Abstract
As the international community continues to fall short on reducing emissions to avoid disastrous impacts of climate change, some scientists have called for more research into solar geoengineering (SGE) as a potential temporary fix. Others, however, have adamantly rejected the notion of considering SGE in climate policy discussions. One prominent concern with considering SGE technologies to help manage climate change is the so-called “free driver” conjecture. The prediction is that among countries with different preferences for the level of SGE, the country that prefers the most will deploy levels higher than the global optimum. This paper tests the free-driver hypothesis experimentally under different conditions and institutions. We find that aggregate deployment of SGE is inefficiently high in all settings, but slightly less so when players are heterogeneous in endowments or when aggregate deployment is determined by a best-shot technology. Despite persistent inefficiencies in SGE deployment, free-driver behavior, on average, is less extreme than the theoretical predictions.
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Notes
In a follow-up paper to Abatayo et al., Ghidoni et al. (2023) test in a similar set-up whether side-payments can decrease the extent of free driving behavior.
Barrett (2007, p.38) points out that “geoengineering essentially constitutes a large project, a single best effort”.
As is standard practice in economic experiments, the instructions use neutral language and avoid terms like solar geoengineering, climate change and public goods.
Given SGE is relatively inexpensive, we parameterize endowment heterogeneity to focus on income effects rather than capacity effects. However, introducing counter-engineering creates a conflict in which endowment heterogeneity entails capacity effects.
We report p-values from conditional t-tests from least squares estimates that take advantage of the panel nature of the data to control for round effects, while also accounting for observational dependence with robust standard errors clustered at the session level.
This unproductive production is akin to investments in SGE capacity, costly negotiations, lobbying, etc. Future studies may consider experimental designs that rebate unproductive production.
We thank an anonymous reviewer for pointing out the differences in best responses between the summation and best shot treatments and suggesting a “trembling hand” argument as potential explanation of the observed difference between summation and best shot. Note that in a summation treatment with more “non-C players”, strategic uncertainty and therefore the expectations of trembling hands increase even more in the summation treatment and therefore we might expect the free-driver’s production to decline even more relative to its average production given a best-shot technology.
Note that Player C’s average production in Treatment 3, 9.11, is not equal, contrary to what theory would predict, to Net SGE, 9.48, because there were 39 cases, mainly in the first half of the experiment, where Player C’s production was actually not the highest one and therefore did not provide the best shot.
Actual surplus range: 24.6%-50.1%. Equilibrium surplus range: 59.2%-67.7%.
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Acknowledgements
We thank Juan Moreno-Cruz, Mark Borsuk, Tyler Felgenhauer, Khara Grieger, Billy Pizer and Jonathan Wiener for valuable comments.
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This material is based upon work supported by the National Science Foundation under Grant No. 2033855.
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Cherry, T.L., Kroll, S., McEvoy, D.M. et al. Solar Geoengineering, Free-Driving and Conflict: An Experimental Investigation. Environ Resource Econ 87, 1045–1060 (2024). https://doi.org/10.1007/s10640-024-00854-1
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DOI: https://doi.org/10.1007/s10640-024-00854-1