Feasibility Study of Medium- and Heavy-Duty Compressed Renewable/Natural Gas Vehicles in Canada
Bibliographic record
Abstract
Abstract Concerns about environmental degradation and finite natural resources necessitate cleaner sources of energy for use in the transportation sector. In Canada, natural gas (NG) is currently being appraised as a potential alternative fuel for use in vehicles for both medium- and heavy-duty use due to its relatively lower costs compared with that of conventional fuels. The idea of compressed natural gas vehicles (CNGVs) is being mooted as inexpensive for fleet owners and especially because it will potentially significantly reduce harmful emissions into the environment. A short feasibility study was conducted to ascertain the potential for reduced emissions and savings opportunities presented by CNGVs and renewable NGVs (RNGVs) in both medium- and heavy-duty vehicles. The study which is discussed in the present paper was carried out on long-haul trucking and refuse trucks, respectively. Emphasis was laid on individual vehicle operating economics and emissions reduction, and the identification of practical considerations for both the individual application and CNGVs/RNGVs as a whole. A financial analysis of the annual cost savings that is achievable when an individual diesel vehicle is replaced with a CNG vehicle was also presented. This paper drew substantial references from published case studies for relevant data on maintenance costs, fuel economy, range, and annual distance traveled. It relied on a summary report from Argonne National Laboratory’s GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) (Argonne National Laboratory, 2018, Argonne GREET Model, US Department of Energy. https://greet.es.anl.gov/. Accessed October 5, 2020) for its discussion on relative fuel efficiency penalties for heavy-duty CNGVs. The fuel cost figures were mostly drawn from motor fuel data of the Ontario Ministry of Transportation since the Ministry is one of the few available sources of compressed natural gas fuel prices. Finally, the GHGenius life-cycle analysis tool ((S&T) Squared Consultants Inc., 2019, GHGenius, (S&T) Squared Consultants Inc. https://www.ghgenius.ca. Accessed October 5, 2020) was employed to determine fuel-cycle emissions in Canada for comparison purposes. The study produced remarkable findings. Results showed that compared with diesel-fuelled vehicles, emissions in CNG heavy-and-medium-duty vehicles reduced by up to 8.7% (for well-to-wheels) and 11.5% (for pump-to-wheels), respectively. Overall, the most beneficial application appeared to be long-haul trucking based on the long distances covered and higher fuel economy achieved (derived from economies of scale), while refuse trucks appeared to have relatively marginal annual savings. However, these annual savings are actually a conservative estimate, which will ultimately be determined by a number of factors that are likely to be predisposed in favor of NG vehicles. Significantly, the prospect of using RNG as fuel was found to be a factor for improving the value proposition of refuse trucks in particular, certainly from an emissions standpoint with a reduction of up to 100%, but speculatively from operational savings as well.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".