Consumer End-Use Energy Efficiency and Rebound Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy efficiency policies are pursued as a way to provide affordable and sustainable energy services. Efficiency measures that reduce energy service costs will free up resources that can be spent in the form of increased consumption—either of that same good or service or of other goods and services that require energy (and that have associated emissions). This is called the rebound effect. There is still significant ambiguity about how the rebound effect should be defined, how we can measure it, and how we can characterize its uncertainty. Occasionally the debate regarding its importance reemerges, in part because the existing studies are not easily comparable. The scope, region, end-uses, time period of analysis, and drivers for efficiency improvements all differ widely from study to study. As a result, listing one single number for rebound effects would be misleading. Rebound effects are likely to depend on the specific attributes of the policies that trigger the efficiency improvement, but such factors are often ignored. Implications for welfare changes resulting from rebound have also been largely ignored in the literature until recently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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 it