Bibliographic record
Abstract
Biofuels have drawn the attention of policymakers as a medium to address concerns of energy security, climate change and socio-economic development. The paper examines the biofuel policies of the major biofuel producing nations and analyzes the key instruments being adopted by them during the last decade such as blending mandates, financial incentives, subsidies, import tariffs, greenhouse gas (GHG) emission and carbon trading. The countries are categorized by continent and covers Asia (India, China, Malaysia, Indonesia and Thailand), Europe (European Union, Germany and France), South America (Brazil, Argentina), North America (USA, Canada) and Australia. The paper assesses whether the blending mandates had the desired impact on realization of policy targets and examines the impact of biofuel policies on the socio-economic development and environmental sustainability in these nations. Most countries continue to use sugarcane, corn, grains, and vegetable oils as feedstock for biofuel production thereby raising concerns about their adverse impact on food prices and food availability to the underprivileged people. Biofuel policies for this paper refer to policy instruments, strategies and programs which were established to aid and manage the production and consumption of biofuels - both ethanol and biodiesel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".