The economic and environmental cost of delayed GM crop adoption: The case of Australia's GM canola moratorium
Bibliographic record
Abstract
Incorporating socio-economic considerations (SECs) into national biosafety regulations regarding genetically modified (GM) crops have opportunity costs. Australia approved the cultivation of GM canola through a science-based risk assessment in 2003, but allowed state moratoria to be instituted based on potential trade impacts over the period 2004 to 2008 and 2010 in the main canola growing states. This analysis constructs a counterfactual assessment using Canadian GM canola adoption data to create an S-Curve of adoption in Australia to measure the environmental and economic opportunity costs of Australia's SEC-based moratoria between 2004 and 2014. The environmental impacts are measured through the amount of chemical active ingredients applied during pest management, the Environmental Impact Quotient indicator, and greenhouse gas emissions. The economic impacts are measured through the variable costs of the weed control programs, yield and the contribution margin. The environmental opportunity costs from delaying the adoption of GM canola in Australia include an additional 6.5 million kilograms of active ingredients applied to canola land; a 14.3% increase in environmental impact to farmers, consumers and the ecology; 8.7 million litres of diesel fuel burned; and an additional 24.2 million kilograms of greenhouse gas (GHG) and compound emissions released. The economic opportunity costs of the SEC-based moratoria resulted in foregone output of 1.1 million metric tonnes of canola and a net economic loss to canola farmers' of AU$485.6 million. The paper provides some of the first quantified, post-adoption evidence on the opportunity cost and environmental impacts of incorporating SECs into GM crop regulation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".