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A multicointegration model of global climate change

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  • Bruns, Stephan B.
  • Csereklyei, Zsuzsanna
  • Stern, David I.

Abstract

We model the role of the ocean in climate change, using the concept of multicointegration. Surface temperature and radiative forcing cointegrate and the accumulated cointegration disequilibria represent the change in Earth system heat content, which is predominantly stored in the ocean. System heat content in turn cointegrates with surface temperature. Using a multicointegrating I(2) model, we find that the climate sensitivity is 2.8°C and the rate of adjustment to equilibrium is realistically slow. These results contrast strongly with those from I(1) cointegration models and are more consistent with global circulation models. We also estimate Earth system heat content as a latent variable for the full period, 1850-2014, and this predicted heat content cointegrates with available ocean heat content observations for 1940-2014.

Suggested Citation

  • Bruns, Stephan B. & Csereklyei, Zsuzsanna & Stern, David I., 2018. "A multicointegration model of global climate change," University of Göttingen Working Papers in Economics 336, University of Goettingen, Department of Economics.
    • Handle: RePEc:zbw:cegedp:336
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Abandoning a Paper
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2020-08-05 00:26:00
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      by noreply@blogger.com (David Stern) in Stochastic Trend on 2020-12-19 07:18:00
    3. Annual Review 2018
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    4. Annual Review 2019
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2019-12-25 00:24:00
    5. Annual Review 2020
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2020-12-19 07:18:00
    6. A Multicointegration Model of Global Climate Change
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2018-02-10 07:11:00

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    Cited by:

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    3. Pretis, Felix, 2020. "Econometric modelling of climate systems: The equivalence of energy balance models and cointegrated vector autoregressions," Journal of Econometrics, Elsevier, vol. 214(1), pages 256-273.
    4. Joseph Nyangon & John Byrne, 2023. "Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(1), January.
    5. Chen, Li & Gao, Jiti & Vahid, Farshid, 2022. "Global temperatures and greenhouse gases: A common features approach," Journal of Econometrics, Elsevier, vol. 230(2), pages 240-254.
    6. Eric Hillebrand & Søren Johansen & Torben Schmith, 2020. "Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature," Econometrics, MDPI, vol. 8(4), pages 1-19, November.
    7. Phillips, Peter C.B. & Kheifets, Igor L., 2024. "High-dimensional IV cointegration estimation and inference," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Hildegart Ahumada & Magdalena Cornejo, 2020. "The effect of Amazon deforestation on global climate variables," Asociación Argentina de Economía Política: Working Papers 4332, Asociación Argentina de Economía Política.
    9. Jiecheng Song & Merry Ma, 2023. "Climate Change: Linear and Nonlinear Causality Analysis," Stats, MDPI, vol. 6(2), pages 1-17, May.
    10. Peter C.B. Phillips & Igor Kheifets, 2021. "On Multicointegration," Cowles Foundation Discussion Papers 2306, Cowles Foundation for Research in Economics, Yale University.
    11. Philippe Goulet Coulombe & Maximilian Gobel, 2021. "On Spurious Causality, CO2, and Global Temperature," Papers 2103.10605, arXiv.org.
    12. 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.
    13. Jushuang Qin & Menglu Ma & Jiabin Shi & Shurui Ma & Baoguo Wu & Xiaohui Su, 2023. "The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China," IJERPH, MDPI, vol. 20(1), pages 1-18, January.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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