The poverty implications of increasing frequencies of extreme weather events - identifying tipping points across households from different income groups

The poverty implications of increasing frequencies of extreme weather events - identifying tipping points across households from different income groups Climate coffee on 22 May 2025. The recording is available here: https://youtu.be/SLjl-bamAX8 Abstract The frequency of extreme weather events is projected to increase in many world regions under climate change. However, the societal implications of these higher event frequencies are far less understood, as the short- and long-term impacts of extreme events such as floods and tropical cyclones are commonly studied for individual extremes. It is generally assumed that the impacts of extreme events with overlapping socio-economic repercussions are likely to differ from the cumulative impacts of independent events. Yet, a comprehensive quantitative understanding of the potential non-linear impacts of increased event frequencies remains widely absent. Critical thresholds that, when surpassed, cause non-linear transitions in social systems to a qualitatively different state are often referred to as social tipping points. Here, we assess whether increasing frequencies of weather extremes can trigger the social tipping of households, defined as a transition from a non-poor state that allows for self-reliant recovery to a poverty trap that hinders further independent recovery. To this end, we employ an agent-based model accounting for the recovery dynamics of households affected by consecutive extreme events. We assess the effect of incomplete recoveries in between recurrent extremes in terms of consumption and well-being losses as well as critical event frequencies inducing tipping across households from different income groups. We find that middle-income households are most sensitive to being trapped in poverty due to increased event frequencies and associated incomplete recovery. High-income households can mostly afford a self-reliant recovery even under high event frequencies, while poor households are often trapped in poverty by individual events. Finally, we explore the implications of these household tipping dynamics for societal inequality. Our speaker Inga Sauer completed her Master’s Degree in Sustainability Science and Technology at the Technical University of Catalonia (UPC) in Barcelona, working on institutional barriers for climate change adaptation in coastal management. In 2019, she started her PhD in the group of Event-based modeling of economic impacts of Climate Change at PIK. In her PhD project she worked on the socio-economic dimension of river floods, analyzing direct damage and distributional effects of disasters and their attribution to climate change. Since 2024 she co-leads the group Data_centric modeling of cross-sectoral impacts together with Matthias Mengel.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-26
DOI
https://doi.org/10.5281/zenodo.15517966
Primary Topic
Ecosystem dynamics and resilience
Type
article
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article

The poverty implications of increasing frequencies of extreme weather events - identifying tipping points across households from different income groups

Zenodo (CERN European Organization for Nuclear Research)
Ecosystem dynamics and resilience
article

The poverty implications of increasing frequencies of extreme weather events - identifying tipping points across households from different income groups

article en

Abstract

The poverty implications of increasing frequencies of extreme weather events - identifying tipping points across households from different income groups Climate coffee on 22 May 2025. The recording is available here: https://youtu.be/SLjl-bamAX8 Abstract The frequency of extreme weather events is projected to increase in many world regions under climate change. However, the societal implications of these higher event frequencies are far less understood, as the short- and long-term impacts of extreme events such as floods and tropical cyclones are commonly studied for individual extremes. It is generally assumed that the impacts of extreme events with overlapping socio-economic repercussions are likely to differ from the cumulative impacts of independent events. Yet, a comprehensive quantitative understanding of the potential non-linear impacts of increased event frequencies remains widely absent. Critical thresholds that, when surpassed, cause non-linear transitions in social systems to a qualitatively different state are often referred to as social tipping points. Here, we assess whether increasing frequencies of weather extremes can trigger the social tipping of households, defined as a transition from a non-poor state that allows for self-reliant recovery to a poverty trap that hinders further independent recovery. To this end, we employ an agent-based model accounting for the recovery dynamics of households affected by consecutive extreme events. We assess the effect of incomplete recoveries in between recurrent extremes in terms of consumption and well-being losses as well as critical event frequencies inducing tipping across households from different income groups. We find that middle-income households are most sensitive to being trapped in poverty due to increased event frequencies and associated incomplete recovery. High-income households can mostly afford a self-reliant recovery even under high event frequencies, while poor households are often trapped in poverty by individual events. Finally, we explore the implications of these household tipping dynamics for societal inequality. Our speaker Inga Sauer completed her Master’s Degree in Sustainability Science and Technology at the Technical University of Catalonia (UPC) in Barcelona, working on institutional barriers for climate change adaptation in coastal management. In 2019, she started her PhD in the group of Event-based modeling of economic impacts of Climate Change at PIK. In her PhD project she worked on the socio-economic dimension of river floods, analyzing direct damage and distributional effects of disasters and their attribution to climate change. Since 2024 she co-leads the group Data_centric modeling of cross-sectoral impacts together with Matthias Mengel.

Zenodo (CERN European Organization for Nuclear Research)
Potsdam Institute for Climate Impact Research (DE)
No poverty
Openalex Percentile: Top 58%
Ecosystem dynamics and resilience
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