The Elasticity of Substitution between Clean and Dirty Inputs in the Production of Electricity
Bibliographic record
Abstract
I obtain a calibrated estimate of the elasticity of substitution between clean and dirty inputs in the production of electricity for 21 countries. To perform the calibration, I extend the endogenous growth model with directed technical change developed in Acemoglu et al. (2009) to a multi-sector setting. In the model, the elasticity of substitution determines the relative size of two effects – the price effect and the market size effect – which in turn determines the direction of technical change towards clean or dirty technologies. The threshold value of 1 defines an interval where a switch to clean inputs is impossible ( 1). I calibrate an average elasticity of 0.51 for all the 21 countries taken into consideration, thanks to the hypothesis of perfect capital mobility between clean and dirty production within sectors. The complementarity of these inputs makes it theoretically impossible for the electricity sector to reach a tipping point if left to its own devices. Moreover, the strong complementarities characterizing it allow to generalize the prediction to the economy as a whole. A complete switch to clean technologies seems to be difficult to attain unless growth in the electricity sector comes to a complete stop.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".