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

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Article number37
Pages (from-to)1-20
Number of pages20
JournalClimatic Change
Volume170
DOIs
Publication statusPublished - 24 Feb 2022

Bibliographical note

Funding Information:
This work was partially supported by the National Science Foundation (NSF) through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 and the Penn State Center for Climate Risk Management. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding entities.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • CO emissions
  • Integrated Assessment
  • Probabilistic Projections
  • Sensitivity Analysis

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