Impact of warmer weather on electricity sector emissions due to building energy use

Meier, P.J.; Holloway, Tracey; Patz, Jonathan A.; Harkey, Monica; Ahl, Doug; Abel, David; Schuetter, Scott; Hackel, S.

doi:10.1088/1748-9326/aa6f64

Cited by 17 publications

(19 citation statements)

References 31 publications

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“…The change in maximum CO 2 is not as large as the change in electricity production because additional capacity in mid-century (necessary to meet increased demand) is generated by natural gas power plants based on the US EIA’s Annual Energy Outlook, which emit less carbon than the current mix of generation sources [ 36 ]. We find that electricity production and emissions in the present-day exhibit a more uniform distribution than does temperature ( Fig 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…We selected the year 2069 from CCSM version 3 [ 74 ], downscaled with the WRF model in NARCCAP, and further downscaled with WRF for this study to our 12 km by 12 km study domain. This year was chosen as the warmest year from the mean model in the suite, as shown in S1 Fig , adapted from Meier et al [ 36 ]. To isolate the impact of climate change on air quality, we used the same 2011 emissions data and lateral boundary conditions for all simulations.…”

Section: Methodsmentioning

confidence: 99%

“…Present-day and warm mid-century meteorology were input to the RBESS, a modeling process developed following Schuetter et al [ 76 ] and used here to determine the response of building energy demand to meteorology. This process merges industry-standard building energy modeling techniques using the DOE-2 software (developed by James J. Hirsch & Associates and Lawrence Berkeley National Laboratory) and regional building stock data with the meteorology discussed above following Meier et al [ 36 ], which describes the methodology used here in detail. Building stock data were provided by the US EIA through the Commercial Buildings Energy Consumption Survey (CBECS), the Manufacturing Energy Consumption Survey (MECS), and the Residential Energy Consumption Survey (RECS).…”

Section: Methodsmentioning

confidence: 99%

“…Building energy demand was input to the MyPower model, a load duration curve (LDC) electricity dispatch model, used to simulate plant-level electricity production and emissions of NO X , SO 2 , and CO 2 . Detailed methodology for MyPower is described in Meier et al [ 36 ]. Data for power plant characteristics including heat rates and emissions rates were derived from NEEDS, a part of the US EPA’s Power Sector Modeling Platform, and modified to reflect data reported in the US EPA’s Clean Air Markets Database through 2013.…”

Section: Methodsmentioning

confidence: 99%

“…Power plants have been extensively evaluated as a controllable source of pollution [ 33 – 35 ]. However, without action, residential and commercial buildings are expected to see an increase in cooling load and subsequent emissions [ 36 , 37 ]. Recent research has demonstrated the air-quality-related health benefit of the green building movement and reducing energy demand in buildings.…”

Section: Introductionmentioning

confidence: 99%

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Air-quality-related health impacts from climate change and from adaptation of cooling demand for buildings in the eastern United States: An interdisciplinary modeling study

et al. 2018

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BackgroundClimate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario.Methods and findingsWe used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM2.5) concentrations by 58.6% (2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14 concentration–response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality.ConclusionsThis study exa...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%