Climate adaptation as a team process: the role of place-based climate adaptation workshops in catalysing collective action.
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| Title: | Climate adaptation as a team process: the role of place-based climate adaptation workshops in catalysing collective action. |
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| Authors: | O'Brien, Caleb1 (AUTHOR) calebo@vt.edu, Stern, Marc J.1 (AUTHOR), Brousseau, Jennifer J.1 (AUTHOR), Hansen, Lara J.2 (AUTHOR) |
| Source: | Journal of Environmental Planning & Management. Jun2026, Vol. 69 Issue 6, p1680-1707. 28p. |
| Subjects: | Collective action, Adult education workshops, Community leadership, Climate change adaptation, Community involvement, Social groups |
| Geographic Terms: | United States |
| Abstract: | Place-based climate adaptation workshops are an increasingly common approach to advance collective efforts to cope with the effects of climate change. Despite their increasing prevalence, uncertainty remains about effective and ineffective elements of these processes. We conducted a comparative case study across 30 communities in which workshops took place in the United States between 2017 and 2020 to identify which workshop characteristics were most often associated with subsequent adaptation-related planning and action. We examined these workshops through a team process lens to reveal which inputs, processes, and emergent states distinguished workshops with substantial evidence of positive impact (n = 6) from those with little impact (n = 6). Key factors included the involvement of a local champion, co-design of the workshop between facilitators and participants, and sustained engagement post-workshop. As more communities embark on multi-sectoral processes meant to catalyze collective climate action, these findings offer insights for ensuring efforts are as effective as possible. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Environmental Planning & Management is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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