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Consistent and replicable estimation of bilateral climate finance

Nature Climate Change volume 12, pages 897–900 (2022)Cite this article

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Abstract

International climate finance is key to achieving the goals of the Paris Agreement. Here we develop a machine learning classifier to identify international climate finance from 2.7 million official development assistance projects between 2000 and 2019, resulting in a consistent and replicable inventory of 82,023 bilateral climate finance projects (US$80 billion). Our findings reinforce concerns that the actual numbers may be much lower than current estimates made with Rio markers.

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Fig. 1: Bilateral ICF from 2000 to 2019.
Fig. 2: Bilateral ICF from 2016 to 2019.

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Data availability

The underlying data for the analysis in this study and the annotated training set for the language classifiers can be retrieved from https://github.com/MalteToetzke/consistent-and-replicable-estimation-of-bilateral-climate-finance. For access to the raw data and the trained model parameters of ClimateFinanceBERT, please contact M.T. (mtoetzke@ethz.ch).

Code availability

All scripts used for pre-processing, modelling and analysis are publicly available via https://github.com/MalteToetzke/consistent-and-replicable-estimation-of-bilateral-climate-finance.

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Acknowledgements

We thank seminar participants at ETH Zurich and the University of St. Gallen and staff of the Swiss Federal Office for the Environment for valuable input on previous versions of this paper. We also thank R. Weikmans and J. T. Roberts for making available the project-level data used in Weikmans et al.13 and the research assistants and students for participating in the validation surveys. This project was partially funded by the Swiss Network for International Studies.

Author information

Authors and Affiliations

  1. Group of Sustainability and Technology, ETH Zurich, Zurich, Switzerland

    Malte Toetzke

  2. Comparative Politics Group, University of St. Gallen, St. Gallen, Switzerland

    Anna Stünzi

  3. Climate & Energy Policies WG, Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Anna Stünzi

  4. Energy and Technology Policy Group, ETH Zurich, Zurich, Switzerland

    Florian Egli

  5. Institute for Innovation and Public Purpose, UCL, London, UK

    Florian Egli

Authors
  1. Malte Toetzke
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  2. Anna Stünzi
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  3. Florian Egli
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Contributions

On the basis of an idea by F.E., F.E., A.S. and M.T. developed the research design. M.T. developed ClimateFinanceBERT and analysed the data. A.S., F.E. and M.T. interpreted the data. F.E., A.S. and M.T. wrote the manuscript. F.E. and A.S. secured the project funding.

Corresponding authors

Correspondence to Malte Toetzke, Anna Stünzi or Florian Egli.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Axel Michaelowa, J. Roberts and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Bilateral climate finance in ODA over time including projects with focus on environment.

Bilateral climate finance in ODA over time including projects with focus on environment.

Extended Data Fig. 2 Precision scores of Rio marker reporting.

Precision scores of Rio marker reporting on project-level by contributing country. The precision score is measured as the share of projects tagged as climate finance through the Rio markers that are also classified as climate finance by ClimateFinanceBERT. For the analysis, only projects tagged with principal Rio markers were considered. NaN values represent cases where no corresponding finance has been reported via the Rio markers with a principal marker. The period of evaluation is from 2016 to 2019.

Extended Data Fig. 3 Recall scores of Rio marker reporting.

Recall scores of Rio marker reporting on project-level by contributing country. The recall is measured as share of projects classified as climate finance by ClimateFinanceBERT that are also tagged as climate finance through the Rio markers. For the analysis, projects tagged with principal and significant Rio markers were considered. NaN values represent cases where no corresponding finance has been classified by ClimateFinanceBERT. The period of evaluation is from 2016 to 2019.

Extended Data Fig. 4 Financial instruments used by contributors.

Financial instruments used by contributors for mitigation and adaptation between 2016 and 2019. The use of loans in ICF is concentrated among Germany, France, Japan, and Canada. Hence, one would expect absolute changes for these countries’ ICF if grant-equivalents are used.

Extended Data Fig. 5 Robustness check for mitigation and adaptation finance in ODA by recipient.

Robustness check for mitigation and adaptation finance in ODA by recipient, calculating a grant-equivalent of 50% per loan for 2016–2019. The calculation of grant-equivalents for loans is required for data since 2018 but has only been applied to 18% of the data since then. The average calculated grant-equivalent per loan is 58%. Y-axis shows that share of the total adaptation (square) and mitigation (dot) finance that a country received since the Paris Agreement (cumulated, 2016–2019). X-axis shows the national GDP in 2019. Countries that received more mitigation finance than adaptation finance are coloured in green. Countries that received more adaptation finance than mitigation finance are coloured in orange.

Extended Data Fig. 6 Robustness check for mitigation and adaptation finance in ODA by contributor.

Robustness check for mitigation and adaptation finance in ODA by contributor, calculating a grant-equivalent of 50% per loan for 2016–2019. The calculation of grant-equivalents for loans is required for data since 2018, but has only been applied to 18% of the data since then. The average calculated grant-equivalent per loan is 58%. Note: Y-axis shows that share of the total adaptation (square) and mitigation (dot) finance that a country contributed since the Paris Agreement (cumulated, 2016–2019). X-axis shows the national GDP in 2019. Countries that contributed more mitigation finance than adaptation finance are coloured in green. Countries that contributed more adaptation finance than mitigation finance are coloured in orange.

Extended Data Fig. 7 Schematic overview of the model framework for ClimateFinanceBERT.

Schematic overview of the model framework for ClimateFinanceBERT. Project descriptions that are annotated as relevant (Relevance = 1) relate to mitigation, adaptation, or environmental activities.

Extended Data Fig. 8 Results from the user study.

Results from the user study evaluating project descriptions regarding their relevance for climate finance. X-Axis indicates the share of project descriptions that have been evaluated as climate finance by the respondents (0 = no climate finance; 1 = climate finance). Coloured triangles represent the average evaluation of each respondent. The dashed lines represent the average across all respondents for each case. Every colour refers to a specific respondent and each respondent evaluated project descriptions from each case.

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Supplementary Information

Supplementary Discussions 1 and 2 and Tables 1–14.

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Toetzke, M., Stünzi, A. & Egli, F. Consistent and replicable estimation of bilateral climate finance. Nat. Clim. Chang. 12, 897–900 (2022). https://doi.org/10.1038/s41558-022-01482-7

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