Getting It Right Matters: Why Efficiency Incentives Should Be Based on Performance and Not Cost
Bibliographic record
Abstract
At least 28 countries and regions offer government-sponsored incentives for energy efficiency, as well as a number of U.S. states and Canadian provinces. But, there has been little cross-fertilization of ideas and even less scientific evaluation of the results. This lack of dialogue and evaluation has led to disproportionate reliance on the simplest solutions, which generally base efficiency incentives on costs. (Sometimes, other performance parameters are also used in addition to costs.) This paper examines the practice and some of the theory that predicts the likely outcomes of different structures of economic incentives for efficiency. It shows how purely cost-based incentives, whenever they have been evaluated, have shown excessive levels of free ridership and failed to transform markets. It finds anecdotal evidence for mixed performance and cost-based incentives working in some cases, but a paucity of evidence to corroborate these anecdotes. These results are contrasted to the experience with performance-based DSM programs, which have proven to be effective both at acquiring efficiency resources and transforming markets. This finding is consistent with analysis of the market barriers and market failures that efficiency confronts, and with the incentives to consumers and suppliers that are provided by the different types of incentives.
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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 | 0.041 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".