Optimising Downstream Carbon Pricing Mechanisms to Mitigate Environmental Externalities in the Fuel‑intensive Mobility Sectors
Bibliographic record
Abstract
This thesis investigates the optimization of downstream carbon pricing mechanisms to mitigate environmental externalities in fuel-intensive mobility sectors—namely, ground transportation, maritime shipping, and industrial fisheries. Despite the pivotal role of carbon pricing in climate policy, current schemes remain predominantly upstream and often fail to directly influence fuel consumers or capture sector-specific risks and incentives. Addressing this gap, the thesis develops an integrated “pricing–incentive–risk-control” framework that systematically aligns policy stringency, economic signals, and leakage mitigation across heterogeneous transport systems. The research commences with a comprehensive review of carbon emissions trading in fuel-intensive mobility sectors, revealing that existing upstream-focused systems lack sufficient stringency and leave significant decarbonisation potential untapped at the end-user level. Building on this foundation, a life-cycle assessment of municipal waste transport under Quebec’s cap-and-trade system demonstrates that integrating full-chain carbon accounting into regional downstream trading can achieve significant emission reductions (up to 94%) and substantial cost savings (63%) when revenues are strategically recycled. These findings establish a methodological basis for extending downstream carbon pricing beyond ground transport. Recognizing the interconnectedness of global mobility networks, this thesis next examines cross-regional integration of maritime emission management through a Euro-American carbon market linkage model. The results show that coordinated allowance allocation not only curbs carbon leakage but also stabilizes market prices, highlighting the benefits of synchronized policy design across borders. However, real-world disruptions—such as the Red Sea crisis—underscore the vulnerability of carbon pricing effectiveness to geopolitical shocks, as rerouted maritime traffic temporarily elevates emissions by up to 75% and undermines policy goals. To address such risks, the thesis advances a dynamic system model illustrating how widespread adoption of shore power in ports, incentivized by market-based measures, can mitigate maritime carbon leakage by reducing auxiliary-engine emissions over a decade. Then, a geo-economic network analysis of trans-shipment routes uncovers how uneven port charge structures induce passive carbon leakage. This analysis unifies environmental externality through road, rail, port, and maritime, points to the importance of spatially differentiated transship strategies in downstream climate policy design. Extending this downstream approach to industrial fisheries, the study reveals that most major fishing nations face significant fiscal and administrative barriers to using carbon revenues as a substitute for harmful subsidies, further emphasizing the need for context-sensitive design. By connecting these sequential studies, this thesis not only develops a robust interdisciplinary framework for optimising downstream carbon pricing but also delivers actionable policy recommendations—such as unified management of market participants and leakage-responsive stakeholder charges—capable of transforming carbon pricing into a nuanced, effective tool for deep decarbonisation across diverse, fuel-intensive mobility sectors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".