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  • 标题:Archetyping shared socioeconomic pathways across scales: an application to central Asia and European case studies
  • 本地全文:下载
  • 作者:Simona Pedde ; Kasper Kok ; Katharina Hölscher
  • 期刊名称:Ecology and Society
  • 印刷版ISSN:1708-3087
  • 电子版ISSN:1708-3087
  • 出版年度:2019
  • 卷号:24
  • 期号:4
  • 页码:30
  • DOI:10.5751/ES-11241-240430
  • 语种:English
  • 出版社:The Resilience Alliance
  • 摘要:The complex interactions of drivers represented in scenarios and climate change impacts across scales have led to the development of multiscale scenarios. Since the recent development of global shared socioeconomic pathways (SSPs), which have started being downscaled to lower scales, the potential of scenarios to be relevant for decision making and facilitate appreciation and inclusion of different perspectives has been increasing, compared with a single-scale global scenario set. However, in practice, quantitative downscaling of global scenarios results in narratives that are compressed from the global level to fit the local context to enhance consistency between global and local scales. We brought forward the concept of scenario archetypes to analyze multiscale SSP scenario narratives and highlight important diverging assumptions within the same archetype. Our methodology applied scenario archetypes both as typologies, to allocate specific cases of scenarios into existing scenario archetypes, and building blocks, conceptualized with worldviews from cultural theory. Although global SSPs generally match existing archetypes and tend to be well defined, the socially unequal SSPs at subglobal scales are more nuanced, and dominant worldviews are much less straighforward to interpret than in global scenarios. The closest match was the great transition-sustainability (SSP1) archetype, whereas the most divergent was the market forces-fossil fuel development (SSP5) archetype. Overall, our results highlight the need to improve uptake of bottom-up approaches in global scenarios to improve appreciation of different perspectives as sought after in multiscale scenarios.
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