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PDF
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Type:
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Journal Article |
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Author:
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Baggio, Jacopo Alessandro; Barnett, Allain T.; Pèrez-Ibarra, Irene; Brady, Ute; Ratajczyk, Elicia; Rollins, Nathan; Rubiños, Cathy; Shin, Hoon C.; Yu, David J.; Aggarwal, Rimjhim; Anderies, John M.; Janssen, Marco A. |
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Journal:
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International Journal of the Commons |
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Volume:
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10 |
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Page(s):
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417-439 |
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Date:
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2016 |
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URI:
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https://hdl.handle.net/10535/10154
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Sector:
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General & Multiple Resources
Theory |
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Region:
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Subject(s):
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common pool resources
fisheries
forestry
governance and politics
irrigation
social-ecological systems
technology
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Abstract:
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"Governing common pool resources (CPR) in the face of disturbances such as globalization and climate change is challenging. The outcome of any CPR governance regime is the influenced by local combinations of social, institutional, and biophysical factors, as well as cross-scale interdependencies. In this study, we take a step towards understanding multiple-causation of CPR outcomes by analyzing 1) the co-occurrence of Destign Principles (DP) by activity (irrigation, fishery and forestry), and 2) the combination(s) of DPs leading to social and ecological success. We analyzed 69 cases pertaining to three different activities: irrigation, fishery, and forestry. We find that the importance of the design principles is dependent upon the natural and hard human made infrastructure (i.e. canals, equipment, vessels etc.). For example, clearly defined social bounduaries are important when the natural infrastructure is highly mobile (i.e. tuna fish), while monitoring is more important when the natural infrastructure is more static (i.e. forests or water contained within an irrigation system). However, we also find that congruence between local conditions and rules and proportionality between investment and extraction are key for CPR success independent from the natural and human hard made infrastructure. We further provide new visualization techniques for co-occurrence patterns and add to qualitative comparative analysis by introducing a reliability metric to deal with a large meta-analysis dataset on secondary data where information is missing or uncertain."
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