Net zero energy barns for industrial egg production: An effective sustainable intensification strategy?
Bibliographic record
Abstract
Net zero energy buildings (NZEBs) are energy efficient buildings that incorporate renewable energy generation systems so as to produce sufficient renewable energy to at least offset the total amount of non-renewable energy used by the building on an annual basis. NZEB technologies have widespread commercial and residential application, but their feasibility and efficacy in the livestock sector in support of sustainable intensification have received little attention. This study quantifies the potential for such technologies to improve sustainability outcomes in the livestock sector based on an ISO 14044-compliant life cycle assessment of a pilot net zero energy laying hen facility in Alberta, Canada compared to a conventional facility. It was found that direct energy inputs account for 6.47% and 31.64% of the life cycle cumulative energy use of egg production in NZE and non-NZE hen housing, respectively. Average infrastructure-related contributions to the life cycle impacts of egg production are only 4.34% and 1.94% for the NZE and non-NZE barns, but NZE technologies reduce the net impacts of egg production by 0.89–64.82%. The environmental impact payback time for the NZE barn (30-year lifespan) ranges from 1.38 to 20.66 years, considering the largely fossil fuel-based electricity grid in Alberta, which indicates that non-trivial environmental benefits would accrue across impact categories considered. However, this could vary considerably elsewhere depending on the types and amounts of green energy utilized in regional grid mixes. The type and availability of renewable energy resources that are integrated into NZE barns will similarly be important in determining the potential of such technologies to support sustainable intensification in this sector.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".