Energy Burden & Poverty Data
Households spending 10%+ of income on energy bills -- the equity data that weatherization programs, utility assistance AI, and policy researchers need to target relief.
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Find Me This Data →Overview
What Is Energy Burden & Poverty Data?
Energy burden data measures the percentage of household income spent on energy bills, with energy poverty typically defined as spending 10% or more of income on energy costs. This dataset is critical for identifying vulnerable populations—low-income, Black, Hispanic, rural, elderly, and non-white households—who face disproportionately higher energy burdens than average households. Energy burden is correlated with greater risk for respiratory diseases, increased stress, economic hardship, and difficulty escaping poverty. In 2024, at least 70 million U.S. households experienced some form of energy insecurity, with 34% reporting they reduced or forwent basic necessities like food or medicine to pay energy bills. Researchers, policymakers, utility assistance programs, and weatherization initiatives use this data to target relief interventions and model the effectiveness of energy poverty reduction policies.
Market Data
At least 70 million
Households in energy insecurity (2024)
Source: RMI
1 in 4
Low-income households spending 15%+ of income on energy
Source: ACEEE
71.4 million (34%)
Households forgoing food or medicine for energy bills
Source: RMI
$21.1 billion
Total U.S. electricity customer debt (Sept 2024)
Source: RMI
30%
Electricity price increase (past 4 years)
Source: Energy Professionals
Who Uses This Data
What AI models do with it.do with it.
Utility Assistance & Weatherization Programs
Target low-income households with the highest energy burdens to deliver LIHEAP funding, bill assistance, and energy efficiency retrofits that can reduce burdens by up to 25%.
Policy & Regulatory Analysis
Lawmakers and regulators use energy poverty data to design and model policies, such as percentage-of-income payment plans (PIPP), to measure cost-effectiveness and identify geographic or demographic gaps in relief coverage.
Energy Insecurity Research
Academic and nonprofit researchers identify disparities among racial, rural, elderly, and renter households to understand root causes and design targeted interventions.
AI-Driven Benefit Enrollment
Machine learning models use administrative data to predict energy poverty risk and automatically connect eligible households to assistance programs without requiring manual applications.
What Can You Earn?
What it's worth.worth.
Aggregated National Energy Burden Data
Varies
Compiled from LEAD Tool, ACEEE reports, and energy utility administrative records. Value depends on geographic granularity (national vs. county vs. census tract) and demographic segmentation.
State-Level Energy Burden & Insecurity Datasets
Varies
Household-level or neighborhood-level data on energy spending patterns, income, and energy insecurity indicators. Price reflects licensing scope and exclusivity.
Utility Customer Arrearages & Debt Records
Varies
Administrative data on unpaid energy bills by income level and demographic. Restricted by utility privacy regulations; licensing volume and time period affect pricing.
Machine Learning Training Sets
Varies
Labeled datasets combining energy burden, household characteristics, and outcomes (e.g., bill payment, disconnection risk). Price varies by sample size and feature richness.
What Buyers Expect
What makes it valuable.valuable.
Demographic Granularity
Data must clearly segment households by income level, race/ethnicity, age, housing type (renters vs. owners, single-family vs. multifamily), and geography (urban vs. rural). Disparities across these groups are central to the value.
Income & Energy Burden Alignment
Records must link actual energy costs (annual bills or monthly averages) to household income to calculate burden ratios. Income data must be current or from a matching time period to ensure accuracy.
Geographic Coverage & Resolution
Coverage should span state, county, and ideally census-tract or service-territory levels. National, state, and utility-specific datasets are all in demand depending on the buyer's scope.
Energy Insecurity & Hardship Indicators
Beyond burden, data should capture behavioral or outcome measures: bill payment behavior, disconnection risk, reduced/foregone expenses, unsafe temperatures maintained, or participation in assistance programs.
Administrative Data Provenance
Data sourced from utility companies, government energy assistance programs (LIHEAP), Census, or validated survey instruments (e.g., HES, NEMS) carry higher credibility than self-reported estimates.
Companies Active Here
Who's buying.buying.
Design bill assistance programs, identify disconnection risk, and comply with energy poverty reduction mandates. Wholesale electricity prices are running 12% higher than prior year, making targeted assistance programs more urgent.
Administer Low-Income Home Energy Assistance Program (LIHEAP) funds, allocate weatherization resources, and monitor progress toward energy poverty reduction targets using the LEAD Tool and similar platforms.
Model energy poverty policy interventions, such as percentage-of-income payment plans, to estimate nationwide costs and savings. RMI's EPPS tool enables scenario planning for regulators.
Target weatherization and bill assistance to highest-burden households. Research shows rural, non-white, and elderly households spend more on energy as a share of income.
Conduct energy poverty epidemiology, test machine learning methods for household identification, and publish peer-reviewed analyses of disparities and policy effectiveness.
FAQ
Common questions.questions.
What percentage of income qualifies as energy burden or energy poverty?
Energy burden is typically measured as the percentage of household income spent on energy bills. While no single universal threshold exists, spending 10% or more is commonly used as an energy poverty indicator. ACEEE's 2024 research found that one in four low-income households spend over 15% of their income on energy bills, a significantly higher burden than average households face.
Which household groups face the highest energy burdens?
Low-income, Black, Hispanic, rural, non-white, elderly (age 60+), renter, and multifamily/manufactured housing households all face dramatically higher energy burdens than average households. Black and Hispanic households are more likely to be energy insecure than white households, even when accounting for housing condition and energy burden factors. Disparities vary by region and housing type.
What policy solutions can reduce energy poverty?
RMI's analysis found that implementing a universal percentage-of-income payment plan (PIPP)—ensuring all customers stay below a 4% energy burden—could end energy poverty nationwide for a net cost of $9.3 billion after accounting for $10 billion in savings from avoided arrearages. Energy efficiency retrofits can reduce household energy burdens by as much as 25%. Targeted LIHEAP funding, bill assistance programs, and weatherization initiatives are also key interventions.
How much energy insecurity exists in the United States?
In 2024, at least 70 million U.S. households experienced a form of energy insecurity. Specifically, 71.4 million households (34%) reduced or forwent basic necessities like food or medicine to pay energy bills, and 46.7 million households (22%) kept their homes at unsafe or unhealthy temperatures. Electricity customer debt totaled $21.1 billion as of September 2024, underscoring the affordability crisis.
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