five

Data_Sheet_1_Navigating climate change complexity and deep uncertainty: approach for building socio-ecological resilience using qualitative dynamic simulation.pdf

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Navigating_climate_change_complexity_and_deep_uncertainty_approach_for_building_socio-ecological_resilience_using_qualitative_dynamic_simulation_pdf/25602792
下载链接
链接失效反馈
官方服务:
资源简介:
The consequences of climate change on different sectors of society are interrelated. The threats posed by rising global temperatures, intensifying extreme weather events, and shifting climatic patterns are becoming increasingly evident all around the world. Policymakers face the daunting task of assessing climate change risks, encompassing impacts and response strategies, to guide sustainability transformations. In this study, we introduce a four-step qualitative Decision Making Under Deep Uncertainty (DMDU) approach in the context of Climate Change Impact Assessments (CCIA). Our goal is to enhance the integration of CCIA into spatial planning, particularly in the Global South, using qualitative system dynamics simulation. Emphasizing the value of qualitative DMDU, we explore vulnerability and resilience through a lens of multi-sectoral and multi-scalar socio-ecological processes. We exemplify our approach by applying CCIA to the coastal zone of Yucatán, Mexico, accounting for social and environmental heterogeneity across the four Regions in which it is administered. Results identify the optimal allocation of climate change mitigation and adaptation policies to address specified resilience in each Region, all of which are required to achieve the overall resilience of the coastal zone. We argue that our qualitative DMDU approach provides an analytical platform to address the trade-offs inherent in the ranking of multiple vulnerabilities related to achieving general resilience.
创建时间:
2024-04-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作