Environmental Impact of Outdoor Cannabis Production
Bibliographic record
Abstract
Environmental impacts of cannabis production are of increasing concern because it is a newly legal and growing industry. Although a handful of studies have quantified the impacts of indoor production, very little is known about the impact of outdoor cannabis agriculture. Outdoor production typically uses little direct energy but can require significant fertilizer and other inputs due to dissipative losses via runoff and mineralization. Conversely, fertilizer high in nitrogen can be counterproductive, as it produces flowers with decreased cannabinoid content. This study has two aims: (1) To identify reduced-fertilizer regimes that provide optimal cannabis flower yields with reduced inputs and (2) to quantify how this shifts greenhouse gas emissions, resource depletion (fossil and metal), terrestrial acidification, and the eutrophication potential of outdoor cannabis production. Primary data from a fertilizer response trial are incorporated into a life-cycle assessment model. Results show that outdoor cannabis agriculture can be 50 times less carbon-emitting than indoor production. Dissemination of this knowledge is of utmost importance for producers, consumers, and government officials in nations that have either legalized or will legalize cannabis production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".