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. 2021 Aug 24;118(34):e2103081118.
doi: 10.1073/pnas.2103081118.

Economic impacts of tipping points in the climate system

Simon Dietz  1   2 , James Rising  3 , Thomas Stoerk  2 , Gernot Wagner  4
Affiliations

Economic impacts of tipping points in the climate system

Simon Dietz et al. Proc Natl Acad Sci U S A. .

Abstract

Climate scientists have long emphasized the importance of climate tipping points like thawing permafrost, ice sheet disintegration, and changes in atmospheric circulation. Yet, save for a few fragmented studies, climate economics has either ignored them or represented them in highly stylized ways. We provide unified estimates of the economic impacts of all eight climate tipping points covered in the economic literature so far using a meta-analytic integrated assessment model (IAM) with a modular structure. The model includes national-level climate damages from rising temperatures and sea levels for 180 countries, calibrated on detailed econometric evidence and simulation modeling. Collectively, climate tipping points increase the social cost of carbon (SCC) by ∼25% in our main specification. The distribution is positively skewed, however. We estimate an ∼10% chance of climate tipping points more than doubling the SCC. Accordingly, climate tipping points increase global economic risk. A spatial analysis shows that they increase economic losses almost everywhere. The tipping points with the largest effects are dissociation of ocean methane hydrates and thawing permafrost. Most of our numbers are probable underestimates, given that some tipping points, tipping point interactions, and impact channels have not been covered in the literature so far; however, our method of structural meta-analysis means that future modeling of climate tipping points can be integrated with relative ease, and we present a reduced-form tipping points damage function that could be incorporated in other IAMs.

Keywords: climate risk; climate tipping points; integrated assessment model; social cost of carbon.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
The percentage change in the SCC due to tipping points collectively and individually. Boxes show medians and interquartile ranges, whiskers show 95% CIs, crosses mark the average changes (0.1% trimmed), triangles mark the 0.5 percentiles, and squares mark the 99.5 percentiles. The y axis is truncated. Specification comprises RCP4.5-SSP2 emissions and GDP/population growth, Hope and Schaefer PCF, Whiteman et al. beta OMH, and IPSL AMOC hosing. Monte Carlo sample size is 10,000.
Fig. 2.
Fig. 2.
Infographic summarizing uncertainty about the percentage change in the SCC due to tipping points and the sources of that uncertainty. Histogram shows the full distribution of percentage changes in the SCC from a pooled Monte Carlo sample of size 32,000 (SI Appendix has further details). Percentage changes reported in the boxes are expected values for one factor at a time variations on the following specification: RCP4.5-SSP2 emissions and GDP/population growth, Hope and Schaefer PCF, Whiteman et al. beta OMH, and IPSL AMOC hosing. Note that the result for the OMH scenario includes all eight tipping points on.
Fig. 3.
Fig. 3.
Histograms showing the distribution of world mean consumption per capita in 2050 (Upper) and 2100 (Lower) both without tipping points (blue) and with tipping points (red). Main specification comprises RCP4.5-SSP2 emissions and GDP/population growth, Hope and Schaefer PCF, Whiteman et al. beta OMH, and IPSL AMOC hosing. Monte Carlo sample size is 10,000. Values reported are in 2020 US dollars. TP, tipping point; USD, US dollars.
Fig. 4.
Fig. 4.
Country-level expected SCC estimates (2020 US dollars) without tipping points (Upper) and the percentage change in the expected country-level SCC due to all tipping points (Lower). Welfare changes are normalized to global mean consumption per capita. Specification comprises RCP4.5-SSP2 emissions and GDP/population growth, Hope and Schaefer PCF, Whiteman et al. beta OMH, and IPSL AMOC hosing. Monte Carlo sample size is 10,000 with 0.1% trimmed.
Fig. 5.
Fig. 5.
Scatterplots of additional warming (Top), SLR (Middle), and damages (Bottom) from all tipping points combined. Data are sampled on a decadal interval (2020, 2030,...2200) using 1,000 Monte Carlo simulations under each of the RCP4.5 and RCP8.5 emissions scenarios. A nonlinear fit is used for temperature and damages; a linear fit is used for SLR. Incremental damage from tipping points is expressed as the change in world consumption per capita due to tipping points, relative to world consumption per capita without climate damages. Specification comprises Hope and Schaefer PCF, Whiteman et al. beta OMH, and IPSL AMOC hosing. GMST, global mean surface temperature.
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References

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