The Development of a Framework for Modelling Greenhouse Gas Mitigation Scenarios in the Electricity Generation Sector
Bibliographic record
Abstract
Low- or zero-emitting alternative sources of energy have become widely sought for reducing greenhouse gas (GHG) emissions globally. Specifically, in the electricity generation sector, governments, utilities, regulators, and institutions have announced and implemented integrated policy measures such as renewable electricity generation targets, incentives, and efficiency standards for climate change mitigation. However, the associated constraints of recoverable resource viability, public acceptability, and high investment costs, along with limited generation output of alternative energy technologies compared to fossil fuel technologies, could make a low-emission electricity generation mix uneconomical to pursue. Therefore, it is necessary to quantitatively evaluate the greenhouse gas mitigation possible and the associated abatement costs from different integrated alternative energy penetration scenarios in an electricity generation mix in order to make informed policy decisions. The Long-range Energy Alternative Planning (LEAP) software was used to model the power generation sector over a study period of 41 years (2010-2050). Alberta, a Western Canadian province, was selected to evaluate the environmental and policy implications of the foregoing. This study assessed the comparative GHG mitigation in terms of dollar per tonne avoided and cumulative GHG emissions that could result from the adoption of different alternative energy penetration scenarios in which fossil fuels are replaced in an electricity generation mix in the medium term (to the year 2030) and long term (to 2050) using LEAP. Pathways for increasing the renewable share of electricity generation and associated GHG mitigation possible were investigated. The business-as-usual (BAU) scenario and 18 alternative scenarios were developed, simulating situations in which high-emission baseload coal-fired power plants would be retired and replaced by gas-fired power plants for baseload generation, and zero- or low-emission alternatives such as biomass hydro, solar, wind, geothermal, and nuclear are introduced into the generation mix to replace at least two-thirds of retired coal capacity by 2030. Over the study period, the results show that a GHG mitigation potential of 44% to 60% below 2014 reported emissions of 48.9 Mt CO2 eq. from the electricity generation sector may be achieved by the year 2030. A 30% renewable capacity target would increase the renewable electricity production share from the current 10% to 22% by 2030. The GHG abatement costs of the alternative scenarios range from −$5/t CO2 eq. to $820/t CO2 eq. compared to the BAU by 2030. By 2050, about 42% to 65% GHG mitigation potential may be achieved with scenario abatement costs of −$13/t CO2 eq. to 214/t CO2 eq. compared to the BAU. The outcomes of this study offer insights into the selection of alternative energy penetration pathways for a lower GHG emission electricity generation mix in Alberta.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".