Inês Azevedo, Associate Professor, Energy Resources Engineering, Stanford University
Electricity generation is a large contributor to PM 2.5 air pollution. However, the demographic distribution of its resulting exposure is largely unknown. We estimate the health effects from air pollution from electricity generation in the US, for each of the seven Regional Transmission Organizations, for each US state, by income and by race. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, we observe large differences between where electricity is generated and where people experience the resulting air pollution health consequences: for 36 US states, most of the health impacts are attributable to emissions in other states. Then, we discuss the issue of improved air quality and human health, which are often discussed as “co-benefits” of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We have developed and implemented a model that optimizes emissions reductions costs from the U.S. power sector for climate and health benefits under retirements and new plant construction decisions. We determine the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO2 reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration−response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO2 by 30% yields $21−68 billion in annual health benefits, with an additional $9−36 billion possible when co-optimizing for climate and health benefits. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.
Inês M.L. Azevedo is Associate Professor in the Department of Energy Resources Engineering at Stanford University. She also serves as Senior Fellow for the Woods Institute for the Environment at Stanford University and Fellow for the Precourt Institute for Energy at Stanford University. Prof. Azevedo’s research interests focus on how to transition to a sustainable, low carbon, affordable, and equitable energy system. She is interested in sustainability and energy issues where a systems approach is needed, by combining engineering and technology analysis with economic and decision science approaches. Her current interest is to address energy issues with particular focus on distributional effects and equity. She has published 100+ peer-reviewed journal papers. She has participated as an author and committee member in several National Research Council reports from the U.S. National Academy of Sciences. She was one of the Lead Authors for IPCC AR6 report on Climate Mitigation for the Energy chapter, and she is now also participating as Lead Author for the upcoming U.S. National Climate Assessment chapter on climate change mitigation. Prof. Azevedo is also contributing as a chapter author to the upcoming U.S. National Climate Assessment report. Prof. Azevedo has received the World Economic Forum’s “Young Scientists under 40” award in 2014, and the C3E Women in Clean Energy Research Award in 2017.
- Sergi, B.J. , Adams, P.J., Muller, N., Robinson, A.L., Davis, S.J., Marshall, J.D., Azevedo, I.L., (2020). Optimizing emissions reductions from the U.S. power sector for climate and health benefits, Environmental Science & Technology.
- Thind, M.P.S., Tessum, C.W., Azevedo, I.L., Marshall, J.D., (2019). Fine particulate air pollution from electricity generation in the US: health impacts by race, income, and geography. Environmental Science & Technology
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