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Area of Science:

  • Environmental Science
  • Political Science
  • Computational Social Science

Background:

  • Multilateral climate negotiations are complex, involving numerous country parties and groupings.
  • Understanding negotiation dynamics is crucial for effective climate policy development.
  • Existing datasets on negotiation interactions are limited in scope and scale.

Purpose of the Study:

  • To develop and validate an automated algorithm for coding negotiation interventions and interactions.
  • To create a comprehensive dataset of negotiation data from 1995-2023.
  • To provide a reusable tool for analyzing multilateral negotiations.

Main Methods:

  • Automated data scraping and coding of negotiation summaries from Earth Negotiations Bulletins (ENBs).
  • Dataset covers multilateral negotiations under the United Nations Framework Convention on Climate Change (UNFCCC).
  • Validation against a hand-coded dataset to assess accuracy and identify limitations.

Main Results:

  • A dataset of negotiation interventions and interactions was generated for the period 1995-2023.
  • The automated algorithm identified fewer opposition interactions compared to manual coding due to sentence-level analysis.
  • Despite this limitation, the distribution of opposition interactions is deemed representative and not substantively biased.

Conclusions:

  • The developed algorithm and dataset offer a scalable approach to studying climate change negotiations.
  • The algorithm can be adapted for analyzing other negotiations documented in ENBs.
  • This resource facilitates further research into international environmental policy and cooperation.