Enenkel M, Brown M E, Vogt J V, McCarty J L, Reid Bell A, Guha-Sapir D, Dorigo W, Vasilaky K, Svoboda M, Bonifacio R, Anderson M, Funk C, Osgood D, Hain C, Vinck P
Harvard Humanitarian Initiative, Harvard University, Cambridge, MA USA.
World Bank Disaster Risk Financing and Insurance (DRFI) Program, Washington, DC USA.
Clim Change. 2020;162(3):1161-1176. doi: 10.1007/s10584-020-02878-0. Epub 2020 Oct 9.
Virtually all climate monitoring and forecasting efforts concentrate on hazards rather than on impacts, while the latter are a priority for planning emergency activities and for the evaluation of mitigation strategies. Effective disaster risk management strategies need to consider the prevailing "human terrain" to predict who is at risk and how communities will be affected. There has been little effort to align the spatiotemporal granularity of socioeconomic assessments with the granularity of weather or climate monitoring. The lack of a high-resolution socioeconomic baseline leaves methodical approaches like machine learning virtually untapped for pattern recognition of extreme climate impacts on livelihood conditions. While the request for "better" socioeconomic data is not new, we highlight the need to collect and analyze environmental and socioeconomic data together and discuss novel strategies for coordinated data collection via mobile technologies from a drought risk management perspective. A better temporal, spatial, and contextual understanding of socioeconomic impacts of extreme climate conditions will help to establish complex causal pathways and quantitative proof about climate-attributable livelihood impacts. Such considerations are particularly important in the context of the latest big data-driven initiatives, such as the World Bank's Famine Action Mechanism (FAM).
几乎所有的气候监测和预测工作都集中在灾害而非影响上，而后者对于规划应急活动和评估减灾战略来说才是优先事项。有效的灾害风险管理战略需要考虑当前的“人文环境”，以预测哪些人处于风险之中以及社区将如何受到影响。几乎没有人努力使社会经济评估的时空粒度与天气或气候监测的粒度保持一致。缺乏高分辨率的社会经济基线使得机器学习等系统方法在识别极端气候对生计条件的影响模式方面几乎未得到利用。虽然对“更好的”社会经济数据的要求并不新鲜，但我们强调需要同时收集和分析环境与社会经济数据，并从干旱风险管理的角度讨论通过移动技术进行协调数据收集的新战略。对极端气候条件的社会经济影响有更好的时间、空间和背景理解，将有助于建立复杂的因果路径以及关于气候导致的生计影响的定量证据。在最新的大数据驱动举措（如世界银行的饥荒行动机制（FAM））的背景下，这些考虑尤为重要。
