ASSESSMENT OF WIND ENERGY IN INDIA AT THE NATIONAL AND SUB- NATIONAL LEVEL: ATTRIBUTIONAL LCA EXERCISE
Bibliographic record
Abstract
The Government of India has set the target of 60 GW wind energy capacity to be achieved by 2022. The Attributional Life Cycle Assessment methodology and E3-India model are used to study the total economic, energy, and environmental impact of the operational phase of wind turbines across the country. It is expected to generate waste from 2021 until 2051 at the rate of 217.89 tonnes/wind turbine, cumulatively amounting to 7.9 million tonnes. This generates an opportunity for recycling and resale of the metal materials worth USD 4.5 billion, which can be utilized in manufacturing 3.3 GW of wind turbines, thus emphasizing its high circular economy potential. The embodied energy and CO2 emission savings by substituting virgin material are estimated to be 17,215 GWh and 6,626 million tonnes, respectively. Thus, measures like Extended Producer Responsibility could help create a viable circular economy through the partially closed-loop recycling of wind turbines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".