Risk and advantage in a changing climate : business preferences for climate change policy instruments in Canada
Bibliographic record
Abstract
How do major business associations and firms determine their preferences for public policy instruments? This dissertation examines the puzzling case of business preferences for climate change policy instruments in Canada in which businesses supported a carbon price over cheaper voluntary policy instruments. It presents the findings of a qualitative study that included 13 major industrial associations (representing chemicals, gas refiners, petroleum producers, natural gas, forestry, mining, steel, vehicle manufacturers, electricity, aluminum, cement, railways and the chief executives) and 17 firms in the cement, oil and gas, and forestry industries. The study found that, in 2008 and 2009, participating firms and associations were strongly in favour of a carbon price – either a cap-and-trade program or carbon tax – despite the higher costs entailed by these policy instruments for industry when compared to voluntary programs. Moreover, Canadian corporations and business associations shifted their policy instrument preferences almost en masse away from voluntary agreements and subsidies to carbon pricing around the same time in 2006-2007. What explains variation in business preferences for climate change policy instruments in Canada over time and between organizations? This dissertation creates a model of business preferences for climate change policy instruments based on the findings of interviews with firm and association executives, as well as government and environmental NGO officials and consultants working in the environmental policy field. In particular, the model suggests that business officials determine climate change policy preferences by weighing risks to capital investments and external investor concern against the competitive advantages entailed by each policy instrument. As these assessments require predictions about an uncertain future, they are strongly influenced by expectations about future government policy choices. These expectations are in turn influenced by the political context, particularly public opinion, and previous experience with a policy instrument. The model, developed inductively from interview data, is validated in the dissertation using new data from the same case and methods such as process-tracing and falsifiable tests. The model is found to offer a good explanation of business preferences for climate change policy instruments in Canada, and may be generalizable to other areas of public policy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".