Air Quality-Related Health Benefits of Energy Efficiency in the United

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The air quality-related health benefits of energy efficiency in the United States David Abel, Tracey Holloway, Javier Martínez-Santos, Monica Harkey, Madankui Tao, Cassandra Kubes, and Sara Hayes Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06417 • Publication Date (Web): 05 Mar 2019 Downloaded from http://pubs.acs.org on March 14, 2019

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Environmental Science & Technology

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The Air Quality-Related Health Benefits of Energy Efficiency in the United States.

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David W. Abel1,*, Tracey Holloway1,2, Javier Martínez-Santos1,3, Monica Harkey1, Madankui Tao1, Cassandra Kubes4, Sara Hayes4

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* Corresponding Author Email: [email protected], 1710 University Ave., Madison, WI 53726, USA

Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin – Madison, Madison, WI 53726, USA Department of Atmospheric and Oceanic Sciences, University of Wisconsin – Madison, Madison, WI 53706, USA Department of Mechanical Engineering, University of Wisconsin – Madison, Madison, WI 53706, USA American Council for an Energy-Efficient Economy, Washington, DC 20045, USA

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February 4, 2019

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Abstract

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While it is known energy efficiency (EE) lowers power sector demand and emissions, study of the

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air quality and public health impacts of EE has been limited. Here we quantify the air quality and

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mortality impacts of a 12% summertime (June, July, and August) reduction in baseload electricity

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demand. We use the AVoided Emissions and geneRation Tool (AVERT) to simulate plant-level

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generation and emissions, the Community Multiscale Air Quality (CMAQ) model to simulate air

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quality, and the Environmental Benefits Mapping and Analysis Program (BenMAP) to quantify

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mortality impacts. We find EE reduces emissions of NOX by 13.2%, SO2 by 12.6%, and CO2 by

ACS Paragon Plus Environment


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11.6%. On a nationwide, summer average basis, ambient PM2.5 is reduced 0.55% and O3 is reduced

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0.45%. Reduced exposure to PM2.5 avoids 300 premature deaths annually (95% CI: 60 to 580)

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valued at $2.8 billion ($0.13 billion to $9.3 billion), and reduced exposure to O3 averts 175 deaths

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(101 to 244) valued at $1.6 billion ($0.15 billion to $4.5 billion). This translates into a health

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savings rate of $0.049/kWh ($0.031/kWh for PM2.5 and $0.018/kWh for O3). These results

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illustrate the importance of capturing the health benefits of EE, and its potential as a strategy to

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achieve air standards.


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Introduction

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In the U.S., over 40% of the population lives in counties violating one or more of the health-based

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air quality standards known as the National Ambient Air Quality Standards (NAAQS)

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(https://www3.epa.gov/airquality/greenbook/popexp.html). Controls on power plant emissions

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have been a significant driver of emissions reductions since the passage of the Clean Air Act in

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1970. NOX emissions from electricity generating units (EGUs) have decreased 76% since 1970,

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peaking around 1980 at over six times current levels (https://www.epa.gov/air-emissions-

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inventories/air-pollutant-emissions-trends-data). SO2 emissions have seen an even more dramatic

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92% decrease since 1970. Historically, power-sector controls have been primarily end-of-pipe

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technologies to limit emissions of pollutants such as nitrogen oxides (NOX), sulfur dioxide (SO2)

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and particulates. However, these technologies are expensive, with capital costs for NOX and SO2

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controls in the tens of millions of dollars for small power plants and hundreds of millions for large

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power plants, plus considerable operation and maintenance costs 1.

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A 2013 study by Fann et al. found that EGUs were the leading contributor to deaths from exposure

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to ozone (O3) and fine particulate matter (PM2.5) in 2000 (approximately 38,000 deaths). In 2016

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projections, however, EGUs dropped to 4th highest contributor to death from O3 and PM2.5

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(approximately 17,000 deaths)2. While reductions so far are impressive, EGUs remain a major

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source of health-damaging emissions. Increasing temperatures increase air conditioning use, this

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increases electricity demand, which in turn increases power plant emissions 3, and this may play a

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larger role in public health as air conditioning demand increases under a warmer climate 4.

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Although end-of-pipe controls have been effective at removing reactive pollutants from the waste

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stream of EGUs, there are no analogous technologies for limiting carbon dioxide emissions (CO2).

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In fact, traditional controls effectively decrease the efficiency of a power plant through a heat rate



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penalty and increase CO2 emissions rate 1. Clean energy production has been identified as an

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alternative to traditional controls that would offer similar air quality benefits by displacing

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emissions from traditional power plants, while also decreasing emissions of CO2 5–9. This area of

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research also includes a large body of literature devoted to the air quality co-benefits of climate

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change mitigation policy and interventions 10–19.

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Many energy efficiency measures are cost-effective or cost-negative greenhouse gas mitigation

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strategies, and as such are often preferable to alternatives

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efficiency extend beyond energy cost savings to include energy security, job creation, reduced

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water use, consumer comfort, and improved fire safety

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described as non-energy benefits (NEB). While these NEB have been demonstrated, many states

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do not include NEB in cost-benefit analyses

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efficiency are likely to be the most high-value NEB 15.

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A large body of research has explored the complex relationship between energy efficiency and

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indoor air quality 23–25. Likewise, a large body of research has characterized the health impacts of

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ambient air quality 2,26–29. However, only a few studies have quantified parts of the health impacts

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of ambient air quality changes attributable to energy efficiency

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energy efficiency could also help U.S. states and counties meet air quality standards set by the

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U.S. Environmental Protection Agency (EPA) under the U.S. Clean Air Act. The EPA has

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provided guidance to states on incorporating renewable energy and energy efficiency (RE/EE) into

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State Implementation Plans (SIPS) for air quality management 36,37. States create SIPS when they

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are in violation of standards for one or more of the six criteria pollutants regulated under the

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NAAQS, also known as being in non-attainment. To date, RE/EE strategies have rarely been

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. Further, the benefits of energy

21,22

. These additional benefits are often

. The air quality and health benefits of energy


7,30–35

. By supporting clean air,

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included in SIPS. A major barrier to the integration of energy and air quality planning is the

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difficulty in quantifying the benefits of RE/EE strategies.

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Only a few studies have examined the air quality impact of demand-side measures and several

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only quantify emissions benefits or are focused on indoor air. Overall, these studies have tended

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to focus on the benefits of specific efficiency measures or programs such as residential insulation

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of energy efficiency and renewable energy scenarios ranging from $14 to $170 per MWh by

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linking detailed electricity dispatch with a statistical representation of air quality impacts based on

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six locations in the Pennsylvania-New Jersey-Maryland interconnection and four different

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renewable energy and energy efficiency installations 7.

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The potential benefits of energy efficiency to carbon mitigation, air quality and public health are

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understood in concept, but interdisciplinary modeling is required to quantify these benefits. This

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study is the first to quantify the ambient air quality and health benefits of a nationwide and non-

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measure specific U.S. energy efficiency scenario meant to emphasize the potential role of energy

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efficiency using freely-available EPA tools for regulatory analysis and to provide heuristics for

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simple analyses. This study builds on analysis of the benefits of energy efficiency for achieving

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air quality standards from the American Council for an Energy-Efficient Economy (ACEEE)

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which used reduced-form models to consider the same scenario analyzed here 38. These analyses

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consider the current impacts of EE on air pollution and other efforts to reduce emissions in the

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power sector and changes to future chemistry, climate and emissions would impact the benefit of

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EE on future pollutant concentrations.

, LEED buildings 33, cool roofs 31, and an empirical study of early demand-side management

. Buonocore et al. demonstrate the air quality-related public health and climate-related benefits



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Here we consider energy efficiency resulting in a constant load reduction throughout the year, but

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chemical transport modeling is only performed for the summer months of June, July and August

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due to computational expense. Such measures could include more energy efficient appliances,

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electronics, and lighting, as well as energy storage combined with efficiency measures to reduce

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building cooling, such that the net reduction in demand were distributed evenly over time. This

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approach represents a reasonable look at energy efficiency benefits, recognizing that additional

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efficiency measures targeting cooling loads could target peak generation as well as baseload. The

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methods are described in detail in the following section. Results are included separately for

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emissions analysis, air quality simulations, and health outcomes, and the discussion contextualizes

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results within ongoing efforts across energy efficiency, air quality management, and public health.

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Methods

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To analyze the impact of energy efficiency on EGU emissions, air quality and health, we follow

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an analysis structure shown graphically in Figure 1. Two scenarios are compared using a linked

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system of interdisciplinary models. The first scenario represents the current (2016) U.S. power

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system, without additional energy efficiency (NoEE). The second scenario includes energy savings

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of 348 TWh annually (or 91 TWh decrease in generation over the summertime) from energy

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efficiency, applied as a constant energy demand reduction over the continental U.S. study region.

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348 TWh annually totals 15% of demand whereas a 91 TWh reduction in the high-demand summer

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months totals only 12% of demand. A 15% annual reduction in energy demand is the maximum

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change within which AVERT is recommended for use

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demand than the annual average, a 15% annual reduction applied as a constant hourly reduction

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results in 11.9% average electricity demand reductions over the summer. This scenario is thus

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referred to as the energy efficiency scenario (EE) or 12% EE scenario. Demand reductions in EE

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. As summer months exhibit higher


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on an annual basis by region are given in the caption of Figure S1. We focus on the summer, when

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electricity demand and O3 are typically highest across the U.S. and sulfate contributes a greater

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fraction to PM2.5 40. Thus, we expect the impact of electricity measures to be greatest during the

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summer.

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To simulate electricity grid behavior and associated EGU emissions we use the U.S. EPA’s

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AVoided Emissions and geneRation Tool (AVERT) for 2016.39 AVERT uses a statistical

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representation of emissions monitoring data from the Clean Air Markets Database to simulate

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displacements in generation and emissions under proposed scenarios. This statistical

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representation of historical emissions simulates power plant dispatch but does not explicitly

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represent least-cost dispatch nor transmission. AVERT divides the U.S. into ten regions simulated

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separately, (as shown in Figure S1), based on sub-regions from EPA’s Emissions & Generation

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Resource Integrated Database (eGRID). We run AVERT for all ten regions for the year 2016, the

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most up-to-date year available at the time of the study. As described above, we apply a constant

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reduction in energy demand to represent an increase in baseload energy efficiency. AVERT then

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calculates hourly generation and emissions of NOX, SO2, CO2, and primary PM2.5 with and without

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energy efficiency. This approach has been shown to be appropriate for modest changes in demand

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as applied here and has been used in related work 39.

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To evaluate the skill of AVERT in capturing energy system change, we utilize benchmarking

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analysis performed by Synapse Energy Economics Inc. and compare the base emissions

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simulations vs. measured emissions reported in the Air Markets Program database. In a robustness

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test, AVERT the variability within the benchmark model was the same order of magnitude, and

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for some metrics exceeded the variability between models. AVERT performed very well in

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capturing generation and CO2 emissions in benchmarking, generally within 5% for a



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Massachusetts test case but underestimated SO2 by 14% to 205% and overestimated NOX by 12%

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to 60%. However, given the robustness test and limited geographic range of the benchmarking

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test, Synapse finds AVERT is no less equipped to calculate displaced generation and emissions

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than the benchmarking model. In comparison to Air Markets Program data, AVERT captures

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baseline measured emissions well, overestimating SO2 emissions by 7.25% and underestimating

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NOX emissions by 1.73% in the NoEE scenario (Figure S5).

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EGU emissions of NOX and SO2 from AVERT for the base-case and energy efficiency scenarios

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are input to the EPA’s Community Multiscale Air Quality (CMAQ) model v5.0.1 run with the

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carbon bond 05 (CB05) chemical mechanism and “AERO6” aerosol chemistry to simulate ambient

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air quality on a 12 x 12 km grid of the continental U.S. with 25 vertical layers of the atmosphere.

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Meteorology is supplied for 2011 conditions as simulated with the Weather Research and

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Forecasting (WRF) model prepared for CMAQ with the Meteorology Control Interface Processor

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(MCIP). Non-EGU emissions are provided by the 2011 National Emissions Inventory (NEI), the

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most recent available emissions at the time of analysis. Biogenic emissions are provided by the

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Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 for 2011

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conditions. EGU locations and NOX and SO2 emissions are provided by AVERT and represent

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2016 conditions. Emissions of NOX are assigned constant partitioning of 85% NO and 15% NO2.

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Boundary conditions are provided by monthly averages of simulations from the Model for Ozone

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and Related chemical Tracers (MOZART)

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changes in fuel mix and technological controls that are highly influential in determining the impact

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of energy efficiency on emissions and pollutant concentrations, but 2011 conditions are used for

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the other sectors as the most recently available for modeling. This presents a limitation in that

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interannual changes to meteorology and non-EGU emissions trends will impact results, but the

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. 2016 power plant emissions are used to reflect


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focus of this study on the difference between two cases where all is held constant except EGU

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emissions limits any potential error.

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Emissions of EGU pollutants not provided by AVERT are taken as a national average from the

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2011 NEI and distributed evenly amongst AVERT power plants regardless of fuel type and

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scenario. These pollutants are listed in Table S1, and this method could slightly impact geographic

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distribution of these pollutants not assessed by AVERT but does not have a significant impact on

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PM2.5 and O3 concentrations. While AVERT calculates primary PM2.5 emissions, this information

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lacks the chemical partitioning needed as input to CMAQ. Therefore, calculated impacts are

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changes to secondary PM2.5, not including changes to primary PM2.5 emissions. EGUs in 2017

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emitted only 3% of U.S. primary PM2.5 which in turn only makes up a fraction of total, ambient

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PM2.5

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CMAQ performance is within expectations showing underestimates of PM2.5 (mean bias of -4.57

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µg/m3) and overestimates of O3 (mean bias of 9.85 ppbv) compared to Air Quality System (AQS)

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monitors. A detailed evaluation is included in the supplementary material. In addition, the impact

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of raw biases is diminished because results are most dependent on the difference between NoEE

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and EE simulations rather than their absolute concentrations. For O3, where absolute CMAQ

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values are used, ambient concentrations are adjusted for bias with monitors. This process is also

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discussed in the supplementary material.

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Simulated surface-level ambient concentrations of MDA8 O3 and daily average PM2.5 from CMAQ

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are used as input to the U.S. EPA’s Environmental Benefits Mapping and Analysis Program –

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Community Edition (BenMAP-CE v1.3) to calculate mortality and morbidity benefits 42. BenMAP

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is a GIS-based tool to combine gridded air pollution data with health outcome incidence data,

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population, and health impact functions. Population data is based on U.S. census data. Baseline

(https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data).



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incidence data comes from a variety of sources, but predominantly from the Centers for Disease

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Control and Prevention (CDC), as outlined in Appendix D of the BenMAP user manual 43. We use

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standard EPA configurations for mortality and asthma exacerbation in children based on O3 and

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PM2.5 exposure available with the BenMAP software. Each impact function is run in a 5,000-

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member Monte-Carlo ensemble to quantify uncertainty of outcomes and valuation. Calculations

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for asthma exacerbation are based on two studies for O3 and two studies for PM2.5 pooled together

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by pollutant using EPA standard configurations

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BenMAP value of statistical life derived from 26 studies with a mean of $6.3 million per avoided

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mortality. Here we present results from each individual mortality function and discuss the average

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of results with equal weighting across fourteen functions for mortality from all causes for PM2.5

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and three functions for O3

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each pollutant.

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We take a conservative approach to analyzing the annual impacts of air quality changes based on

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summer modeling results. Therefore, air pollution exposure is assumed to only change during the

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three summer months simulation, and nine months are assumed to have no change in air quality or

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health. BenMAP performs PM2.5 concentration-response functions on annual average PM2.5

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values. BenMAP internally calculates this annual average based on provided daily averages. In

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this assessment, the daily average for non-summer months is given as the NoEE summer average

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in both the NoEE and EE cases. Therefore, the difference between cases is based exclusively on

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summer months, but the overall annual average includes realistic data for non-summer months that

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does not change between cases. The same process is followed for ozone except ozone

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concentration-response functions utilize MDA8 O3 values provided for each day. This assumption

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offers a lower bound to potential benefits, given that any investment in energy efficiency would

48–50

43–47

. Mortality valuation is based on a standard

. Figures are based on median mortality results across studies for


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be expected to lower pollutant concentrations year-round. To assess this discrepancy, we include

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an annual mortality estimate in the supporting information using simplified methods. BenMAP

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results presented here are annual PM2.5- and O3-related mortality changes from energy efficiency

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based on reduced exposure in the summer months of June, July, and August only. Valuation of

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mortality results follows standard EPA methods using a combination of willingness to pay and

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cost of illness, (or value of statistical life), methods based on 23 studies. All costs are given as

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2015 US dollars.

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Limitations of the methods used here include the regional basis of applied energy efficiency. One

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difficulty in implementing energy efficiency for air quality improvement is that the benefits of EE

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may accrue away from where energy saving measure are applied due to the interconnectedness of

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the electricity grid across political boundaries. By using only ten regions, AVERT is only as

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specific as the region-level. In addition, the constant, baseload energy demand reduction is

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advantageous in its simplicity and generality but would likely not reflect the true demand profile

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change from energy efficiency policies and practices. In addition, the use of 2011 non-EGU

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emissions and meteorology and the nature of analyzing the current energy system does not account

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for changes and variabilities that may influence results over the time implementing 12% energy

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efficiency might take such as growth of low or zero emission energy technologies, variability is

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meteorology, and changes to other sectors. While the summer focus of CMAQ modeling is

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appropriate for ozone in most regions, it is a limitation for PM2.5, which has different sources and

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chemical processes throughout the year. To contextualize summertime simulations, a reduced-

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form approach is used to relate summertime to annual average PM. Many of these issues reflect

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the opportunities and limitations of large-scale energy and air quality modeling consistent with

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regulatory analysis under the US Clean Air Act.



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Results and Discussion

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Emissions

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We find that an annual 15% reduction in electricity demand (348 TWh annually), distributed

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evenly across the year, results in summertime (June, July and August) decrease in generation of

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91 TWh or 12%. This is because summer shows higher electricity demand and, thus a constant

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hourly energy reduction has a lower percentage reduction over times of high demand

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(summertime). This reduction in summertime electricity production yields a nationwide 13.2%

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(44.8 thousand tonnes) decrease in NOX emissions from the power sector and 12.6% (56.2

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thousand tonnes) decrease in SO2 emissions over the summer. CO2 emissions are reduced by 64.5

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Mt (11.6%). On an annual basis, avoided CO2 emissions are 258.5 Mt. Using $42/tonne, the central

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estimate of the social cost of carbon in 2020 from a U.S. Interagency Working Group under

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Executive Order 12866, these avoided emissions have a value of $10.9 billion in 2007 dollars 51.

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On a per energy basis, efficiency has an emissions savings rate of 0.49 kg/MWh for NOX, 0.58

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kg/MWh for SO2, and 0.72 tonnes/MWh for CO2. These metrics are displayed by state for the

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contiguous U.S. in Table 1 and graphically in Figures S2-S4.

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Emissions decreases, on a mass basis, remain approximately constant throughout the day,

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consistent with the study design, focused on baseload electricity demand. Displacement of NOX

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emissions remain ~250 tonnes/hour, but percentage reductions on average throughout a day range

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from just under 12% to nearly 16.5%. Similarly, SO2 displacement remains steady (~180

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tonnes/hour) and ranges from 8% to 20%. The diurnal and summer profiles of emissions reductions

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are shown in Figures S6 and S7. The greater range in displaced SO2 by percentage is emblematic

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of the role that individual power plants play in SO2 emissions, especially large baseload coal plants


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that may be displaced at off-peak times of the day and peaking plants during peak hours. Emissions

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reductions exhibit expected behavior for the energy efficiency scenario applied.

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Figure 2 shows electricity generation and emissions by power plant and fuel type. These figures

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are limited to power plants with measurable emissions (coal, gas, oil) and do not include

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production from solar, wind, and nuclear. Electricity generation in the base case is dominated by

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gas (50.1%) and coal (48.5%), while emissions are driven mostly by coal power plants (67.6% of

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CO2, 80.4% of NOX, and 98.7% of SO2). Displaced emissions follow similar trends. 55.6% of

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generation is displaced at gas-fired plants while 42.5% is displaced at coal plants. 59.4% of CO2,

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65.7% of NOX, and 96.9% of SO2 is displaced at coal power plants.

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State-level summer-total displacements range from 3.9 to 25.4% for CO2, 4 to 31.8% for NOX and

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5.9 to 33.7% for SO2. In all states, energy efficiency reduces emissions, although in some states

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these improvements round to zero absolute reduction. The largest savings accrue in Texas for all

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pollutants; while smaller states and states with fewer coal power plants tend to see the total smallest

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displacements (these small changes may still be high on a percentage basis).

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Air Quality

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On a summer and nationwide average, decreases in emissions of NOX and SO2 lower

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concentrations of O3 and PM2.5. Local and regional air impacts depend on meteorology and

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chemistry as calculated using CMAQ. The model calculates secondary production of O3 from NOX

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and volatile organic compounds, as well as secondary production of nitrate and sulfate PM.

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Figures 3 and 4 show the summer-average (June, July and August) concentrations of PM2.5 and

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the 4th highest maximum daily 8-hr average (MDA8) O3 as well as the percent change in

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concentrations in the EE scenario. Figure S8 shows the changes on a physical unit basis. On



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average, summer PM2.5 concentrations over land decrease 0.55% (0.022 µg/m3), relative to

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baseline summer-average PM2.5 concentrations averaging 3.11 µg/m3 (grid-average concentrations

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range from 0.787 to 38.08 µg/m3). On average, MDA8 O3 decreases 0.45% (0.313 ppbv) relative

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to the baseline average of 66.56 ppbv (grid-average concentrations range from 29.47 to 125.29

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ppbv). The greatest pollutant reductions, up to 1.5% for both pollutants, accrue near and downwind

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from coal power plants in the mid-Atlantic states. CMAQ calculates potential O3 increases in parts

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of the West Coast due to localized increases in NOX emissions from some power plants as modeled

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by AVERT.

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To assess the potential for energy efficiency to meet air quality standards, Figure 5 shows summer

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days above the 2015 NAAQS value of 70 ppb mapped to county-level by selecting the grid with

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the maximum MDA8 O3 in each county and correcting for model bias as described in the

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supplementary material. These values do not reflect formal EPA designations, which are based on

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three years of monitoring data. However, they are broadly consistent with 2018 final designations

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for counties under the 2015 O3 standard (shown in Figure S9). Basing county-wide design values

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on the maximum grid concentration within that county as well as including counties without

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ground monitors explains the higher number of quasi-non-attainment counties measured by the

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model as compared to 2018 designations.

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In our simulations for a three-month summer, 725 counties have four or more days with MDA8

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O3 above 70 ppb. We will refer to these here as quasi-non-attainment counties, recognizing the

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inconsistency between our model simulation and the actual regulatory metric. Of these quasi-non-

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attainment counties, 180 are within one or two days of achieving the standard and an additional

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258 are within 7 days of achieving the standard. The energy efficiency scenario examined here

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lowers O3 concentrations such that 45 quasi-non-attainment counties (6.2%) have fewer than four


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days above the 70 ppb MDA8 O3 limit. These results suggest that 12% baseload energy efficiency

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measures could help actual non-attainment counties achieve attainment with the O3 NAAQS. Table

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S2 shows the number of counties by state where model-based designations are flipped from quasi-

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non-attainment to quasi-attainment.

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For PM2.5, fewer counties are out of attainment with the NAAQS, and the benefits of energy

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efficiency are less pronounced. The potential health benefits of reduced PM2.5, even in attainment

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counties, are discussed in the next section.

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Health

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Reductions in concentrations of PM2.5 and O3 benefit public health by reducing premature

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mortality and other adverse health outcomes. Here we use BenMAP to quantify avoided premature

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mortality and asthma exacerbations in children due to air quality improvements associated with

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energy efficiency. Benefits accrue most in states showing the greatest reductions in PM2.5 and O3

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concentrations combined with high population exposure (compare with Figures 3 and 4). Figures

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6 and 7 show graphically the state-level mortality benefits of energy efficiency based on the

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median health-response functions assessed; Expert J for PM2.5 and Bell et al for O3.26,52

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Reduced exposure to PM2.5, calculated as an average across the 14 health impact functions, reduces

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premature mortality by 300 deaths per year (95% CI: 60 to 580). The range of mean results from

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all studies is 27 to 593 deaths for PM2.5. Reduced exposure to O3, calculated as an average across

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the 3 health impact functions, reduces premature mortality by 173 deaths per year (95% CI: 101

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to 244). The range of mean results from all studies is 109 to 240 deaths for O3. These reductions

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are conservative, because only changes in exposure during the three summer months are

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considered. In the supplemental information, annual estimates are calculated using a simplified



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approach based on EPA’s benefits-per-ton study shown in Table S3. PM2.5 summer impacts are

325

expected to total 30% of annual impacts. Therefore, total annual PM2.5 mortalities are estimated to

326

total 1000 deaths. Mortality impacts from all causes are shown by study for all studies in standard

327

EPA BenMAP configurations in Table S4. In addition, avoided asthma exacerbations in children

328

ages 6 to 18 are quantified nationally as 123,000 (95% CI: -106,000 to 299,400) cases caused by

329

O3 and 4,600 (95% CI: -270 to 10,500) caused by PM2.5 for a mean total of 127,600 cases annually

330

based on summer exposure. This equates to a total of 1.4 cases of asthma avoided per GWh of

331

electricity saved.

332

Using a statistical value of life based on 23 studies BenMAP quantifies the monetary value of

333

avoided premature mortality as $2.8 billion (95% CI: $0.13 to $9.3 billion) based on PM2.5

334

exposure (estimated $9.3 billion annually – range of $0.43 to $31 billion) and $1.6 billion (95%

335

CI: $0.15 to $4.5 billion) based on O3 exposure. Given that the total summertime energy savings

336

for this scenario is 91 TWh, the value of reduced PM2.5-related mortality is $0.031/kWh and the

337

value of reduced O3-related mortality is $0.018/kWh for O3-related mortality, or a combined

338

$0.049/kWh with an uncertainty range of $0.003/kWh to $0.15/kWh. For context, electricity costs

339

averaged $0.110/kWh for all sectors in the U.S. in July 2018.53 Therefore, the ambient air benefits

340

of energy efficiency average 25% of the cost of electricity for PM2.5 benefits, and 16% for O3

341

benefits.

342

Discussion

343

To date, air quality control strategies in the U.S. have relied most heavily on technological controls,

344

such as desulfurization, electrostatic precipitators and selective catalytic reduction. The U.S. has

345

invested greatly in these technologies in the past, with profound health benefits (estimated as 30:1

346

by EPA).54 Although these technologies are highly effective at limiting pollutant emissions, they


Page 16 of 31

Page 17 of 31


347

are both expensive and do not address CO2 emissions. In fact, some controls can increase power

348

plant CO2 emissions by decreasing plants’ efficiencies. Energy efficiency and renewable energy

349

offer great potential as complementary strategies to meet both climate and public health goals. In

350

our model results, over 6% of counties calculated as quasi-non-attainment would be brought into

351

quasi-attainment through the implementation of efficiency measures considered here.

352

Energy efficiency programs are often evaluated for effectiveness to claim savings in cost-benefit

353

analyses. However, the health benefits of avoided air pollution from energy efficiency are rarely

354

included. The monetized value of health savings from energy efficiency ($0.049/kWh calculated

355

here) offers a simple measure to integrate health benefits into energy decision-making. Both the

356

air management and the public health communities stand to gain from incorporating the cost-

357

effective benefits of energy efficiency. Such analyses would also ensure that full benefits of energy

358

efficiency are accounted for when assessing energy investments, technologies, and programs.

359

Past studies have tended to examine specific energy efficiency measures such as residential

360

insulation or energy efficiency as part of larger policies which makes direct comparison difficult.

361

However, Buonocore et al. examines comparable energy efficiency and renewable energy

362

scenarios for the PJM interconnect and find benefits of $14/MWh to $170/MWh 7. MacNaughton

363

et al. finds energy savings of 51.8 TWh and mortality savings of 165 to 370 or total health savings

364

of $4.35 billion from U.S. LEED buildings from 2000 to 2016

365

translated to $84/MWh in health savings. Both of these studies are similar to our findings of

366

savings from $3/MWh to $150/MWh with a central estimate of $49/MWh. Levy et al. also

367

quantifies the benefit of residential energy efficiency as $49 per ton of CO2. Translating the results

368

here to this metric would total $68 per ton which is within the Levy et al. uncertainty range of $12

369

to $390. In addition, using reduced-form modeling of the scenario analyzed here including AVERT


33

. Therefore, results can be


370

and the Co-Benefits Risk Assessment model, ACEEE has found up to $20 billion in avoided

371

annual health harms from 348 TWh of demand savings which totals $57/MWh and is very similar

372

to findings here 38. Additional reduced-form modeling for regulatory or scientific purposes could

373

offer a useful extension of these findings.

374

Acknowledgements

375

This work was completed with generous funding from the George Bunn Wisconsin Distinguished

376

Graduate Fellowship in Energy Analysis and Policy (support for DA), the Office of the Vice

377

Chancellor for Research and Graduate Education (OVCRGE) at the University of Wisconsin—

378

Madison (TH), and the University of Wisconsin – Madison’s Global Health Institute’s Graduate

379

Award (DA). Support for MH and TH was provided by the NASA Health and Air Quality Applied

380

Sciences Team (Grant # NNX16AQ92G). Support for JMS was provided by Bridge to the

381

Doctorate fellowship from the Wisconsin Louis Stokes Alliance for Minority Participation by the

382

National Science Foundation. Thank you to all public data sources and models used in this analysis

383

including AVERT, CMAQ, and BenMAP as well as the Air Markets Program Database from the

384

U.S. EPA.

385

Supporting Information

386

A single supporting information file is included with text regarding CMAQ model performance

387

and annual PM2.5 mortality estimates plus figures and tables to support the main text.

388 389 390


Page 18 of 31

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391

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554


Figures

555 556 557 558

Figure 1: The methodological framework used in this study to analyze the air quality and health impacts of a 12% summertime energy efficiency scenario (15% energy efficiency on an annual basis). AVERT is the AVoided Emissions and geneRation Tool.

559 560 561 562 563 564 565 566 567 568 569 570 571 572



State

Displaced Generation

Page 24 of 31

TWh (%)

Displaced NOX Emissions tonnes (%)

Displaced NOX Rate (Base Rate) kg/MWh

Displaced SO2 Emissions tonnes (%)

Displaced SO2 Rate (Base Rate) kg/MWh

Displaced CO2 Emissions ktonnes (%)

Displaced CO2 Rate (Base Rate) tonnes/MWh

AL

2.8 (10.4)

2000 (18.4)

0.7 (0.4)

1400 (16.7)

0.5 (0.31)

1800 (10.1)

0.63 (0.65)

AR

1.7 (13.1)

1200 (14.7)

0.7 (0.63)

2100 (14.7)

1.23 (1.09)

1300 (12.6)

0.73 (0.75)

AZ

3 (13.4)

1100 (10.6)

0.36 (0.45)

300 (8.8)

0.11 (0.16)

1700 (11.2)

0.57 (0.68)

CA

3 (12.6)

300 (10.3)

0.1 (0.13)

0 (11.4)

0 (0)

1300 (12.6)

0.44 (0.44)

CO

1.8 (14)

800 (9.9)

0.44 (0.63)

500 (10.3)

0.3 (0.41)

1300 (12.2)

0.73 (0.84)

CT

0.3 (6.7)

100 (16.4)

0.51 (0.21)

0 (26.4)

0.16 (0.04)

200 (7.4)

0.53 (0.48)

DE

0.4 (20.7)

100 (23.5)

0.23 (0.2)

0 (26.2)

0.11 (0.09)

300 (21.4)

0.64 (0.62)

FL

7 (12)

2600 (16.9)

0.38 (0.27)

6200 (24.1)

0.88 (0.44)

4100 (12.2)

0.58 (0.57)

GA

4.1 (14)

1000 (18)

0.24 (0.19)

1000 (18.5)

0.25 (0.19)

3100 (15.1)

0.75 (0.7)

IA

1.4 (15.7)

1000 (15.8)

0.72 (0.72)

1200 (13.7)

0.85 (0.97)

1200 (14.8)

0.88 (0.93)

ID

0.2 (20.3)

0 (31.8)

0.11 (0.07)

0 (22.6)

0 (0)

100 (25.4)

0.46 (0.37)

IL

4 (16.1)

1800 (17.8)

0.46 (0.42)

2700 (13.7)

0.69 (0.81)

3100 (14.4)

0.8 (0.89)

IN

2.7 (10)

2600 (11.3)

0.95 (0.85)

2800 (10.4)

1.03 (0.99)

2400 (9.9)

0.89 (0.91)

KS

1.1 (12.6)

700 (14.8)

0.67 (0.57)

300 (11.2)

0.23 (0.26)

1000 (11.6)

0.92 (0.99)

KY

2.2 (9.7)

1400 (9.5)

0.65 (0.67)

1900 (9.1)

0.85 (0.91)

1900 (9.1)

0.86 (0.93)

LA

1.9 (8.9)

2200 (18.9)

1.13 (0.54)

1600 (12)

0.82 (0.61)

1300 (10.3)

0.69 (0.6)

MA

0.7 (10.9)

100 (14)

0.15 (0.12)

100 (12.5)

0.09 (0.08)

400 (11.2)

0.52 (0.51)

MD

1.5 (20.9)

800 (26)

0.52 (0.41)

1100 (19.5)

0.75 (0.8)

1200 (19.4)

0.79 (0.85)

ME

0.2 (16.9)

0 (17.4)

0.08 (0.08)

0 (26.1)

0.18 (0.12)

100 (18.4)

0.45 (0.42)

MI

2.1 (10.8)

800 (9.3)

0.4 (0.46)

1700 (9.1)

0.82 (0.98)

1500 (10.2)

0.74 (0.79)

MN

1.4 (13.8)

500 (10.5)

0.35 (0.46)

300 (6.7)

0.2 (0.42)

1000 (11.2)

0.68 (0.84)

MO

2 (10.2)

1400 (9.2)

0.69 (0.77)

2500 (9)

1.24 (1.4)

1700 (9.2)

0.82 (0.91)

MS

1.6 (10.9)

1000 (26)

0.63 (0.26)

0 (25.4)

0.02 (0.01)

900 (13)

0.59 (0.49)

MT

0.3 (8.3)

300 (8.1)

0.89 (0.9)

300 (11.3)

0.79 (0.58)

400 (8.1)

1.02 (1.05)

NC

3.1 (14)

1300 (16.4)

0.42 (0.36)

900 (13.5)

0.28 (0.29)

2300 (15.5)

0.76 (0.68)

ND

0.4 (5.9)

400 (4.9)

0.98 (1.17)

700 (5.9)

1.56 (1.57)

400 (5.6)

1.02 (1.08)

NE

0.7 (10.1)

600 (11.8)

0.95 (0.81)

1200 (8.9)

1.8 (2.04)

600 (9.5)

0.89 (0.94)

NH

0.2 (13.3)

100 (14.8)

0.31 (0.28)

100 (23.1)

0.23 (0.13)

100 (10.3)

0.57 (0.73)

NJ

1.7 (13.1)

400 (29.3)

0.23 (0.1)

0 (22.5)

0.01 (0.01)

800 (14.6)

0.51 (0.46)

NM

0.6 (6.8)

600 (5.7)

1.05 (1.24)

100 (5.9)

0.22 (0.25)

400 (5.8)

0.71 (0.83)

NV

0.9 (10.9)

400 (25.2)

0.42 (0.18)

300 (33.7)

0.35 (0.11)

600 (14)

0.61 (0.48)

NY

2.3 (12.2)

800 (19.2)

0.35 (0.23)

400 (21.5)

0.19 (0.11)

1200 (12.8)

0.52 (0.5)

OH

3.5 (11.6)

2000 (13.2)

0.58 (0.51)

6200 (15.4)

1.79 (1.35)

2700 (11.6)

0.79 (0.78)

OK

2.3 (13.6)

1500 (17.8)

0.63 (0.48)

1600 (10.8)

0.69 (0.87)

1500 (12.8)

0.62 (0.66)

OR

0.7 (14.6)

200 (25.5)

0.29 (0.17)

400 (28.3)

0.49 (0.25)

400 (17)

0.54 (0.47)

PA

3.6 (10.9)

2200 (12.4)

0.63 (0.55)

3800 (15.6)

1.05 (0.73)

2700 (11.7)

0.75 (0.7)

RI

0.3 (13.4)

0 (9.3)

0.05 (0.08)

0 (13.5)

0 (0)

100 (14)

0.5 (0.48)


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573 574 575


SC

1.5 (14)

500 (14.3)

0.32 (0.31)

400 (15.9)

0.26 (0.23)

1200 (13.9)

0.8 (0.81)

SD

0.2 (19.1)

100 (21)

0.42 (0.38)

0 (10.9)

0.13 (0.23)

100 (16.3)

0.74 (0.87)

TN

1.8 (13.3)

700 (12.5)

0.39 (0.41)

1000 (11.9)

0.54 (0.6)

1500 (13.1)

0.84 (0.85)

TX

11.1 (10.9)

4400 (13.4)

0.4 (0.32)

7700 (10.6)

0.7 (0.72)

7300 (10.5)

0.66 (0.69)

UT

0.9 (9.3)

800 (9.4)

0.86 (0.86)

200 (9.6)

0.28 (0.27)

700 (8.6)

0.74 (0.81)

VA

2.5 (14.7)

900 (17.6)

0.38 (0.31)

500 (20.3)

0.21 (0.15)

1500 (15.7)

0.6 (0.56)

VT

0 (4)

0 (4)

0.45 (0.45)

0 (6.8)

0.01 (0)

0 (3.9)

1.31 (1.33)

WA

1.1 (22.6)

500 (28.2)

0.46 (0.37)

200 (24)

0.14 (0.13)

800 (24.2)

0.7 (0.66)

WI

2 (13.7)

700 (15.8)

0.36 (0.31)

500 (14.6)

0.23 (0.21)

1600 (13)

0.8 (0.85)

WV

1.8 (8.8)

800 (6.8)

0.46 (0.59)

1200 (9.9)

0.67 (0.6)

1700 (8.5)

0.91 (0.94)

WY

0.9 (8.2)

800 (8.4)

0.85 (0.83)

700 (8)

0.76 (0.78)

900 (7.9)

1.01 (1.06)

Total

91.7 (11.9)

44800 (13.2)

0.49 (0.44)

56200 (12.6)

0.61 (0.58)

64500 (11.6)

0.7 (0.72)

Table 1: Displaced generation and emissions of NOX, SO2, and CO2 from energy efficiency by state and total throughout the contiguous U.S. and includes the emissions rates of displaced emissions and the base emissions rates prior to energy efficiency.

576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592



Displaced by EE

Base Case

a) Gen

b) NOX

c) SO2

d) CO2

593 594

Figure 2: Base case (left) and displaced (right) a) generation and emissions of b) NOX, c) SO2, and d) CO2 by power plant type and location summed over the summer. Note that “Other” only


Page 26 of 31

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595 596


includes emitting EGUs such as waste and biomass power plants but not emissions-free generation such as solar, wind and nuclear plants.

597 a) NoEE PM2.5 Surface Concentration (µg/m3)

598 b) Change in PM2.5 Concentration with Energy Efficiency (%)

599 600 601 602

Figure 3: Average summer PM2.5 surface concentrations in the a) NoEE scenario and the b) change in concentration from energy efficiency as a percentage.



603 604 a) NoEE O3 Surface Concentration (ppbv)

605 b) Change in O3 Concentration with Energy Efficiency (%)

606 607

Figure 4: O3 surface concentrations in the a) NoEE scenario and the b) change in concentration from energy efficiency as a percentage.

608


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609 a) NoEE MDA8 O3 Exceedance Days by County

610

# days (# counties)

b) Reduction in MDA8 O3 Exceedance Days by County with Energy Efficiency

611

# days (# counties)

612 613 614 615

Figure 5: O3 exceedance-days by county a) in the base case, and b) avoided through energy efficiency. Figures are adjusted for model bias based on measurements at AQS monitoring sites and interpolated between sites. Positive numbers in b) are number of exceedance days less with energy efficiency.



a) Annually Avoided PM2.5-Related Premature Mortalities by State from Energy Efficiency

b) Annual Savings from Avoided PM2.5-Related Premature Mortalities by State ($ million)

616 617

Figure 6: PM2.5-related a) avoided premature mortality, b) mortality savings valuation by state based on the median PM2.5 function in BenMAP, Expert J 50.


Page 30 of 31

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a) Annually Avoided O3-Related Premature Mortalities by State from Energy Efficiency (lives)

618 b) Annual Savings from Avoided O3-Related Premature Mortalities by State ($ million)

619 620 621

Figure 7: O3-related a) avoided premature mortality and b) mortality savings valuation by state based on the Bell et al. health impact function 10.

622 623 624 625 626


Air Quality-Related Health Benefits of Energy Efficiency in the United

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