INVESTIGATION OF METHODS OF MITIGATING NEGATIVE EXTERNALITIES OF FOSSIL FUEL COMBUSTION IN NORTHERN AREAS
Bibliographic record
Abstract
Climate change has negative impacts on the economy and the environment and major polluters may be held responsible for the resultant damages. This thesis has three core components, which show that using solar photovoltaic (PV) technologies as a replacement for fossil fuel electrical generation is a technically-viable and in some cases economically-advantageous way to reduce the liability of emitters even in northern regions with extreme environments. First greenhouse gas emissions liabilities are reviewed and quantified. Then a methodology is developed to determine the economic viability of solar photovoltaic (PV) systems for large scale institutions like universities. Finally, the effects of snow on photovoltaic systems is quantified for an extreme winter location using experimental data. After reviewing the quantification methods for climate liability, the 10 largest emitters in the U.S. are identified and their liability is evaluated. Different classes of potential litigants are identified and their capacity to file climate change lawsuits is assessed. Results show that profits of major companies can be significantly reduced due to their potential emission’s liability. Economic risks of potential litigants is estimated, and results show that liability for the Alliance of Small Island Nations (AOSIS) is over $570 trillion. Such litigation is not yet widespread, so a methodology is proposed for the determining the financial viability of implementing large scale PV systems and is applied to a case study in Houghton, Michigan. Results show that NPV is positive and energy production cost is less than what university pays for electricity; therefore, the investment is recommended if the cost per Watt of PV reaches $3.10 when electricity export escalation rate is 2%, if there are no snow losses. Previous work indicated that snow losses were relatively minor even for systems installed in Canada. However, the Houghton region experiences some of the largest snow accumulations in the country, so experimental evidence was needed. A test system including seven modules with different orientations and tilt angles was installed at the KRC in Calumet, Michigan and it was monitored for a year. The snow related energy losses of PV systems ranged from 5% for the elevated PV system with the highest tilt angle (45 degrees) to 34% for the obstructed module with lowest tilt angle. These results show that careful system design is needed in such snowy regions and that further work is necessary to reduce snow-related losses to improve the economic performance of PV in northern 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".