• Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).

    CAS 
    Article 

    Google Scholar
     

  • Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353, aad9837 (2016).

    Article 

    Google Scholar
     

  • Climate Finance Provided and Mobilised by Developed Countries: Aggregate Trends Updated with 2019 Data (OECD, 2021); https://doi.org/10.1787/5F1F4182-EN

  • Rio Markers for Climate Handbook (OECD, 2016).

  • Yeo, S. Where climate cash is flowing and why it’s not enough. Nature 573, 328–331 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Climate Finance Shadow Report 2020 (Oxfam International, 2020).

  • Halimanjaya, A. Climate mitigation finance across developing countries: what are the major determinants? Clim. Policy 15, 223–252 (2015).

    Article 

    Google Scholar
     

  • Weikmans, R. & Roberts, J. T. The international climate finance accounting muddle: is there hope on the horizon? Clim. Dev. 11, 97–111 (2017).

    Article 

    Google Scholar
     

  • Michaelowa, A. & Michaelowa, K. Coding error or statistical embellishment? The political economy of reporting climate aid. World Dev. 39, 2010–2020 (2011).

    Article 

    Google Scholar
     

  • Donner, S. D., Kandlikar, M. & Webber, S. Measuring and tracking the flow of climate change adaptation aid to the developing world. Environ. Res. Lett. 11, 054006 (2016).

    Article 

    Google Scholar
     

  • Roberts, J. T. et al. Rebooting a failed promise of climate finance. Nat. Clim. Change 11, 180–182 (2021).

    Article 

    Google Scholar
     

  • Climate Adaptation Marker: Quality Review (OECD, 2013).

  • Weikmans, R., Roberts, J. T., Baum, J., Bustos, M. C. & Durand, A. Assessing the credibility of how climate adaptation aid projects are categorised. Dev. Pract. 27, 458–471 (2017).

    Article 

    Google Scholar
     

  • Joint Report on Multilateral Development Banks’ Climate Finance (AfDB et al., 2021).

  • Toetzke, M., Banholzer, N. & Feuerriegel, S. Monitoring global development aid with machine learning. Nat. Sustain. https://doi.org/10.1038/s41893-022-00874-z (2022).

  • Climate Finance in 2013–14 and the USD 100 Billion Goal (OECD & CPI, 2015); http://www.oecd-ilibrary.org/environment/climate-finance-in-2013-14-and-the-usd-100-billion-goal_9789264249424-enhttps://doi.org/10.1787/9789264249424-en

  • Egli, F. & Stünzi, A. A dynamic climate finance allocation mechanism reflecting the Paris Agreement. Environ. Res. Lett. 14, 114024 (2019).

    Article 

    Google Scholar
     

  • Timperley, J. The broken $100-billion promise of climate finance—and how to fix it. Nature 598, 400–402 (2021).

    CAS 
    Article 

    Google Scholar
     

  • Scott, S. The Grant Element Method of Measuring the Concessionality of Loans and Debt Relief Working Paper No. 339 (OECD Development Centre, 2017).

  • van Oldenborgh, G. J. et al. Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett. 12, 124009 (2017).

    Article 

    Google Scholar
     

  • Otto, F. E. L. et al. Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environ. Res. Lett. 13, 124010 (2018).

    Article 

    Google Scholar
     

  • Devlin, J., Chang, M. W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. Preprint at arXiv (2018).

  • Webersinke, N. et al. ClimateBert: a pretrained language model for climate-related text. Preprint at arXiv (2021).

  • Sanh, V., Debut, L., Chaumond, J. & Wolf, T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Preprint at arXiv (2019).

  • Montani, I. & Honnibal, M. Prodigy: a new annotation tool for radically efficient machine teaching (2017). https://explosion.ai/blog/prodigy-annotation-tool-active-learning Accessed: 2022.08.15



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