Carbon Capture and Storage : Research on capturing carbon from emissions of UBC BRDF
Bibliographic record
Abstract
On December 12, 2015, Canada and 194 other countries signed the Paris accord, an ambitious agreement to fight climate change. This agreement called for a concerted effort to limit the global average temperature rise to well below 2°C and pursue further efforts to limit the increase to 1.5°C. (Canada, 2016) This implies that the remaining carbon budget available at our disposal is extremely limited, and that the world must reduce greenhouse gas emissions at a rapid pace. But even then, we may not meet the target without the aid of the so-called negative emissions. Advanced technologies are being employed to reduce emissions in hard-to-abate sectors, such as cement and power, and also to sequester CO2 already present in the atmosphere. High on the list is carbon capture, use, and storage (CCUS), the term for a family of technologies and techniques that capture CO2 and use or store it to prevent its release into the atmosphere. Through direct air capture (DAC) or bioenergy with carbon capture and storage (BECCS), CCUS can effectively bring down CO2 concentrations in the atmosphere, helping to achieve the need of the hour - negative emissions. In some cases, the captured CO2 can be used to create products ranging from cement to synthetic fuels. (Biniek, Henderson, Rogers, & Santoni, 2020) In their endeavour to meet the climate goals, the UBC Vancouver campus has achieved aggressive GHG reductions (38% from 2007 to 2018) and are committed to look for pathways to achieve zero and eventually negative emissions. However, UBC won’t be able to completely reduce its reliance on fossil fuels due to research experiments and Bunsen burners. Also, to be noted is that the burning of purchased RNG (renewable natural gas) also has a biogenic CO2 component. Hence some sort of negative carbon accounting will be necessary if UBC is to fully reach its goal of zero GHG emissions by 2050. UBC Social Ecological Economic Development Studies (SEEDS) Sustainability ProgramStudent Research ReportDecarbonizing UBC’s District Energy System: District Energy Heat Pump TechnologyThivya Viswanathan University of British Columbia APPP 506 Themes: Buildings, Energy, Water Date: Dec 31, 2019 Disclaimer: “UBC SEEDS Sustainability Program provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student research project/report and is not an official document of UBC. Furthermore, readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Sustainability Program representative about the current status of the subject matter of a project/report”.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".