Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics
Bibliographic record
Abstract
Interest groups and experts debate the cost of greenhouse gas (GHG) reduction, and policy-makers do not know whom to believe. The confusion stems from differing definitions of costs and divergent assumptions about key uncertainties, especially the role of policy in influencing the long-run evolution of technologies and consumer preferences. Analysis could be more helpful to policy-makers by combining technological explicitness with behavioral realism in hybrid models. With such a model, we demonstrate how GHG reduction cost estimates vary depending on whether the analyst focuses just on the financial costs of technologies or combines this with other relevant components of consumer and business preferences, such as option value and consumers’ surplus. We also show how this type of model can allow policy-makers to explore the uncertain relationship between policies and the evolution of technologies and preferences, which are critical factors in the long-run cost dynamics of GHG emission reduction. We explore these generic methodological issues with a case study of GHG reduction costs in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".