Electrification, the adoption of electric end-use technologies instead of fossil-fueled alternatives, is a key strategy for economy-wide decarbonization. Beyond reducing CO₂ emissions, electrification also offers significant air quality improvements by reducing criteria pollutants like NOx and SOₓ, which contribute to ozone and fine particulate matter (PM₂.₅) formation. Monetized benefits of air quality improvements are substantial, offering immediate and localized benefits. However, uncertainties exist regarding the speed and scale of electrification, complex emission-air quality interactions, and the influence of other emission sources (fugitive dust, agriculture, solvents) on air quality. This study addresses these uncertainties by using a comprehensive modeling approach to assess the economy-wide impacts of electrification on CO₂ emissions and air quality in the United States.

Previous research has demonstrated the importance of electrification in decarbonization efforts, often highlighting its role alongside energy efficiency, carbon capture, and demand-side management. Studies have shown the potential of electrification to reduce criteria pollutants and improve air quality, particularly in the transportation and electricity generation sectors. However, the literature also points to uncertainties associated with the speed and extent of electrification, the complex interactions between emissions and air quality, and the impact of non-energy-related emission sources. Some studies have used simplified marginal emissions approaches which tend to underestimate the actual benefits of electrification. This study builds upon this existing literature by employing a more detailed and comprehensive modeling framework to provide a more accurate assessment of the co-benefits of electrification.

This study utilizes a coupled modeling approach combining the US-REGEN energy systems model and the CAMx air quality model. US-REGEN, a detailed model developed by EPRI, estimates CO₂ and criteria air emissions from various sources across the contiguous United States (CONUS) for different scenarios from 2015 to 2050. The model incorporates various factors, including power sector capacity planning and dispatch, end-use technology adoption rates, fuel prices, and policy interventions. Four scenarios were modeled: (1) 2035 Limited Electrification; (2) 2035 High Electrification without Carbon Price; (3) 2035 High Electrification with Carbon Price; and (4) 2050 High Electrification with Carbon Price. These scenarios explore the influence of electrification and carbon pricing on emissions and air quality. The model considers various end-uses (transport, buildings, industry) and generation sources. The output from US-REGEN (emissions by sector, source, fuel type, region) is then used as input for CAMx, a full-form photochemical air quality model. CAMx uses hourly meteorological data from 2016 for all scenarios, allowing for isolating air quality changes solely due to emissions variations. The model simulates ozone and PM₂.₅ concentrations across the CONUS. The study compares model results against different emission accounting approaches (REGEN observed, REGEN average emissions, constant average emissions, constant marginal emissions (annual and hourly)) to illustrate the underestimation of electrification benefits by simplified methods.

The study finds that electrification significantly increases electricity demand and its share of final energy. Across all scenarios, economy-wide CO₂ emissions decline, with the most significant reductions seen in the High Electrification with Carbon Price scenario (78% reduction relative to 2005 levels). The High Electrification with Carbon Price scenario is consistent with the U.S. Paris Agreement pledge. Electrification leads to substantial reductions in NOx, SO₂, and VOC emissions, primarily driven by reduced coal generation and cleaner vehicle fleets. However, PM₂.₅ reductions are more modest, as increases from non-combustion sources (fugitive dust, agriculture) offset some of the benefits from electrification. Air quality modeling results show significant ozone reductions across the CONUS, with the highest benefits in the Northeast, Southeast, and Ohio River Valley. Carbon pricing further amplifies ozone reductions, particularly in the Midwest and eastern Texas. PM₂.₅ reductions are also observed, but are limited by increases in primary PM₂.₅ from industrial and non-combustion sources. The study demonstrates that simplified emission accounting methods (marginal and average emissions) systematically underestimate the CO₂ reductions from electrification, compared to the results from the detailed integrated model.

The findings highlight the significant co-benefits of electrification for both climate change mitigation and air quality improvement. Electrification leads to immediate and localized air quality benefits, contrasting with the longer-term and geographically dispersed benefits of climate change mitigation. Deep NOx reductions are crucial for achieving substantial ozone improvements. However, the complex interplay of PM₂.₅ components necessitates addressing non-combustion sources to fully realize the air quality benefits of electrification. The detailed modeling approach used in this study is critical for accurately capturing the complex interactions within the energy and air quality systems. Simplified approaches tend to underestimate the benefits. The results emphasize the need for a holistic approach, incorporating both CO₂ reduction and targeted measures for non-combustion emission sources to meet both climate and air quality goals.

This study demonstrates the substantial co-benefits of electrification and decarbonization policies for improving air quality in the United States. While electrification significantly reduces CO₂ and various air pollutants, additional measures are needed to address non-combustion sources of PM₂.₅. The use of a detailed integrated modeling framework is crucial for accurately quantifying the impacts of electrification. Future research should investigate the impacts of stricter air quality standards, the integration of newer federal policies, community-level air quality assessments, and the translation of air quality changes into human health impacts.

The study's assumptions, such as the projections for economic activity, population growth, and service demands, could influence the results. The study's air quality modeling uses a single weather year (2016) which may not capture the full range of meteorological variability. Future changes in technology costs and policy interventions are also not explicitly considered beyond the scenarios modeled. While the model accounts for some policies, it doesn't fully incorporate all the latest policy proposals.