Transforming tropical peatland governance to manage climate risks using the Three Horizons method

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Abstract

Tropical peatlands occupy at least 440,000 km2 and are estimated to store around 100 megatons of carbon, but are exposed to risks from both current and anticipated future changes in climate. Integrating knowledge from diverse sources using methods that can manage complexity is vital in order to identify transformational governance options for managing climate risks in these multifunctional social-ecological systems. This paper breaks new ground by applying the Three Horizons method to the governance of tropical peatlands. It evaluates the capacity for this method to generate transformative options that address conceptual and existential risks, as well as visible climate risks, whilst integrating research evidence with local knowledge. The paper focuses on countries that collectively represent the majority of global tropical peatland area and emissions, combining evidence from the literature with in-country expertise through Three Horizons workshops in Peru, Democratic Republic of Congo and Republic of Congo, and additional business-to-business engagement in Indonesia. The paper identifies a number of pathways that could transform the resilience of habitats and populations dependent on tropical peatlands, with community empowerment and payments for ecosystem services emerging as key themes across all four countries. Drawing on these findings, recommendations are made for managing climate risks through tropical peatland conservation, restoration and sustainable management. Application of the Three Horizons method demonstrates the critical role of integrating multiple knowledge sources to structure dialogues that can create credible and socially acceptable policy options for managing complex social-ecological systems.

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Reed, M. S., Kopansky, D., Beechener, S., Green, A., Scheel, P., Kendrick, I., … Mihaha, E. T. N. (2025). Transforming tropical peatland governance to manage climate risks using the Three Horizons method. PLOS ONE, 20(11 November). https://doi.org/10.1371/journal.pone.0324399

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