Racial inequity in household energy efficiency and carbon emissions in the United States: An emissions paradox
Bibliographic record
Abstract
Residential energy use represents roughly 17% of annual greenhouse gas emissions in the United States (U.S.). Studies show that legacy housing policies and financial lending practices have negatively impacted housing quality and home ownership in non-Caucasian and immigrant communities. Both factors are key determinants of household energy use. But to date there has been no national scale analysis of how race and ethnicity affect household energy use and related carbon emissions. In this paper, we estimate energy use and emissions of 60 million household to clarify how energy efficiency and carbon emissions vary by race, ethnicity, and home ownership. We find that per capita emissions are higher in Caucasian neighborhoods than in African-American neighborhoods, even though the former live in more energy-efficient homes (low energy use intensity). This emissions paradox is explained by differences in building age, rates of home ownership, and floor area in these communities. In African-American neighborhoods, homes are older, home ownership is lower (reducing the likelihood of energy retrofits), and there is less floor area per person compared to Caucasian neighborhoods. Statistical models suggest that historical housing policies, particularly “redlining”, partially explain these differences. We suggest three policies to address this emissions paradox: Government financing of home retrofits, particularly in rental units; Increased access to photovoltaics in disadvantaged communities; and Disincentivizes for high energy consumption and emissions. Addressing this emissions paradox provides an opportunity for an equitable decarbonization of the U.S. housing sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".