A dynamic equilibrium approach: the application in long-term energy planning
Bibliographic record
Abstract
In this paper, I present the multi-period market equilibrium model with the geometric distributed lag (GDL) demand, called the GDL equilibrium model, as well as its solution technique, called the decoupling algorithm. The dynamic equilibrium approach, including the GDL equilibrium model and the decoupling algorithm, can be valuable aids in long-run energy planning and energy-related CO2 emission control decision-making, in order to represent the time-lagged effect. In the energy GDL equilibrium model, the demand is represented by a function of the prices not only in the current time period but also in previous time periods through the GDL structure, and the supply is a cost-minimizing linear energy process submodel. The solution technique employs sequential nonlinear programming to calculate the intertemporal equilibrium of energy supplies and demands, along with the corresponding CO2 emission control submodel. The methods of analysis for the economic impact of CO2 emission control are carefully explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".