Towards General Equilibrium in a Technology-Rich Model with Empirically Estimated Behavioral Parameters
Bibliographic record
Abstract
Most energy-economy policy models offered to policy makers are deficient in terms of at least one of technological explicitness, microeconomic realism, or macroeconomic completeness. We herein describe CIMS, a model which starts with the technological explicitness of the “bottom-up” approach and adds the microeconomic realism and macroeconomic completeness of the “topdown” CGE approach. This paper demonstrates CIMS’ direct utility for policy analysis, and also how it can be used to better estimate the long run capital-forenergy substitution elasticity (ESUB) and autonomous energy efficiency index (AEEI) technology parameters used in top-down models. By running CIMS under several possible energy price futures and observing their effects on capital and energy input shares and energy consumption, we estimate an economy-wide ESUB of 0.26 and an AEEI of 0.57%, with significant sectoral differences for both parameters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".