Integrated Analysis and Modeling of Energy Demand, Emissions, and Lifecycle Impacts of Bus Electrification Under Low-Carbon Electricity Scenarios
Bibliographic record
Abstract
The transition to net-zero transportation in urban areas requires an integrated understanding of clean electricity deployment, transit electrification strategies, and operational energy dynamics. This thesis presents an integrated analysis and modeling framework for evaluating energy demand, greenhouse gas (GHG) emissions, and life cycle impacts of electric bus deployment under low-carbon electricity scenarios in Canada. The research is structured into four interconnected studies, each addressing a critical layer of the energy transition. The first study conducts a meta-analysis of 26 national decarbonization scenarios to evaluate the role of clean energy in achieving Canada's 2050 targets across electricity, transport, and heating sectors. Findings reveal consistent reliance on hydropower and nuclear (collectively ~80%), while the balance between wind, solar, and natural gas remains uncertain. The study highlights key challenges such as inter-provincial coordination and inadequate carbon pricing, particularly in the electricity sector. Building on this, the second study proposes a regionalized modeling framework to assess electric bus penetration in Toronto, Montreal, Edmonton, and Halifax. Integrating capital costs, dynamic fuel prices, social costs of pollution, and carbon pricing, the study quantifies GHG reductions from 2019 to 2030, with emission cuts ranging from 18.7% to 34.6% under energy system decarbonization (ESD) scenarios. The results emphasize the need for localized strategies within a polycentric governance framework. The third study analyzes real-world operational data from Montreal’s transit network to evaluate battery electric bus (BEB) energy performance under seasonal variations. Energy consumption peaks during winter due to auxiliary heating and road friction, while regenerative braking performs optimally at mid-speeds (30–50 km/h) in warmer conditions. Findings demonstrate the influence of climate and road conditions on BEB energy efficiency and operational cost. The final study develops machine learning-based models to estimate trip-level BEB energy consumption using three years of operational data. The models incorporate external variables such as temperature, state of charge, traffic conditions, road gradient, and stop frequency. Key predictors of BEB energy demand are identified, offering insights for route optimization and energy-efficient scheduling. Together, these studies provide a multiscale, data-driven framework for understanding and advancing electric bus adoption in the context of clean energy transitions. The findings inform both national policy planning and local transit system optimization, supporting the broader goal of achieving sustainable, low-carbon urban mobility in cold-climate regions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".