Life cycle assessment of electricity delivery systems: Attributional and Consequential approaches
Bibliographic record
Abstract
The transition towards low carbon electricity generation can be guided by investigating the economic and environmental consequences of policy decisions. However, there is limited information on greenhouse gas (GHG) emissions, energy footprints, and changes in production cost under different policy constraints for emerging sustainable energy generation systems. This thesis, therefore, explores the environmental and economic implications of transitioning to a low carbon electricity generation system through a life cycle approach. Large-scale solar power plants have captured the attention of energy policymakers and industrial stakeholders globally because they can contribute to the long-term plan to reduce the impacts of climate change related to conventional fossil fuel power plants. In this study, we developed a comprehensive bottom-up life cycle assessment model to evaluate the emissions and energy profiles of large-scale solar photovoltaic systems. A case study for a fossil fuel-based energy jurisdiction, Alberta, a western province in Canada, was conducted. We also investigated the potential to use such an energy system to provide consistent electricity supply to the grid compared to peak load options. The results show life cycle GHG emissions of 60.21-79.61 g CO2eq/kWh, a net energy ratio (total energy output divided by total fossil fuel consumed over the lifecycle) of 7.48-10.04, and an energy payback time (time required to regain the invested energy) of 2.73-3.00 years. The system was integrated with lithium-ion energy storage for a consistent electricity supply over a period. The corresponding results are 155.25-220.61 g CO2eq/kWh, an NER of 2.63-3.61, and a payback time of 7.01-9.45 years. More than 60% of the energy consumed is in upstream manufacturing processes. We also developed a novel framework to evaluate the long-term environmental consequences of marginal changes in electricity generation that result from policy decisions in fossil fuel-dominant jurisdictions. The framework integrates market penetration, long-term energy demand and supply modeling, and marginal cost and emissions analyses. A case study for Alberta was conducted. Based on the province’s specific energy generation resources and its policy initiatives, we created 9 scenarios investigate the effects of renewable energy penetration under competitive market conditions (no renewable targets), regulations to ensure minimum production from renewables, improved storage capabilities, and GHG emission targets. With the Long-range Energy Alternatives Planning (LEAP) framework, we developed an energy generation model to calculate probable future electricity mixes, generation costs, and the resulting GHG emissions. The marginal changes in energy generation and GHG emissions were quantified for each scenario to incorporate different policy decisions and market effects. We determined that in Alberta combined cycle power plants and wind energy are the key marginal suppliers of electricity in the transition to a cleaner grid. The effects of adding energy storage to the grid along with renewable energy systems, replacing natural gas with renewable energy, and setting more aggressive GHG emission reduction targets than current policies require were also investigated. The information provided in this thesis would help concerned entities in formulating policies and making investments in the electricity sector.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".