INTEREST-BASED EXPLANATIONS OF THE STRINGENCY OF CARBON-PRICING POLICIES: THREE ANALYTICAL APPROACHES
Bibliographic record
Abstract
Putting a price on emissions is one of the most efficient ways to fight climate change. However, in spite of the international community’s efforts, most carbon-pricing policies (CPPs) fall short of the stringency needed to reach the commitments of the Paris Agreement. Given that abating emissions imposes costs on specific actors within a national economy, the policy design and implementation of mitigation policies are still in the realm of domestic decision-making. This dissertation investigates the political economy determinants of the stringency of CPPs. It argues that specific constituencies –the emission-intensive trade exposed sectors and the fossil fuel industry– influence the policymaking process to lower CPP stringency because they would pay the cost of commodifying emissions. Chapter 1 uses a non-traditional instrumental variable and finds negative and statistically significant associations between industrial output size and the CPP stringency in 34 countries from 1990 to 2015. Chapter 2 develops an argument that follows from the findings in chapter 1, using process-tracing to assess the “who and how” of stakeholder influence in three Mexican policies, the carbon tax, the Law for Energy Transition and the pilot cap-and-trade. Chapter 3 uses a qualitative comparative analysis to analyze subnational CPPs in North America. It includes 45 cases in which states and provinces in Canada, Mexico, and the United States, have sought to implement these policies. Overall, the chapters find that regardless of the levels of economic development, ideological orientation, institutional design, and other conventional wisdom variables, interest-based considerations influence CPP stringency the most. The dissertation uses three different methods –deductive statistical inferences, process-tracing, and qualitative comparative analysis– to demonstrate how calculations of costs and benefits shape the current landscape of national and subnational CPPs. The work closes with policy recommendations that acknowledge that policymakers must consider political economy forces and engage with powerful stakeholders in the design of these policies.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".