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<p>This paper describes a coding algorithm and corresponding dataset of negotiation interventions and negotiation interactions by country parties and groupings in the multilateral negotiations under the United Nations Framework Convention on Climate Change (UNFCCC). The data is obtained by scraping and automatically coding the negotiation summaries published in the Earth Negotiations Bulletins (ENBs) between 1995 and 2023. The data is validated by comparing it with a hand-coded dataset of negotiation interactions under the UNFCCC. One limitation discovered upon validation is that our automated procedure finds significantly fewer opposition interactions than the hand-coding procedure. The main reason for this is that the algorithm identifies negotiation interactions on the basis of individual sentences, while the hand coding is able to identify them across sentences and even paragraphs. However, the distribution of opposition interactions seems to be representative of the larger dataset and therefore not substantively biased. We describe possible uses of this data in research, and provide the algorithm, which can be adapted for application to other negotiations covered by the ENBs.</p>

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Participation, Cooperation and Conflict in UN Climate Negotiations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Paula Castro,
  • Victor Kristof,
  • Marlene Kammerer,
  • Tatiana Cogne

摘要

This paper describes a coding algorithm and corresponding dataset of negotiation interventions and negotiation interactions by country parties and groupings in the multilateral negotiations under the United Nations Framework Convention on Climate Change (UNFCCC). The data is obtained by scraping and automatically coding the negotiation summaries published in the Earth Negotiations Bulletins (ENBs) between 1995 and 2023. The data is validated by comparing it with a hand-coded dataset of negotiation interactions under the UNFCCC. One limitation discovered upon validation is that our automated procedure finds significantly fewer opposition interactions than the hand-coding procedure. The main reason for this is that the algorithm identifies negotiation interactions on the basis of individual sentences, while the hand coding is able to identify them across sentences and even paragraphs. However, the distribution of opposition interactions seems to be representative of the larger dataset and therefore not substantively biased. We describe possible uses of this data in research, and provide the algorithm, which can be adapted for application to other negotiations covered by the ENBs.