Integrating policy, market, and technology for sustainability governance of agriculture-based biofuel and bioeconomic development in the US
Bibliographic record
Abstract
Abstract The scaled-up production of biofuels and bioproducts in the US is likely to cause land use expansion and intensification domestically and internationally, possibly leading to undesirable environmental and socioeconomic consequences. Although these concerns have been widely recognized, sustainability governance systems are yet to be developed. Here, we review (1) the US bioenergy policies, (2) biofuel production and market trends, (3) major sustainability concerns, and (4) existing regulations and programs for sustainability governance, including potential interactions with markets and technology. US bioenergy policy dates back to the 1970s and has evolved over time with various tax incentives plus production mandates in recent key legislation. Commercial production of cellulosic biofuels is impeded largely by technology and cost barriers. Uncertainties exist in the estimates of environmental and socioeconomic impacts due to the lack of empirical data and knowledge of complex relationships among biofuel and bioeconomic development, natural ecosystems, and socioeconomic dimensions. There are various existing sustainability governance mechanisms on which a biofuel sustainability governance system can be built on. Considering all these, we propose an adaptive system that incorporates regulations, certification, social norms, market, and technology for sustainability monitoring and governance, and is able to contribute to addressing the overall environmental concerns associated with collective land use for food, fiber, and fuel production. Building on existing programs and mechanisms and with proper monitoring of biofuel and bioproduct development, such a governing system can be developed and implemented in response to sustainability concerns that may arise as biofuel and bioproduct production increases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".