International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
Vienna University of Economics and Business (WU), Vienna, Austria.
Sci Rep. 2021 Oct 11;11(1):20146. doi: 10.1038/s41598-021-99343-4.
Natural disasters negatively impact regions and exacerbate socioeconomic vulnerabilities. While the direct impacts of natural disasters are well understood, the channels through which these shocks spread to non-affected regions, still represents an open research question. In this paper we propose modelling socioeconomic systems as spatially-explicit, multi-layer behavioral networks, where the interplay of supply-side production, and demand-side consumption decisions, can help us understand how climate shocks cascade. We apply this modelling framework to analyze the spatial-temporal evolution of vulnerability following a negative food-production shock in one part of an agriculture-dependent economy. Simulation results show that vulnerability is cyclical, and its distribution critically depends on the network density and distance from the epicenter of the shock. We also introduce a new multi-layer measure, the Vulnerability Rank (VRank), which synthesizes various location-level risks into a single index. This framework can help design policies, aimed to better understand, effectively respond, and build resilience to natural disasters. This is particularly important for poorer regions, where response time is critical and financial resources are limited.
自然灾害会对地区造成负面影响，并加剧社会经济的脆弱性。尽管自然灾害的直接影响已被充分了解，但这些冲击如何传播到未受影响的地区，仍然是一个悬而未决的研究问题。在本文中，我们提出将社会经济系统建模为空间显式的多层行为网络，其中供应方的生产和需求方的消费决策之间的相互作用，可以帮助我们了解气候冲击是如何级联的。我们应用这个建模框架来分析一个依赖农业的经济体的某一部分发生负粮食生产冲击后脆弱性的时空演变。模拟结果表明，脆弱性是周期性的，其分布严重依赖于网络密度和距冲击震中的距离。我们还引入了一个新的多层度量指标，即脆弱性排名（VRank），它将各种位置层面的风险综合为一个单一的指数。该框架可以帮助设计政策，旨在更好地了解、有效应对和建立对自然灾害的抵御能力。这对于资源有限且响应时间至关重要的贫困地区尤为重要。
