The application of an energy metric (EROI) for the analysis of a city energy profile
Bibliographic record
Abstract
Energy return on investment (EROI) is an energy metric used to build models comparing different energy extraction, transport, and use options.Its demand to date is determined by two limitations: the development of correct calculation and forecasting methods, as well as establishing the limits of practical applicability.This article proposes a solution to the second limitation, namely, the calculation of the weighted average EROI of a city's electrical energy consumption system.This value can be useful in the analysis of the fuel and energy balance, economic development potential, and sustainability of a city energy system.The authors calculated the balance of the energy system based on the volume of electrical energy consumption for 4 megacities of the world: Toronto-Hamilton-Oshawa, New York, London, and Moscow, indicating the estimated weighted average EROI based on the world average values of each energy resource.A comparison of the weighted average EROI of London and Moscow calculated using global and local values is presented.The results show that EROI for local energy resources allows for a more reliable calculation for individual cities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".