From Utility Bills to Retrofit Finance: An AI Framework for Energy-Burden-Aware Underwriting in Residential Decarbonization

This study develops a literature-driven conceptual framework for energy-burden-aware underwriting within the context of U.S. residential decarbonization finance. It addresses a critical and growing disconnect between the policy-driven urgency to decarbonize residential buildings and the financial constraints faced by households most in need of retrofit investments. Notably, those households that stand to benefit the most from energy-efficiency improvements are often underserved by traditional underwriting models. Conventional lending frameworks prioritize stable income streams, established credit histories, and property-based collateral signals, whereas energy-burdened households—characterized by high utility costs, inefficient housing conditions, and constrained liquidity—exhibit affordability dynamics that are poorly captured by these models but are highly relevant for socially beneficial investment. Drawing upon interdisciplinary literature spanning energy burden, building decarbonization, alternative data underwriting, explainable artificial intelligence, and algorithmic accountability, this paper proposes an AI-enabled underwriting framework that integrates utility-bill histories, housing attributes, retrofit cost-benefit dynamics, and consumer protection considerations. Importantly, the framework emphasizes transparency and interpretability, avoiding reliance on opaque risk-scoring mechanisms. Methodologically, the study employs a structured integrative review of academic and policy-oriented sources, synthesizing insights into a multimodal framework encompassing data governance, feature engineering, model development, explainability outputs, fairness diagnostics, and financing pathway design. The contributions of this research are threefold. First, it reconceptualizes utility payment data from a signal of financial distress into a meaningful input for transition finance. Second, it establishes a direct linkage between household energy burden and underwriting design principles. Third, it outlines governance and accountability conditions under which AI-assisted retrofit financing can advance decarbonization objectives without reinforcing systemic exclusion. Given the absence of original empirical data, the findings are presented as a conceptual architecture, evaluation framework, and forward-looking research agenda rather than as empirical validation. The proposed framework is intended to guide future implementation and testing by financial institutions, utilities, public green banks, and housing policy stakeholders. paper develops a literature-based conceptual framework for energy-burden-aware underwriting in U.S. residential decarbonization finance. The study addresses a widening disconnect between the climate-policy imperative to decarbonize homes and the financial reality that many households most likely to benefit from retrofit investment are also the least well served by conventional underwriting. Existing lending models privilege stable income, established credit files, and property value signals, whereas the households facing high utility costs, inefficient housing, and limited liquidity often present precisely the affordability patterns that make retrofit investment socially valuable. Drawing on scholarship on energy burden, building decarbonization, alternative-data underwriting, explainable artificial intelligence, and algorithmic accountability, the paper proposes an AI framework that uses utility-bill histories, housing characteristics, retrofit economics, and consumer-protection rules to assess payment feasibility without reducing underwriting to opaque risk scoring. Methodologically, the paper uses a structured integrative review academic and policy sources and synthesizes them into a multimodal framework comprising data governance, feature design, model development, explanation outputs, fairness diagnostics, and financing-channel selection. The paper’s contribution is threefold: it reframes utility bills from a distress signal into a transition-finance input; it links household energy burden to underwriting design; and it specifies governance conditions under which AI-assisted retrofit finance may advance decarbonization without reproducing exclusion. Because no original dataset is analyzed, the results are presented as a conceptual model, evaluation logic, and research agenda rather than empirical claims. The framework is intended to support future testing by lenders, utilities, public green banks, and housing-policy practitioners.