Energy efficiency is essential to meeting the growing demand for clean energy reliably and affordably; however, understanding the impact and cost of making homes more energy-efficient is not always straightforward. The focus of this study - updated modeling of residential building shell, which is the outer structure or envelope of a home - adds significant new capabilities to EER’s treatment of residential energy demand. Where previously EER focused primarily on building equipment electrification (e.g., replacing gas furnaces and water heaters with electric heat pumps), this study takes a deep dive into building shell efficiency to analyze the cost and energy impact of upgrades (e.g., better windows, more insulation, less air leakage). While earlier modeling of building shell retrofitting was coarse and reliant on average savings values across building categories, the new approach takes advantage of the latest data from the National Renewable Energy Laboratory (NREL), allowing examination of the effects of retrofits in a more detailed way than previous studies. The result is a highly granular analysis of the impact of different building shell efficiency measures across building types (e.g., single-family, multifamily, mobile home), vintages, climate zones, and geographic locations.
The methods employed here were designed to yield data that are deeply granular and multifaceted. To create a more detailed picture of energy efficiency, we integrated two powerful data products from NREL into our EnergyPATHWAYS model. The 2024.1 release of the ResStock dataset represents the U.S. residential housing stock through 2.2 million modeled homes. NREL modeled the impact of over 200 different building energy upgrade packages on each of these representative units. Given the bespoke nature of building shell retrofits, we incorporated the unique costs for each modeled home using the National Residential Efficiency Measures Database (REMDB). To analyze the role of retrofits in achieving net-zero emissions, this study evaluated five different combinations of building shell upgrades. Two of the retrofit packages focused only on upgrading windows (to ENERGY STAR certified and thin triple-pane windows). The other three packages include a combination of different energy-saving building shell measures, ranging from light-touch upgrades to more extensive retrofits. The details of these individual measures and packages are laid out in Tables 1 and 2. Figure 1 shows the breakdown of average household costs per package and measure, across building vintages from pre-1940 to the 2010s.
Improving the modeling of residential building shells enables a preliminary screening analysis on retrofit feasibility and impacts even before running EER’s economy-wide model. Key findings include:
1. Window costs matter – a lot. The installation of more energy-efficient windows is the largest single component of retrofit package cost.
2. Heating vs. cooling. The energy savings from upgrades can vary depending on whether you’re trying to keep your home warm or cool. More savings are realized from homes that have greater need for heating.
3. Location, location, location. While the average cost of different retrofit packages ($/sqft) tend to be relatively similar across the U.S., the impacts on energy demand vary widely as a function of building location due to climate and building codes. Figure 2 illustrates the significance of building location for potential energy savings from retrofit measures.
4. Age matters too. Building vintage is a significant indicator of cost-effectiveness: simply put, older structures have more room to improve from a given efficiency upgrade, as illustrated in Figure 3.
We modeled the impact of these building upgrades in two scenarios, with both incorporating the improved granularity of input data across location, vintage, and building type. When each home is eligible for upgrading, the baseline case assumes the building is brought up to 2010 standards. A high building shell efficiency case assumes that each building is upgraded with an advanced energy efficiency retrofit package instead. This case uses the individual service demand reduction that was modeled for that specific unit in order to calculate the new heating and cooling demand.
Future versions of our model may focus on selecting retrofit packages that are the most cost-effective for a given set of consumers. Differences in cost-effectiveness, measured in $ per unit of energy saved per year, are significant across building types, ages, and retrofit packages. Generally, the light-touch and intermediate retrofits give the consumers the greatest “bang for their buck” in terms of energy saved for dollars spent. By incorporating subsidies available in the Inflation Reduction Act (IRA), which includes federal tax rebates for residential building efficiency, the cost of these upgrades can drop even further. While these rebates help lower costs across the board, they don’t necessarily change which package is the most cost-effective—the relative ranking only shifted for about 0.5% of the homes we modeled. Another way to view cost-effectiveness is to look at the payback period of a package – the time needed for savings on electricity bills to pay for the cost of the retrofit. Again, location and building vintage are significant factors in whether an efficiency retrofit makes sense economically (Figure 4.).
Our updated modeling results in a much clearer picture of the circumstances in which building shell energy efficiency retrofits can make a major contribution to decarbonization. By using high-resolution data from NREL, we have shown that while the costs of a given upgrade package is relatively similar across the U.S., the savings and cost-effectiveness of that package can vary widely depending on a home’s type, age, and location. This highlights the importance of choosing the right retrofit package as a consumer, and of targeting specific locations and vintages as a policy designer, especially when evaluating the potential savings from federal tax rebates.
Table 1. Residential building shell efficiency measures.
Name | Description |
ENERGY STAR Windows | Replace any less-efficient existing windows with windows that meet ENERGY STAR (v7) criteria. |
Thin Triple Windows | Replace any less efficient existing windows with thin triple-pane windows. |
Attic Floor Insulation | Increase attic floor insulation to IECC-Residential 2021 levels for dwelling units with vented attics and any lower level of insulation |
General Air Sealing | 30% reduction in infiltration (ACH50) for dwelling units with greater than 10 ACH50 in the baseline |
Duct Sealing | Duct sealing to 10% leakage and R-8 duct insulation for any leakier or less-insulated ducts |
Drill-and-fill Wall Insulation | Drill-and-fill wall insulation (R-13) for dwelling units with no wall insulation and wood stud walls |
Foundation Wall and Rim Joist Insulation | Add R-10 interior insulation to foundation walls and rim joists in conditioned basements and crawlspaces; seal crawlspace vents |
Exterior Continuous Wall Insulation | 1” exterior insulation to foundation walls and rim joists in conditioned basements and crawlspaces; seal crawlspace vents |
IECC 2021 Air Sealing | Improve dwelling unit infiltration to IECC 2021 air sealing requirements |
Roof Insulation | R-30 insulation for less-insulated finished attics and cathedral ceilings |
Improved Ventilation | Energy recovery or exhaust-only ventilation added to dwelling units depending on climate zone and ACH50 values. |
Table 2. Residential building shell package descriptions.
Package | Included Measures |
Windows, Thin Triple |
|
Windows, ENERGY STAR |
|
Envelope, Light Touch |
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Envelope, Intermediate |
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Envelope, Advanced |
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The full report is available below:
About the Author: Audrey McManemin is currently pursuing her Master's in Energy Science & Engineering at Stanford University, where her research focuses on evaluating methane emission detection and quantification technologies. She holds a degree in Mechanical Engineering from Duke University and previously worked as a data engineer. As a Research Fellow with Evolved Energy Research, Audrey analyzed the impact and cost of upgrading the energy efficiency of residential building shells. Following her master's program, Audrey intends to continue exploring the application of data in providing pathways to decarbonization.
References:
National Renewable Energy Laboratory (2018). National Residential Efficiency Measures Database, v3.1.0. https://remdb.nrel.gov/
National Renewable Energy Laboratory (2024). ResStock Database, v2024.1. https://remdb.nrel.gov/
National Renewable Energy Laboratory, OpenEI U.S. Utility Rate Database, https://data.openei.org/submissions/5806
Present, E., White, P. R., Harris, C., Adhikari, R., Lou, Y., Liu, L., Fontanini, A., Moreno, C., Robertson, J., & Maguire, J. (2024). ResStock Dataset 2024.1 documentation (NREL/TP-5500-88109). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy24osti/88109.pdf
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