Second generation biofuels and bioinvasions: An evaluation of invasive risks and policy responses in the United States and Canada
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Biofuels are being embraced worldwide as sustainable alternatives to fossil fuels, because of their potential to promote energy security and reduce greenhouse gas emissions, while providing opportunities for job creation and economic diversification. However, biofuel production also raises a number of environmental concerns. One of these is the risk of biological invasion, which is a key issue with second generation biofuel crops derived from fast-growing perennial grasses and woody plant species. Many of the most popular second generation crops proposed for cultivation in the U.S. and Canada are not native to North America, and some are known to be invasive. The development of a large-scale biofuel industry on the continent could lead to the widespread introduction, establishment, and spread of invasive plant species if invasive risks are not properly considered as part of biofuel policy. In this paper, we evaluate the risk of biological invasion posed by the emerging second generation biofuel industry in the U.S. and Canada by examining the invasive risk of candidate biofuel plant species, and reviewing existing biofuel policies to determine how well they address the issue of invasive species. We find that numerous potentially invasive plant species are being considered for biofuel production in the U.S. and Canada, yet invasive risk receives little to no attention in these countries' biofuel policies. We identify several barriers to integrating invasive species and biofuel policy, relating to policy analytical capacity, governance, and conflicting policy objectives. We recommend that governments act now, while the second generation biofuel industry is in its infancy, to develop robust and proactive policy addressing invasive risk. Policy options to minimize biological invasions include banning the use of known invasive plant species, ongoing monitoring of approved species, and use of buffer zones around cultivated areas.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 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".