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Quasi-experimental estimates of the transient climate response using observational data

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  • Giselle Montamat

    (Harvard University)

  • James H. Stock

    (Harvard University and the National Bureau of Economic Research)

Abstract

The transient climate response (TCR) is the change in global mean temperature at the time of an exogenous doubling in atmospheric CO2 concentration increasing at a rate of 1% per year. A problem with estimating the TCR using observational data is that observed CO2 concentrations depend in turn on temperature. Therefore, the observed concentration data are endogenous, potentially leading to simultaneous causation bias of regression estimates of the TCR. We address this problem by employing instrumental variables regression, which uses changes in radiative forcing external to earth systems to provide quasi-experiments that can be used to estimate the TCR. Because the modern instrumental record is short, we focus on decadal fluctuations (up to 30-year changes), which also mitigate some statistical issues associated with highly persistent temperature and concentration data. Our estimates of the TCR for these shorter horizons, normalized to be comparable to the traditional 70-year TCR, fall within the range in the IPCC-AR5 and provide new observational confirmation of model-based estimates.

Suggested Citation

  • Giselle Montamat & James H. Stock, 2020. "Quasi-experimental estimates of the transient climate response using observational data," Climatic Change, Springer, vol. 160(3), pages 361-371, June.
    • Handle: RePEc:spr:climat:v:160:y:2020:i:3:d:10.1007_s10584-019-02589-1
    DOI: 10.1007/s10584-019-02589-1
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    References listed on IDEAS

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    Cited by:

    1. Simon Dietz & Frederick van der Ploeg & Armon Rezai & Frank Venmans, 2021. "Are Economists Getting Climate Dynamics Right and Does It Matter?," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 8(5), pages 895-921.
    2. Liang Chen & Juan J. Dolado & Jesús Gonzalo & Andrey Ramos, 2023. "Heterogeneous predictive association of CO2 with global warming," Economica, London School of Economics and Political Science, vol. 90(360), pages 1397-1421, October.
    3. Yang, Tianle & Li, Fangmin & Du, Min & Huang, Miao & Li, Yinuo, 2023. "Impacts of alternative energy production innovation on reducing CO2 emissions: Evidence from China," Energy, Elsevier, vol. 268(C).
    4. Chen, Liang & Ramos Ramirez, Andrey David, 2013. "Revisiting Granger Causality of CO2 on Global Warming: a Quantile Factor Approach," DES - Working Papers. Statistics and Econometrics. WS 35531, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Philippe Goulet Coulombe & Maximilian Gobel, 2021. "On Spurious Causality, CO2, and Global Temperature," Papers 2103.10605, arXiv.org.
    6. Tobias Adrian & Nina Boyarchenko & Domenico Giannone & Ananthakrishnan Prasad & Dulani Seneviratne & Yanzhe Xiao, 2022. "800,000 Years of Climate Risk," Staff Reports 1031, Federal Reserve Bank of New York.
    7. Pretis, Felix, 2021. "Exogeneity in climate econometrics," Energy Economics, Elsevier, vol. 96(C).

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