Improving Electricity Bill Affordability For Low-Income Customers Using the California Climate Credit
Bibliographic record
Abstract
California’s retail electricity prices have become some of the highest in the country, increasing at a rate that far outpaces inflation. While high electricity prices adversely affect all utility customers, it is low-income customers who are hit the hardest due to electricity bills comprising a larger share of their household income. Though different programs exist to help support low-income customers, including utility-provided ratepayer assistance programs, low-income customers remain more likely to end up in arrears and have been found to be less likely to use cooling or heating electric loads in response to extreme temperatures, which can pose a dire health risk. These financialand health-related risks borne by low-income customers are particularly acute in California climate zones prone to more extreme temperatures, which lead to higher electricity bills and worse health outcomes for customers that forgo consuming electricity for fear of higher bills. This is an urgent problem for too many Californians—more than a quarter of low-income ratepayers are now in significant bill arrearage and could potentially lose access to electricity. In recent years, these electricity affordability concerns have become increasingly important to members of California’s government. Governor Gavin Newsom issued an executive order in late 2024 seeking solutions to the state’s high retail electricity prices, and many state legislators have introduced legislation aimed at providing electricity bill relief and addressing some of the underlying drivers of exorbitant rate increases. While many of the proposed solutions are rooted in sound fundamentals, there is reason to believe that some of these approaches, if passed into law, might take time to realize their potential to lower electricity bills or could be prone to certain political realities, such as state budget constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".