A tool for estimating the future energy use, carbon emissions, and capital and operating cost impacts of energy efficiency and demand flexibility technologies in the U.S. residential and commercial building sectors.
States or municipalities adopt new building code provisions and/or existing building performance standards that improve efficiency and encourage electrification; Scout’s AEO baseline accounts for ref. case code adoption but needs to account for more aggressive code and BPS adoption.
Proposed approach:
Create a database that lists assumptions about code and BPS characteristics (e.g., energy reduction factors vs. current code, % scope 1 emissions reduction) and adoption levels by state, building type, building vintage (new/existing), end use (or whole building), and start/end years, with applicability factor to map codes/standards that only affect a portion of the state’s area.
Database could include rows for representing federal code/BPS adoption (e.g., state = “all”) with applicability factors that represent estimated % of U.S. energy affected by building type, or in broad groupings of states (e.g., state = ”leading”; state= “usca”).
Apply the assumptions from (a) as a post-processing step in run (likely in finalize_outputs function) across all summed scenario results broken out by the dimensions in (a) that compares scenario efficient-captured results vs. comparable portion of baseline, determines whether relative reductions in (a) are met or exceeded, and adjusts efficient results to meet (a) as needed.
Likely will represent adjustments as separate codes/standards measure in results
States or municipalities adopt new building code provisions and/or existing building performance standards that improve efficiency and encourage electrification; Scout’s AEO baseline accounts for ref. case code adoption but needs to account for more aggressive code and BPS adoption.
Proposed approach: