Energy Efficiency as a Preferred Resource: Evidence from Utility Resource Plans in the Western United States and Canada
Bibliographic record
Abstract
This article examines the future role of energy efficiency as a resource in the Western United States and Canada, as envisioned in the most recent resource plans issued by 16 utilities, representing about 60percent of the region's load. Utility and third-party administered energy efficiency programs proposed by 15 utilities over a ten-year horizon would save almost 19,000 GWh annually, about 5.2percent of forecast load. There are clear regional trends in the aggressiveness of proposed energy savings. California's investor-owned utilities (IOUs) had the most aggressive savings targets, followed by IOUs in the Pacific Northwest, and the lowest savings were proposed by utilities in Inland West states and by two public utilities on the West coast. The adoption of multiple, aggressive policies targeting energy efficiency and climate change appear to produce sizeable energy efficiency commitments. Certain specific policies, such as mandated energy savings goals for California's IOUs and energy efficiency provisions in Nevada's Renewable Portfolio Standard had a direct impact on the level of energy savings included in the resource plans. Other policies, such as revenue decoupling and shareholder incentives, and voluntary or legislatively mandated greenhouse gas emission reduction policies, may have also impacted utilities' energy efficiency commitments, though the effects of these policies are not easily measured. Despite progress among the utilities in our sample, more aggressive energy efficiency strategies that include high-efficiency standards for additional appliances and equipment, tighter building codes for new construction and renovation, as well as more comprehensive ratepayer-funded energy efficiency programs are likely to be necessary to achieve a region-wide goal of meeting 20percent of electricity demand with efficiency in 2020.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".