The effects of CO2 abatement policies on power system expansion
Bibliographic record
Abstract
Human development owes a great debt to cheap plentiful energy. Historically, abundant and energy dense materials such as coal, oil and more recently natural gas, have played an important role in powering our economies. To this day, any study analysing the short-term costs and benefits of energy system expansion, will continue to favour fossil fuels. At the same time, there is increasing concern about the levels of human made greenhouse gasses such as CO2 (the major by product of burning fossil fuels) and their forecasted effects on the global climate. This thesis investigates the consequences of using political intervention to internalize the cost of future negative effects of anthropogenic CO2 emissions. More specifically, this thesis investigates the effects of regulatory and market based instruments for curbing CO2 emissions from electric power systems in terms of both cost and efficacy. \n A model is developed to approximate the yearly changes in generation capacity and electricity supply mixture of a power system subject to the constraints of carbon abatement policies. The model proposes a novel approach for incorporating investment in non-dispatchable, intermittent wind generation capacity as a decision variable in the planning process. The model also investigates the effects of the stochastic nature of input parameters through the use of Monte Carlo simulation. To explore many features of this model, the Ontario power system is chosen for a case study because of its diverse portfolio of both generation technologies and political objectives. Five policies are simulated and compared with a ‘business-as-usual’ base case in which no carbon abatement policy is imposed. No single policy can meet all of the political objectives being investigated; however, some policies are clear winners in terms of specific objectives. Due to the broad scope of this work, the study finds many conclusions, such as: \n \n-\tAggressive policies do not always promote heavy investment in intermittent wind generation sources. \n-\tOn a $/tCO2 avoided basis, aggressive policies are expensive. Modest policies (very small penalties for CO2 emissions) are very sensitive to the uncertainties in future fuel prices and load profiles. \n-\tInvestment in nuclear capacity is very responsive to the severity of CO2 penalty. \n \nThe study also concludes that the most aggressive policies produce the greatest overall reductions in CO2 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".