Analytical study of opportunities for Next Generation Biofuels Market
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».