Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

2026-09-21

AI Summary

研究通过引入敏感性机制并结合遥感、社会经济数据和因果机器学习方法，解决了气候变异对价格飙升影响粮食安全问题的预警不足。

Abstract

Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Niño, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by introducing sensitivity regimes, a stratification of regions by the direction and strength of their vegetation response to the El Niño Southern Oscillation, and using them to estimate how food price spikes affect acute food insecurity across sub-Saharan Africa. Integrating remote sensing, socioeconomic data, and causal machine learning, we find that in regions where ENSO systematically suppresses vegetation, a price spike raises the share of the population at acute risk by 5.4 percentage points in the following month. In regions where vegetation is unaffected by or positively linked to ENSO, the estimated effect is smaller (around 2 percentage points) and statistically insignificant. These results demonstrate that climate context is critical for understanding food security vulnerabilities. Sensitivity regimes can be combined with operational price-spike triggers to stage anticipatory action: the ENSO state flags vulnerable regions months ahead, and a pre-positioned response in those regions to a price spike would avert the largest jump in acute food insecurity.

Problem

• Climate Variability
• Food Insecurity
• El Niño Southern Oscillation
• Price Spikes
• Sensitivity Regimes

Innovation

• sensitivity regimes
• ENSO
• food price spikes
• acute food insecurity
• anticipatory action