The Impacts of Unilateral Climate Policy on Competitiveness: Evidence From Computable General Equilibrium Models
Bibliographic record
Abstract
When considering the adoption of a domestic climate change policy, politicians and the public frequently raise concerns about competitiveness. Competitiveness in this context does not have a precise economic definition. In this article we discuss possible ways to anchor the concept of competitiveness in economic analysis. We then use this framework as the basis for a systematic survey of the literature on the quantitative impacts of unilateral climate change policy, which are derived from the results of computable general equilibrium (CGE) models. We present empirical estimates from this literature on the magnitude of competitiveness effects that might be associated with the adoption of unilateral climate change policies. We find that there is significant agreement in the literature that unilateral emissions abatement is likely to lead to modest reductions in output and exports from emissions-intensive trade-exposed (EITE) sectors. On average, policies designed to reduce economy-wide emissions by 20 percent are estimated to reduce EITE output by 5 percent and exports by 7 percent. We also find that the results of models are highly dependent on modeling assumptions. Finally, we propose some avenues for future research using CGE models.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".