Using a Specific Environmental Tool to Assess the Impacts of Biofuels Transport Policies
Bibliographic record
Abstract
Over the past few years the aviation industry has worked hard on how to replace fossil fuels by biofuels that would meet specific aviation criteria. But before implementing expensive biojet fuel manufacturing it seems legitimate to study global environmental impacts of a significant share of biofuels in transports by 2020. For that purpose, a method has been developed to study biofuels policies based on the life cycle methodology and adapted to the macro level to study indirect large scale effects on economic sectors. In this study, an economic general equilibrium model, has been coupled with an environmental Input/Output table in order to provide the amount of substances emitted in the environment by each economic sector in each region of the world when a biofuel policy is implemented. The economic model is also able to provide data for land use change that are then coupled with environmental factors, to obtain greenhouse gas emissions. Finally the GHG emissions from both the changes in economy and changes in land use are compared for two scenarios: one including the biofuel policy, the other assuming a business as usual situation. Results show that, even though the biofuel scenario is designed to mitigate climate change, it seems to cause more impacts. The inclusion of the emissions due to land use change in the global impacts enables to improve the completeness of this method. Nevertheless some improvement need to be achieved in order to adapt the method to the specificities of the aviation biofuels.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".