Importing exotic plants and the risk of invasion: are market-based instruments adequate?
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Exotic plant species are often intentionally imported into regions outside of their normal range as ornamental plants or as breeding stock, thereby generating benefits for consumers and producers. However, one of the unintended side effects of such introductions is that the exotic plant species may become invasive. Prohibiting sale of this type of exotic plant species, on the basis that it may become invasive, will have social costs in the form of foregone consumer benefits and nursery profits. We develop a model of a private commercial plant breeding industry that imports an exotic plant species into a region. The risk associated with invasion is modeled using a probabilistic dhazard functionT, the key determinants of which are the characteristics of the exotic plant and the number of commercial nurseries contributing to its dispersal. We consider the possibility of employing market-based instruments (e.g., Pigovian tax) consistent with the concept of dintroducers payT, to regulate the nursery industry. We then provide an empirical illustration using the historical introduction of saltcedar (Tamarisk spp.) into the United States. Our results indicate that the mere presence of a risk of invasion does not mean that it is socially optimal to prevent commercial sales of an exotic plant species. Indeed, there appear to be plausible forms of the functional relationships involved that require only a modest reduction in the private industry optimum. In contrast, no sales of the exotic plant should occur at all under several sets of assumptions about the level of invasion risk and the linkage between dispersal sites and invasion hazard. D 2004 Elsevier B.V. All rights reserved.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".