Environmental and Economic Implications of Small-Scale Canadian Aquaponics: A Life Cycle Study
Bibliographic record
Abstract
Agricultural production will be challenged in the near future to keep up with the rising nutritional demands of a growing global population. Additionally, climate change, through increased frequency of extreme weather events and droughts, will further push food production to its limits. Controlled-environment food production systems (CEFPS) are suggested as viable options to supplement existing agriculture by allowing food production expansion without requiring large amounts of land and by offering protection from changing weather patterns and other undesirable external conditions. Aquaponics is a form of CEFPS that combines recirculating aquaculture with hydroponics to produce both fish and vegetables. However, the environmental and economic performances of these systems in Canada and other cold climates have yet to be explored in depth. The overarching goal was to evaluate the potential for aquaponics to be a responsible and sustainable solution to maintaining Canadian food security. Specifically, this thesis aimed to identify environmental and economic barriers faced by small-scale Canadian aquaponics systems and provide options for reducing barriers and environmental impacts through the application of life cycle assessment (LCA) and life cycle cost (LCC) analysis. The major results of this study indicate that aquaponics in its current form is an energy-intensive form of agriculture and is more environmentally impactful than conventional forms of fish and vegetable production with a global warming potential (GWP) of 68 kg CO2eq/kg live fish and 50 kg CO2eq/kg leafy greens. Alternative scenarios, including energy efficiency improvements, renewable energy sources, and insect-based fish feed, were considered in order to address the environmental and economic hotspots identified. The following specific conclusions can be made: (1) energy consumption for artificial lighting and heating made necessary by cold climates is the biggest contributor to environmental impacts and costs; (2) an alternative scenario with off-site wind energy, LED lighting, and insulation reduces life cycle costs by 5% and GWP by 97%; and (3) alternative scenarios with insect-feed and on-site renewable energy can reduce specific environmental impacts but are more costly. It is recommended to pay particular attention to building design aspects, such as access to natural lighting and energy efficient HVAC systems, and climate-specific choices, such as cold-resistant crops and fish, in order to reduce the inherent energy intensity of operation. Overall, this work will help researchers and businesses improve performance of aquaponics systems, while serving as a foundation for the sustainability assessment of cold-climate aquaponics.
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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) | 0.000 | 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) | 0.001 | 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".