Life Cycle Greenhouse Gas Emissions of Western Canadian Natural Gas and a Proposed Method for Upstream Life Cycle Emissions Tracking
Bibliographic record
Abstract
Natural gas (NG) produced in Western Canada is a major source of Canada’s energy and emissions portfolio. However, there is only limited understanding of the sources and drivers of greenhouse gas (GHG) emissions. To assess the climate impact of NG in Western Canada, a life cycle assessment (LCA) of a hypothetical 1 billion cubic feet per day LNG production facility with upstream operations based on Seven Generation Energy Ltd.’s (the ‘company’) operations is performed using a model available through the National Energy Technology Laboratory (NETL) which has been modified to adjust for provided company data. Using this case study as an example, the completeness of publicly available GHG emissions data on oil and gas operations in Western Canada to estimate their upstream GHG footprint is examined. The LCA of company-sourced natural gas resulted in a GHG emissions intensity of 410-477 gCO2e/kWh for electricity production (domestic use and LNG to China) and 87 gCO2e/MJ heat for district heating in China. These results indicate that the company’s natural gas produces lower life cycle GHG emissions than the average emissions from natural gas production in the US, AB, and BC, and emit 370-640 gCO2e/kWh fewer emissions compared to coal. The low emissions intensity is achieved through mitigation methods implemented by the company including but not limited to utilizing air-driven pneumatic devices, regular leak detection and repair (LDAR), inherent reservoir characteristics. The upstream GHG emission intensity of the company’s NG production is estimated to be 3.1 - 4.0 gCO2e/MJ compared to current estimates of BC emissions intensities of 6.2 - 12 gCO2e/MJ NG and the US average of 15 gCO2e/MJ. The analysis reveals that compared to US studies, public GHG emissions data for Western Canada have significant data aggregation and/or missing data (gaps) and satisfy only 50% of the modified NETL model inputs. Company provided data close a majority of these gaps, satisfying all data inputs for pre-production, production, and processing (~80% of model inputs) but not transmission as the Company does not operate NG transmission pipelines. To better inform the public of GHG emissions in Alberta and BC, the thesis recommends that the provinces reduce its reliance on aggregate data reporting and develop a data collection and for public release template based on the modified NETL model. In this context, the thesis proposes a data collection template to facilitate better GHG emissions estimates and provide insight to potential mitigation strategies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".