Analytical study of opportunities for Next Generation Biofuels Market
Bibliographic record
Abstract
The emergence of better science around the issue of land use change associated with the production and usage of biofuels cast doubt on the use of first-generation biofuels, made from edible agricultural feedstocks, as a tool to reduce greenhouse gases (GHG) emissions. While many countries sought to enter the international biofuels market, and draw on the latest technology to move away from crop based biofuels and into cellulosic and algae-based advanced fuels. By 2015, liquid biofuels had become commonly traded commodities worldwide. The impetus behind biofuels was partially due to novel drivers such as green jobs and the interest in the decarbonization of specific sectors of the economy such as transport, but also to provide countries dependent on oil imports with an alternative to enhance their energy security and create new export opportunities.
 In 2015, countries made commitments toward a more environmentally balanced future through the Sustainable Development Goals (SDGs), and now seek to expand policies for low-carbon development after the agreement reached in Paris at COP21. The year also marked a milestone in the bioeconomy, as the point in time when the production of second-generation biofuels (2G) finally took off at commercial scale. Developing countries now face a new set of market opportunities and policy dilemmas to enhance their usage of biomass, which can now be transformed into more valuable products. Developing countries interested in engaging in advanced biofuel markets for the attainment of the SDGs, and as an instrument to meet their commitments under COP21. By carrying out a non-exhaustive mapping of cellulosic ethanol projects and recent policy lessons around the globe.
 Next generation biofuels made from non-edible feedstocks, put forward a promising response to the food versus fuel debate. Nevertheless, process improvements have been a key factor in decreasing costs for the industry and allowing market expansion. Historically, the United States of America (US) has had the largest installed capacity for cellulosic ethanol production of deployed second-generation biofuel facilities, followed by China, Canada, European Union (EU) and Brazil, respectively. Projects in these countries vary significantly in their technological approaches and feedstocks used for fuel production, including the use of corn stover, sugarcane bagasse, municipal solid waste, and forestry residues, among others. One common trait is that companies that possess technology and knowledge in the European Union and the United States engage in partnerships to deploy advanced ethanol facilities abroad, for example, the Fuyiang project, which is a cooperation between Italy-based Beta Renewables and Guozhen Group in China.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".