Pressure to pay the price : a normative approach to environmental policymaking
Bibliographic record
Abstract
The pressure to adopt domestic environmental policies is growing as a result of increasingly integrated and influential global environmental regulations, conventions, and institutions. The transformative nature of environmental policies, and carbon taxes in particular, present challenges for domestic governments vis-à-vis public opinion, business groups, and interest groups. Given that provinces have different regional contexts and differing existing normative structures, how can governments inspire acceptance of environmental policies with broad and unspecific global normative rhetoric? In the Canadian provincial context, I argue that norms (known as the widely accepted appropriate or desirable patterns of behaviour within a given society) and ideas play a significant enabling role in aiding governments in the introduction stage of environmental policies that would otherwise be hard to accept. Further, given that different domestic contexts cannot simply adopt normative rhetoric at the global level, I argue that governments must utilize normative strategies to bring the policy to be complementary to existing local norms, discourses, and structures. These strategies include grafting, known as introducing a new norm by connecting it with an existing norm in the same issue area, and framing, known as suggesting alternative perceptions of appropriate normative application that better resonate with public understanding. Lastly, while norms are not the only policy perspective that governments must consider, domestic normative processes have the potential to be a dominant consideration due to the impact they have on other factors of the policymaking process. In light of the recent federal announcement to implement a carbon tax in all provinces currently without a carbon pricing system by the end of 2018, I illustrate my argument by showing how two provincial governments, with vastly different industry and contextual backgrounds, both voluntarily introduced carbon taxes prior to the announcement. Their existing normative structures – the already existent polluter-pays principle in British Columbia and normative pressures from the top-down in Alberta – proved to be crucial factors to the introduction of the carbon tax in their respective provinces. These cases serve not only as an interesting comparison, but are also useful examples of the employment of normative strategies to other provinces hoping to follow suit.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".