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Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security

The Social-Ecological Systems (SES) framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. Yet, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change that institutions and natural resources systems co-evolve to provide a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how Data Cube technology could overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users.
- University of Arizona United States
- Institute for Environmental Sciences Japan
- University of Geneva Switzerland
- University of Geneva
- Institute for Environmental Sciences Japan
Social-ecological system, TJ807-830, TD194-195, data-cube, Renewable energy sources, institution, social-ecological system, Learning, GE1-350, Data-cube, Governance, learning, Environmental effects of industries and plants, Institution, water security, Environmental sciences, political_science, governance, Water security, ddc: ddc:354.3, ddc: ddc:333.7-333.9
Social-ecological system, TJ807-830, TD194-195, data-cube, Renewable energy sources, institution, social-ecological system, Learning, GE1-350, Data-cube, Governance, learning, Environmental effects of industries and plants, Institution, water security, Environmental sciences, political_science, governance, Water security, ddc: ddc:354.3, ddc: ddc:333.7-333.9
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).18 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
