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Designing a statistical procedure for monitoring global carbon dioxide emissions

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Abstract

Following the Paris Agreement of 2015, most countries have agreed to reduce their carbon dioxide (CO2) emissions according to individually set Nationally Determined Contributions. However, national CO2 emissions are reported by individual countries and cannot be directly measured or verified by third parties. Inherent weaknesses in the reporting methodology may misrepresent, typically an under-reporting of, the total national emissions. This paper applies the theory of sequential testing to design a statistical monitoring procedure that can be used to detect systematic under-reportings of CO2 emissions. Using simulations, we investigate how the proposed sequential testing procedure can be expected to work in practice. We find that, if emissions are reported faithfully, the test is correctly sized, while, if emissions are under-reported, detection time can be sufficiently fast to help inform the 5 yearly global “stocktake” of the Paris Agreement. We recommend the monitoring procedure be applied going forward as part of a larger portfolio of methods designed to verify future global CO2 emissions.

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Notes

  1. http://www.globalcarbonproject.org/

  2. In the 2020 version of the reports, GCB2020, a new sink term, \({S_{t}^{C}}\), was introduced into the budget equation, which is an estimate of the carbon sink from cement carbonation. The magnitude of this sink is small and here we simply include it in the fossil fuel emission estimates as suggested in Friedlingstein et al. (2020, p. 3277).

  3. We write “\(\overset {P}{\rightarrow }\)” for convergence in probability.

  4. Although, prima facie, α = 32% might seem like a high significance level to consider, a similar threshold is often used by the IPCC, where events happening with a probability lower than 33% are termed “unlikely” (Mastrandrea et al. 2010, Table 1, p. 3). Choosing such a high significance level will facilitate early detection of potential misreporting of CO2 emissions; naturally, it comes with the caveat of a correspondingly high probability of making a Type I error.

  5. https://sites.google.com/site/mbennedsen/research/monitoring.

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Acknowledgements

The author would like to thank Eric Hillebrand, Siem Jan Koopman, three anonymous referees, and participants in the session on “Climate change mitigation, impacts, and adaptation” at the European Geoscience Union (EGU) General Assembly, Vienna, 2019, for many helpful comments and suggestions on the manuscript.

Funding

Financial support from the Independent Research Fund Denmark for the project “Econometric Modeling of Climate Change” is acknowledged.

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Authors and Affiliations

  1. Department of Economics and Business Economics and CREATES, Aarhus University, Fuglesangs Allé 4, 8210, Aarhus V, Denmark

    Mikkel Bennedsen

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  1. Mikkel Bennedsen
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Correspondence to Mikkel Bennedsen.

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Availability of data and material

The data used in this paper are freely available online; see https://doi.org/10.18160/GCP-2020for the GCB2020 version.

Code availability

The MATLAB code used to produce the results of the paper is freely available at https://sites.google.com/site/mbennedsen/research/monitoring.

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Bennedsen, M. Designing a statistical procedure for monitoring global carbon dioxide emissions. Climatic Change 166, 32 (2021). https://doi.org/10.1007/s10584-021-03123-y

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