Bibliographic record
Abstract
Rising fuel costs, stricter environmental regulations and an increased desire for corporate transparency by stakeholders are forcing organizations to be more sustainably focused in all aspects of their business. This is especially true for organizations operating in energy intensive and environmentally controversial industries such as oil and gas extraction and production. For organizations operating in Canada and the US where communities and resources are spread out over a large landmass, focusing on vehicle fleets with a sustainability lens has become increasingly popular over the last decade as a way to decrease costs and environmental impact. The purpose of this report is to analyze Enerplus Corporation’s vehicle fleet which is used for oil and gas operations in Canada and the US. Using data provided by Enerplus in conjunction with supplementary research this report identifies the difference in capital costs and emissions for gasoline and alternative fuel vehicles (AFVs) including compressed natural gas (CNG) and liquefied petroleum gas (LPG). It was found that CNG offers the most cost savings and creates the least amount of carbon emissions. Fueling logistics are also assessed for each AFV based on Enerplus’ operations locations along with information outlining costs and considerations for implementing private refueling infrastructure in addition to a discussion on mobile refueling services. The regulatory environment for both Canada and the US is also explored to better understand government action toward emissions from vehicles and emissions from industry. The concept of eco-driving is also discussed with cases presented to understand how non-aggressive driving behavior can result in decreased fuel consumption and accident risk. It was found that eco-driving can be attributed to decreasing fuel consumption by 6 percent and accident frequency by 35 percent. To conclude, recommendations for Enerplus from the author are included based on the findings of this report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.150 | 0.041 |
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".