Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

Vivek Srikrishnan; Yawen Guan; Richard S J Tol; Klaus Keller

doi:10.1007/s10584-021-03279-7

Probabilistic projections of baseline twenty-first century CO₂ emissions using a simple calibrated integrated assessment model

Clim Change. 2022;170(3-4):37. doi: 10.1007/s10584-021-03279-7. Epub 2022 Feb 24.

Authors

Vivek Srikrishnan¹, Yawen Guan², Richard S J Tol^{3

4

5}, Klaus Keller^{6

7

8}

Affiliations

¹ Department of Biological & Environmental Engineering, Cornell University, Ithaca, NY USA.
² Department of Statistics, University of Nebraska, Lincoln, NE USA.
³ Department of Economics, University of Sussex, Sussex, UK.
⁴ Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
⁵ Department of Spatial Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
⁶ Department of Geosciences, Pennsylvania State University, University Park, PA USA.
⁷ Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA USA.
⁸ Thayer School of Engineering, Dartmouth College, Hanover, NH USA.

Abstract

Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.

Supplementary information: The online version contains supplementary material available at 10.1007/s10584-021-03279-7.

Keywords: CO2 emissions; Integrated Assessment; Probabilistic Projections; Sensitivity Analysis.