MODELING CLIMATE POLICY: ADDRESSING THE CHALLENGES OF POLICY EFFECTIVENESS AND POLITICAL ACCEPTABILITY
Bibliographic record
Abstract
Reducing greenhouse gas emissions by a substantial amount will require aggressive climate change policies. Policy makers and the public are concerned that such policies could be associated with negative economic impacts, such as reduction in the growth rate of economic output, loss of international competitiveness, and concentration of costs amongst vulnerable demographic groups, regions, or economic sectors. The aim of this thesis is to show that the design of climate change policy has a substantial bearing on such economic impacts, to the extent that policy makers can effectively choose many of the likely economic impacts of a particular climate change policy through careful design. Conversely, inattention during climate change policy design can lead to undesirable economic impacts. The analysis is conducted with a series of computable general equilibrium models as well as an econometric model. These models are applied to examine both proposed and existing climate change and energy efficiency policies. Several findings emerge from the analysis. First, so-called ‘intensity-based’ climate change policies, which have been proposed in Canada for nearly a decade but which have met with much criticism, may be useful in promoting economic growth and maintaining international competitiveness. Second, under unilateral application of climate change policy, the international competitiveness of energy-intensive industries in developed countries is likely to be worsened. However, several policy mechanisms are available that substantially mitigate this loss in international competitiveness. Third, climate change policies are unlikely to result in a more unequal distribution of income in society, unless revenues from the policy are allocated in a equality-worsening manner. And fourth, past energy efficiency subsidies on average do not appear to have been cost-effective in reducing energy consumption.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".