Comparing high-tech urban agriculture to conventional agriculture in Canada
Bibliographic record
Abstract
This thesis investigates the environmental impacts of controlled-environment urban agriculture (CE-UA) in Canada, focusing on high-tech urban lettuce farming as a case study. Given the projections indicating a global population increase to 9.7 billion by 2050, and over 70% of people residing in urban areas, the pressure on food systems, exacerbated by urbanization, necessitates sustainable solutions. Agriculture already contributes significantly to environmental pressures, with cities playing a notable role due to their population density and consumption patterns. CE-UA holds promise in mitigating the environmental impacts of food production and enhancing food system resilience. The expanding population, the effects of climate change, and dietary transitions towards higher consumption of meat, fruits, and vegetables are setting pressure on current food production technologies. To address these challenges, this study assesses the environmental performance of lettuce production with CE-UA in different Canadian regions with diverse energy grids. By analyzing factors such as carbon emissions, water usage, and land efficiency, this research provides insights into the comparative advantages and disadvantages of CE-UA over conventional agriculture.Through a comprehensive review of the literature and empirical analysis, this study identifies key factors influencing the environmental footprint of CE-UA. It finds that while CE-UA can offer environmental benefits such as reduced water use and land use compared to conventional agriculture, its performance hinges on energy sources. Like Alberta, regions with carbon-intensive energy grids may see higher carbon emissions from CE-UA lettuce production compared to market-average lettuce. In contrast, areas with renewable energy, like Quebec, could achieve comparable or lower emissions. Furthermore, the study highlights the importance of deploying CE-UA in conjunction with low-carbon energy sources to realize its potential for sustainable food production.In conclusion, this thesis underscores the significance of understanding the environmental implications of CE-UA, particularly in the context of Canada's diverse energy landscapes and climatic conditions. By shedding light on the environmental performance of CE-UA and identifying areas for improvement, this study contributes to the ongoing discourse on sustainable urban agriculture and food system resilience
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".