Abstract
We develop a financial-economic model for carbon pricing with an explicit representation of decision making under risk and uncertainty that is consistent with the Intergovernmental Panel on Climate Change’s sixth assessment report. We show that risk associated with high damages in the long term leads to stringent mitigation of carbon dioxide emissions in the near term, and find that this approach provides economic support for stringent warming targets across a variety of specifications. Our results provide insight into how a systematic incorporation of climate-related risk influences optimal emissions abatement pathways.
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Data Availability
The code for the Carbon Asset Pricing model – AR6 (CAP6) can be found at the following Github repository: github.com/adam-bauer-34/cap6.
Notes
This SCC estimate represents a significant increase from the U.S. Interagency Working Group’s central estimate of \(\sim \)$50 (Committee on Assessing Approaches to Updating the Social Cost of Carbon et al. 2017) and is in line with the U.S. Environmental Protection Agency’s recent draft estimates that report a central value of $190 (National Center for Energy Economics 2022).
We provide a more thorough literature review in Online Appendix A.
Nielsen-Gammon and Behl (2021) highlight the need and urgency for standardized, state-of-the-art climate and economic components based on the most up-to-date research for climate-economic modeling.
While one may question the coarseness of our time discretization, it has been shown that including more decision periods in similar models does not significantly affect their output (Coleman et al. 2021).
Note the US EPA uses the so-called RFF-SPs (Rennert et al. 2022) rather than the SSPs used here.
This assumption only impacts SSP1–1.9, as SSP1–1.9 makes more optimistic assumptions around backstop technology than we do in our cost formulation.
We present CAP6 output using only one of each damage function, and compare it to when each damage function is sampled in Online Appendix H.
Note the technological growth factor is offset by ten years as the cost data from AR6 is for 2030 technologies and our first model period is in 2020.
The analogous figure to Fig. 4 for the “no free lunches” MACC is provided in Online Appendix E. We also performed a second recalibration that sets the costs of the the <$0 mitigation options to infinity, coined the “infinite cost” calibration. The figure associated with this calibration is also in Online Appendix E. We do not show the results of CAP6 with this calibration as the final costs of abatement are lower than in the “no free lunches” case, but higher than the ‘main specification.’ Hence, the results will simply be an interpolation between the main specification and the “no free lunches” results.
Dvorak et al. (2022) showed that the TCRE adequately emulates the response of the more comprehensive FaIR model (Smith et al. 2018), itself a combination of carbon cycle models (Joos et al. 2013) and physical response models (Geoffroy et al. 2013b, a). The TCRE can deviate from more sophisticated models slightly depending on the forcing scenario (Intergovernmental Panel on Climate Change 2021), but the differences are minor and are therefore ignored in this study.
We do not here take a stand on which discount rate is correct, but do consider the 2% rate as our benchmark, as it is the central rate used by the EPA.
We demonstrate how including endogenous technological growth influences model output in Online Appendix K.
We refer to this set of variables as “impact variables” for the remainder of this discussion.
This conclusion relies on a high cost of net-negative emissions; if a breakthrough in direct air capture (DAC) technologies occurs, then we would expect the variance explained in impact variables owing to technological growth to be higher, as net-negative emissions would enable long-run temperatures, CO\(_2\) concentrations, and economic losses to be changed, perhaps significantly so, depending on how expensive DAC turns out to be.
An important qualification to this conclusion is that we do not consider solar geoengineering, which could lead to increased spending influencing temperature, CO\(_2\) concentration, and economic damages levels in both the short- and long-term.
This is not to say that RA has no impact on price levels, as increasing (decreasing, resp.) RA does slightly raise (lower, resp.) near term prices, see Online Appendix J.
Another limitation is that we compute the optimal carbon tax with a single exogenous discount rate. In reality, the discount rate will respond to the level of risk (Lucas 1976) and is uncertain on long time horizons (Weitzman 1998). Allowing for a dynamic discount rate in our framework is a potentially fruitful avenue of future work.
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Acknowledgements
The authors thank Simon Dietz, Romain Fillon, Bob Kopp, Geoffrey Heal, Glenn Hubbard, Bob Litterman, Bruce McCarl, James Rising, Chris Smith, Thomas Stoerk, Andrew Wilson, three anonymous reviewers, and seminar audiences at Columbia University, the Center for Social and Environmental Futures at the University of Colorado Boulder, the 2022 fall meeting of the American Geophysical Union, and the 2023 summer conferences of the Association of Environmental and Resource Economists and the European Association of Environmental and Resource Economists for providing useful feedback on this work. The authors thank Alaa Al Khourdajie for providing the data from AR6 WGIII’s cost of mitigation figure, W. Matthew Alampay Davis and Steve Rose for helpful discussions of climate damage functions, David C. Lafferty for his contributions to Fig. 3, and Jaydeep Pillai for testing the public release version of the code. AMB thanks Columbia Business School for their hospitality while this work was being completed, and acknowledges support from the Gies College of Business Office of Risk Management and Insurance at the University of Illinois Urbana-Champaign, Columbia Business School, and a National Science Foundation Graduate Research Fellowship grant No. DGE 21-46756. CP was supported by the Gies College of Business Office of Risk Management and Insurance Research at the University of Illinois Urbana-Champaign. Computations were performed on the Keeling computing cluster, a computing resource operated by the School of Earth, Society and the Environment (SESE) at the University of Illinois Urbana-Champaign.
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Cristian Proistosescu and Gernot Wagner conceived of the study. Adam Michael Bauer wrote the code, designed numerical experiments, performed literature review, and made the figures. The first draft of the paper was written by Adam Michael Bauer, and all authors assisted in editing this draft to shape the final submitted manuscript. All authors have approved the submitted verison.
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Adam Michael Bauer I hold a short-term consultancy position at the World Bank’s Climate Change Group unrelated to this work. I have no other conflicts of interest to disclose. Cristian Proistosescu I have no conflicts of interest to disclose. Gernot Wagner I am on the advisory board of CarbonPlan.org. I have no other conflicts of interest to disclose.
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Bauer, A.M., Proistosescu, C. & Wagner, G. Carbon Dioxide as a Risky Asset. Climatic Change 177, 72 (2024). https://doi.org/10.1007/s10584-024-03724-3
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DOI: https://doi.org/10.1007/s10584-024-03724-3