly before the peak crisis time, which began with the insolvency of Lehman Brothers in September 2008. Therefore, it is very unlikely that the responses were influenced by this specific time period. It should be mentioned that the survey time period is also clearly before the introduction of the U.S. Car Allowance Rebate System (“cash for clunkers”) in 2009, and a similar car-scrap bonus in in Germany in the same year. Both these programs affected the purchase of new vehicles and thus would possibly have influenced the responses to our questions.

  6. 6.

    While this complementary paper also analyzes the determinants of the purchase of \(\hbox {CO}_{2}\) offsets, it focuses on the effect of prior knowledge of this mechanism, but does not consider the relevance of the feeling of responsibility for \(\hbox {CO}_{2}\) emissions and is additionally not based on a theoretical analysis. In particular, the former paper does not examine mitigation activities when purchasing a new vehicle.

  7. 7.

    Response options in the underlying question were “very likely”, “somewhat likely”, “somewhat unlikely”, and “very unlikely”, which implies an ordinal structure of the variable. However, we do not focus on the corresponding ordinal dependent variable and thus ordinal probit models since we include this variable in a multivariate binary probit model as discussed below. Furthermore, Ziegler et al. (2012) show that the estimation results for the main explanatory variables are very similar by including ordinal or binary dependent variables. This finding is confirmed in this new analysis (the estimation results are not reported, but available upon request).

  8. 8.

    Response options were “not at all”, “purchase of a smaller vehicle”, “purchase of a vehicle with a smaller engine with less hp”, “purchase of a vehicle with different fuel or alternative drive systems”, “purchase of a vehicle with lower fuel consumption”, “purchase of a vehicle with low emissions”, “I am going to give up my vehicle entirely in future”, and “other”.

  9. 9.

    Examples for alternative fuels in vehicles are gas (e.g. natural gas, liquid petroleum gas), hydrogen, or biofuel, while examples for alternative driving systems or propulsion technologies are electric or hybrid. It can be argued that the purchase of vehicles with alternative drive systems as well as the purchase of less-emitting vehicles are not optimal indicators for the mitigation channel. Unfortunately, however, data about the intensity of using a vehicle are not available. Nevertheless, we think that our indicators are appropriate alternative indicators and especially assume that the rebound effect of these purchases on the use of the vehicles is less than 100 % so that finally they actually lead to a reduction of \(\hbox {CO}_{2}\) emissions.

  10. 10.

    In this respect, we used 50 random draws in the GHK simulator. Furthermore, we consider the robust estimations of the standard deviation of the parameter estimates (White 1982). The corresponding simulated maximum likelihood estimations (in the same way as all further estimations and also the descriptive statistics as discussed above) were conducted with STATA.

  11. 11.

    Response options were “the topic is extremely important to me”, “the topic is important to me”, “the topic is only of little importance to me”, and “the topic is of no importance to me”.

  12. 12.

    Response options were “thoroughly convinced”, “largely convinced”, “rather unconvinced”, and “not convinced at all”.

  13. 13.

    Response options were “a very large proportion”, “a large proportion”, “a small proportion”, and “no role”.

  14. 14.

    Unfortunately, data on individual income were not available.

  15. 15.

    The estimated correlation coefficients in the error terms of the underlying latent variables are not reported for brevity, but available upon request. They reveal significantly positive correlations between all three dependent variables in the U.S., which underpins the importance of applying multivariate instead of univariate probit models. In Germany, only the correlation coefficient between the two mitigation variables is significantly different from zero. In contrast to the result in the U.S., this correlation coefficient is negative.

  16. 16.

    This result can be shown when “only purchase of less-emitting vehicle” instead of “no purchase of \(\hbox {CO}_{2}\) offsets and no purchase of less-emitting vehicle” or “only purchase of vehicle with alternative drive systems” instead of “no purchase of \(\hbox {CO}_{2}\) offsets and no purchase of vehicle with alternative drive systems” are used as base categories in the multinomial logit models. The corresponding estimation results are not reported for brevity, but available upon request.

  17. 17.

    The corresponding estimation results are not reported for brevity, but available upon request.

  18. 18.

    Therefore, they are not reported for brevity, but available upon request.

  19. 19.

    These estimation results are not reported due to brevity, but available upon request.

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Lange, A., Ziegler, A. Offsetting Versus Mitigation Activities to Reduce \(\hbox {CO}_{2}\) Emissions: A Theoretical and Empirical Analysis for the U.S. and Germany. Environ Resource Econ 66, 113–133 (2017). https://doi.org/10.1007/s10640-015-9944-7

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Keywords

  • Public good
  • Voluntary provision
  • Climate change
  • \(\hbox {CO}_{2}\) offsetting
  • Vehicle purchase
  • Discrete choice models

JEL Classification

  • H41
  • Q54