ENERGY SHADOW VALUE AS A MARGINAL ECONOMIC MEASURE OF ENERGY EFFICIENCY: AN INTUITIVE AND THEORETICAL PERSPECTIVE
Bibliographic record
Abstract
This paper uses a microeconomics perspective to derive an economically meaningful energy efficiency measure. We contribute to the literature by deriving the measure based on neoclassical economic theory. Energy intensity, the most frequently used energy efficiency measure, is a technical average measure drawn from the engineering-science perspective and is not derived from microeconomic foundations, based on marginal production concepts. We address the particular problems associated with this measure and, unlike the literature, introduce the energy shadow value as an economic marginal energy efficiency measure. We describe at least two methods to model the energy index and derive energy shadow value based on a dual profit function: a quasi-fixed approach, treating energy use as quasi-fixed factor; and a directly nested mark-down and up approach, nesting the energy shadow price into the profit function; we discuss which approach is most useful and appropriate to derive an explicit energy shadow value model. We conclude that quasi-fixed approach offers more advantages when deriving a dynamic energy efficiency measure. The paper addresses fallacies of using energy intensity measure and how the shadow value indicator can be applied to address international energy efficiency comparisons and impacting factors particularly capital, technology, and environmental obligations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".