Leveraging big data to inform better policy responses to climate change and extreme heat events

Abstract:

Advances in big data analytics have unlocked huge opportunities for resource management, particularly in terms of producing policy-relevant research with high spatial and temporal resolution. Rather than producing general results that characterize average populations across large geographic extents, datasets collected at the individual-, household-, or census tract-level can produce insights into the distribution of potential behaviors or outcomes, which can help inform targeted interventions directed to the populations that can benefit from them most. This talk will discuss how big datasets characterizing attributes of energy, water, and climate systems have been integrated with socio-economic and public health data for a variety of applications, including: coordinating energy and water management systems to accelerate electric grid decarbonization pathways, identifying populations at highest risk of the health consequences of extreme weather events occurring during the COVID-19 pandemic, and predicting how extreme heat might affect vulnerable neighborhoods in a hotter world using machine learning techniques. This talk will also discuss some of the existing data gaps and methodological challenges that should be addressed to inform better resource governance in a changing world.