Predicting urban food insecurity under climate change in Brazilian Amazonia

气候政策的最终目标是通过减少温室气体排放来缓解气候变化。然而，这是一个具有十年或百年时间尺度的战略。一个同样重要和更紧迫的关切是，通过提高贫困和边缘化人口对干旱和洪水等恶劣天气事件的适应能力，减少气候变化的影响。洪水和干旱等极端事件造成重大经济损失，并威胁人类健康和粮食安全。干旱可能对亚马逊等地区产生最大的负面影响，因为河流运输是分配粮食的主要方式。例如，2005年和2010年的亚马逊干旱扰乱了粮食分配，导致数十人因营养不良而死亡，并宣布进入紧急状态。旅行时间的增加和运输问题导致食品价格上涨。当粮食价格高时，最贫穷的家庭受害最深，无法获得足够的食物和营养。干旱对这些地区粮食安全构成的危险今后将增加，因为气候变化造成降雨量减少，导致河流水位下降。该项目将开发一种计算机工具，用于预测城市居民的粮食安全何时何地因粮食价格上涨而受到干旱威胁。该工具将基于我们在亚马逊小城市收集的数据，包括重要食品（例如肉类和大米）的价格。我们还将使用一系列问题来确定城市家庭是否有足够的机会获得营养和负担得起的食物。我们将联合收割机将这些信息与整个亚马逊地区的河流水位和贫困分布信息（由巴西政府收集）结合起来，并使用通常用于预测疾病爆发的统计技术分析数据。这项分析的结果将成为一个工具，可以生成地图，显示亚马逊数百个城市中每个城市的粮食安全风险水平。这将有助于巴西政府具有战略性，并将其资源投资于预计在干旱期间有大多数人营养不良的城市。

The ultimate goal of climate policy is to mitigate climate change by reducing greenhouse gas emissions. However, this is a strategy with decadal or centurial timescales. An equally important and more urgent concern is to reduce the impacts of climate change by improving the adaptive capacity of poor, marginalized populations to severe weather events such as droughts and floods. Extreme events such as floods and droughts cause significant economic losses, and threaten human health and food security. Droughts probably have the greatest negative impacts in regions such as the Amazon, where river transport is the main way of distributing food. For example, Amazon droughts in 2005 and 2010 disrupted food distribution and led to dozens of deaths from malnutrition and declarations of state of emergency. Increased journey times and transport problems leads to higher food prices. When food prices are high, poorest families suffer most and lack access to adequate food and nutrition. The danger that droughts pose to food security in these regions will increase in the future because of climate change causing reduced rainfall, which leads to lower river levels. This project will develop a computer-based tool for predicting where and when food security of city-dwellers will be threatened by droughts, because of higher food prices. The tool will be based on data that we collect in small cities in the Amazon, including prices of important foods (meat and rice, for example). We will also use a series of questions to establish whether urban families have sufficient access to nutritious and affordable food. We will combine this information with information on river levels and poverty distribution across the whole Amazon (collected by the Brazilian government) and analyze data using statistical techniques normally used to predict disease outbreaks. The outcome of this analysis will be a tool that can generate maps to show the level of Food Security Risk for each of the hundreds of municipalities in the Amazon. This will help the Brazilian government to be strategic and invest their resources in the cities that are predicted to have most people suffering from malnutrition during droughts.