气候变率调节价格飙升对粮食不安全的影响

中文摘要 AI 辅助

气候变率影响市场扰动是否会升级为粮食危机，然而，像厄尔尼诺这样的宏观气候模式，在改变水文气候条件前数月即可被追踪，却仍未作为预警组成部分纳入粮食安全应对措施。我们通过引入敏感性机制（sensitivity regimes）来填补这一空白，该机制根据植被对厄尔尼诺南方涛动（ENSO）响应的方向和强度对区域进行分层，并利用这些机制来估计粮食价格飙升如何影响撒哈拉以南非洲地区的急性粮食不安全状况。整合遥感、社会经济数据和因果机器学习，我们发现，在ENSO系统性抑制植被的区域，价格飙升会使下个月处于急性风险的人口比例上升5.4个百分点。在植被不受ENSO影响或与ENSO正相关的区域，估计效应较小（约2个百分点）且统计上不显著。这些结果表明，气候背景对于理解粮食安全脆弱性至关重要。敏感性机制可与操作性价格飙升触发条件相结合，以分阶段采取预期行动：ENSO状态提前数月标记出脆弱区域，在这些区域对价格飙升进行预先部署的响应将避免急性粮食不安全的最大幅度跃升。

英文摘要

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.

发表机构

• Universitat de València（瓦伦西亚大学）
• Wageningen University & Research（瓦赫宁根大学及研究中心）
• European Commission（欧盟委员会）

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