The Green Housing Privilege? An Analysis of the Connections Between Socio-Economic Status of California Communities and Leadership in Energy and Environmental Design (LEED) Certification
Bibliographic record
Abstract
This statistical analysis investigated the socio-economic patterns of current residential Leadership in Energy and Environmental Design (LEED) certification in California cities and towns. Specifically focusing on the LEED certification process, this analysis assesses the correlation between the percent of residential buildings with LEED certification in California places and the socio-economic characteristics of those places. The pre-analytic hypothesis was that wealthier cities and towns would have a greater number of LEED certified homes with higher levels of LEED certification. The results of Pearson correlation testing using the statistical software R showed no statistically significant relationship between the total number of LEED certified homes or at any level of certification and the socio-economic characteristics of the places in question. One very influential factor in this finding is the lack of available data-of the 1466 places in California treated as distinct by the U.S. Census with available economic information, only 75 of them had at least one LEED certified home. Another important factor is the role of community development organizations in constructing LEED certified homes. 99.9% of the affordable homes considered in this report were part of large developments (2458 out of 2460 affordable homes), 76% of market-rate homes (anything outside of the “affordable” category) were part of large developments (238 of 314 homes), and 97% of all homes considered (2696 out of 2774) were part of large developments. This analysis of LEED certified homes in California at the admittedly early stages of implementation raises further questions about whether the LEED program can function as a tool for the private homeowner and whether a process currently influenced largely by developers can serve the needs of communities and homeowners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".