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Event Ethnography to study the global negotiations on the treaty to end plastic pollution: dataset from the first session of negotiations (INC-1)
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Researchers used event ethnography to study the global negotiations toward a legally binding international plastics treaty, observing negotiation sessions and documenting how different stakeholder groups — governments, industry, and civil society — framed their positions. The study reveals how contested definitions of plastic pollution and responsibility shaped negotiating dynamics.
Abstract Plastics and their associated chemical pollution have emerged as a global threat. This recognition materialized in March 2022 with the adoption of a groundbreaking mandate among United Nations (UN) Member States, signaling the commencement of negotiations for a comprehensive treaty aimed at ending plastic pollution, encompassing the entire lifecycle. The first session of negotiations convened at the International Negotiating Committee (INC-1) in Punta del Este, Uruguay from November 28th–December 2nd, 2022, from there, four other negotiation sessions have either convened or will, with the expected conclusion in December 2024. The current study introduces a methodology for systematically observing and documenting global agreement-making. It utilizes a dataset from the first session of negotiations to explore the event ethnography (EE) methodology for following Multilateral Environmental Agreements (MEA). This paper aims to inspire and guide researchers in employing EE, recognizing its efficacy in navigating the complexities of the extensive Global Plastics Treaty (GPT) negotiations and promoting collaborative efforts for a comprehensive understanding of the process. EE is a method increasingly recognised to study multilateral international negotiations within the science-policy nexus to examine emerging obstacles, trends, power dynamics, and actors (both state and non-state) in action within the negotiations. Finally, a dataset is presented from INC-1 through a practical Excel document; the article then demonstrates one example of how the dataset can be employed to enhance comprehension of the negotiators and observers influencing the GPT negotiations.
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