Tunable and computationally efficient framework for retrofitting: Budget allocation using income and spatial equity

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ID: 313646
2026
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Abstract
Abstract The residential sector accounts for roughly 35% of U.S. electricity use. Retrofitting residential buildings has been proposed as an effective strategy to reduce energy costs and improve individuals' quality of life and health. Federal and state funding plays a crucial role in supporting these initiatives by providing financial assistance through grants, tax credits, and low-interest loans. Key challenges here include selecting which households to serve and determining assistance levels so that savings are not skewed toward particular regions or income brackets. We propose RAISE (Retrofitting Allocation using Income & Spatial Equity), a framework that allocates a state's retrofit budget to maximize total energy savings while ensuring fair distribution across counties and income groups. RAISE estimates prospective savings through hourly household consumption models–pre- and post-retrofit–that integrate demographics, weather data, solar irradiance, appliance inventories, and survey inputs. At the core of RAISE's budget allocation is an Income-Weighted ε (IWε) index, where parameter ε ∈ [0,1] tunes the balance between efficiency and equity by enforcing two tiers of constraints: inter-county equity weighted by each county's median income, and intra-county equity among households based on individual incomes. By adjusting ε, policymakers can trade off equity against total savings to match policy goals. A Virginia case study shows that RAISE–across varying ε settings–delivers 60%–101% greater energy savings than current approaches while markedly reducing the number of low-income households receiving less than 10% of their retrofit cost. This two-tiered method is both computationally efficient and offers precise control over geographic and income-based allocation disparities.
Reference Key
openalex_W7160947228 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aparna Kishore, Kaarthik Sundar, Deepjyoti Deka, Madhav Marathe
Journal PNAS nexus
Year 2026
DOI
10.1093/pnasnexus/pgag162
URL
https://doi.org/10.1093/pnasnexus/pgag162
Keywords Keywords not found

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