The Importance of Policy Neutrality for Lowering Greenhouse Gas Emissions
Bibliographic record
Abstract
The drive by Canadian governments, at the provincial and federal level, to lower greenhouse gas emissions has resulted in a hodgepodge of different policy approaches. Some governments have opted for energy taxes, others for regulated limits on total emissions or emission intensity. Unfortunately, not all policy solutions are created equal; some are more effective than others in lowering total emissions and, worse still, may exact a heavy price on the economy. Policy-makers require a better understanding of how various policies affect the health of an economy and of how to mitigate the most pernicious costs. Key to gaining this improved understanding is to recognize one simple fact: some firms are more productive than others. As a consequence, it matters how workers, machines, energy, and other inputs are distributed between these firms. More productive firms should be larger — it is that simple. Some policies, however, increase input costs differently across firms and create costly distortions. Energy intensity targets are a clear example of a policy that disproportionately burdens lower productivity firms, changing firm sizes for the worse and even leading some to shut down altogether. Using a detailed model of production and energy use that matches the Canadian economy, we explore the consequences of the several forms that energy intensity regulations can take. We find the best approach to lowering greenhouse gas emissions is one that is neutral across firms — one that affects the cost of energy for smaller firms no more, or less, than larger ones. The only policy that fulfils this criterion is a flat energy tax. However, a flat tax on energy could well be politically unsellable in Canada, leaving governments to resort to politically palatable but economically risky intensity targets instead. Recognizing this, we explore a number of ways to improve the performance of intensity targets. First, governments should allow firms the option to pay a fine if an intensity standard is violated. Second, we propose a compensation scheme to firms covered by the regulation to prevent bankruptcy. These modifications can bring the cost of intensity standards closer to flat energy taxes. In short, policy-makers seeking an approach to lower greenhouse gas emissions, with minimal impact on economic efficiency and productivity, should look no further than the flat tax on energy. If this is off-limits politically, a combination of intensity standards, fines and compensation comes very close to having the same effect and may well be the policy approach they can persuade the public, and industry, to accept.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".