Biodiesel Blending and Price Competitiveness with Diesel on Indian Roads
Bibliographic record
Abstract
Much of the demand for energy has been drawn from coal; yet, India continued to rely on the import of crude oil to meet the growing domestic demand. As a result, to reduce the import dependence, biofuels have been considered as one of the ideal substitutes/additives for fuels. However, the applicability of biofuels over the conventional fuels is yet to be assessed, particularly in the context of Indian roads, in terms of pricing, availability and processing per se. This paper has analysed the price competitiveness of biodiesel with diesel using secondary data. Due to different densities of biofuel and diesel, the cost of biodiesel per unit has been expressed as per litre ‘diesel equivalent’ prices to enable their pricing competitiveness. Accordingly, biodiesel per litre diesel equivalent price (with and without taxes and other charges) for different percentages of blending has been worked out. The cost differential between ex-storage price and retail price of diesel appeared to be influenced by the sales tax and levies (tax components) in the states. A comparative study on the ex-storage price of diesel with the net cost of production of Jatropha-based biodiesel, has revealed it to be uncompetitive and uneconomical to blend with biodiesel in RSMML plant, but competitive in CBDA and Nandan Cleantec plants. However, when the energy content is taken into consideration, blending of biodiesel with High Speed Diesel (HSD) at exstorage point and retail level does appear to be encouraging and competitive in CBDA plant on Indian roads. Inspite of biodiesel being a cheaper fuel per kilometre, the unavailability of biodiesel feedstock acts as a wet blanket in achieving its competitiveness with diesel. Therefore, adoption of better agronomic practices, suitable pricing policies, awareness generation and promotion of Jatropha crop along with proper follow-up and policies are necessary for successful implementation of the programme.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".