Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, hybrid models of the energy-economy have been developed with the objective of combining the strengths of the traditional top-down and bottom-up approaches by simulating consumer and firm behavior at the technological level. We explore here the application of discrete choice research and modeling to the empirical estimation of key behavioral parameters representing technology choice in hybrid models. We estimate a discrete choice model of the industrial steam generation technology decision from a survey of 259 industrial firms in Canada. The results provide behavioral parameters for the CIMS energy-economy model. We then conduct a policy analysis and show the relative effects of an information program, technology subsidy, and carbon dioxide tax on the uptake of alternative industrial steam generation technologies, including boilers and cogeneration systems. We also show how empirically derived estimates of parameter uncertainty can be propagated through the model to provide uncertainty estimates for major model outputs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it