CFFS Procurement Strategy : Climate Mitigation
Bibliographic record
Abstract
A review of past research indicates that global warming is an imminent issue for all Canadians — trends predict temperature values to rise 0.2°C per decade (IPCC, 2019). Food systems are responsible for 34% of global greenhouse gas (GHG) emissions and are a significant contributor to increased runoff, extreme weather events, and flooding throughout Canada, and globally (Crippa et al., 2021; IPCC, 2019). Canadian food consumption trends have led to long-term, broad impacts that have accelerated climate change (IPCC, 2019). Therefore, addressing these issues will require action from everyone, including The University of British Columbia (UBC). UBC is well-positioned to lead an integrated approach in creating a just and resilient campus-wide food system; however, food procurement strategies and consumption patterns have not yet been sufficiently analyzed. Our project informs the development of a campus-wide Climate-Friendly Food System (CFFS) Procurement Strategy to reduce GHGs and engage with UBC’s Climate Action Plan 2030 (CAP). Working alongside UBC Student Housing and Community Services (SHCS), and Campus + Community Planning (SEEDS), we developed a Climate-Friendly Food Systems (CFFS) procurement strategy in order to aid our community partners reach their goal of reducing food system-related GHG emissions by 50% in 2025. Our project achieved several goals, including (1) providing knowledge and information on campus-wide food-supply practices, (2) developing a Climate-Friendly Food System Procurement Strategy, and (3) providing recommendations to our community partners of Campus and Community Planning, and UBC Student Housing and Community Services that will help to reduce UBC’s GHG emissions. We conducted primary data collection through a focus group and interviews with individuals involved in UBC’s food system via Zoom, and secondary data collection through practitioner literature reviews to identify high impact opportunities, frameworks, policies, and promising practices to reduce procurement-related GHG emissions at UBC. Based on these results we categorized our results into three major areas of opportunity which were: plantbased, seasonality/locality, and monitoring. To give a broad overview of our primary and secondary results, they indicated that UBC would find it useful economically and environmentally to promote more plant-based menu offerings, conflicting evidence between primary and secondary data on the effects of procuring locally/seasonally, and to emphasize the monitoring of progress towards decreasing GHGs and ensure accountability throughout UBC’s food system. Our discussion revealed that plant-forward was a high impact opportunity for reducing GHGs, that we should focus on a variety of metrics for food procurement rather than focusing solely on locality/seasonality, and that monitoring can help inform the furthering of equity in UBC’s food systems. Overall, we acknowledge data limitations such as limited sample size, scope and sampling bias due to the short time frame of this project, but believe that our findings will still prove useful in developing a CFFS Food procurement strategy. Recommendations found through this project for reducing GHG emissions within the UBC Vancouver Campus Food System include short term recommendations of increasing the appeal and incentivization of plantbased foods, develop climate change accountability benchmarks, and promoting menu switches to less GHG emitting products, and develop assessment tools to determine the sustainability of the food procured at UBC. Longterm recommendations include increased monitoring of food waste to inform a procurement strategy that produces less waste (and therefore fewer emissions), increasing funding to the development of metrics that monitor the GHG emissions associated with foods at UBC, and utilize CFFS metrics to inform food sourcing. We also developed a comprehensive CFFS Procurement Strategy for the UBC Vancouver campus food system, which is provided externally, which involves climate food procurement targets, indicators, and actions that will help reduce GHG emissions in a holistic manner. Overall, we believe that our CFFS Procurement Strategy, informed by our primary and secondary research results, will prove valuable to our clients when furthering these important initiatives. Disclaimer: “UBC SEEDS 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 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 Coordinator 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".