Tracking India’s rise in renewable energy: Capacity, composition and comparisons
Bibliographic record
Abstract
India has emerged as a major player in the global renewable energy landscape, demonstrating substantial growth in installed renewable capacity over the past two decades. This study employs descriptive statistical analysis of secondary data obtained from the International Renewable Energy Agency (IRENA) to examine India’s renewable energy development in terms of capacity, composition, and global comparison. The analysis reveals that India’s renewable energy capacity grew from 25 GW to 176 GW between 2003 and 2023, representing approximately 4.5% of the global total and ranking India fourth in the world, behind China, the United States, and Brazil. During the same period, non-renewable capacity expanded from 93 GW to 329 GW. Despite this, the share of renewables in India’s total electricity capacity increased steadily from 21% in 2015 to 35% in 2023. However, India lags significantly in per capita installed capacity, standing at only 0.12 kW for renewables and 0.35 kW for total electricity, far below Brazil, China, the U.S., and Canada. The study also highlights India’s technological composition: solar energy leads the renewable mix, followed by nearly equal shares of hydro and wind, with bioenergy contributing only 6.11%. Almost all renewable sources are grid-connected due to high energy storage costs. Unlike global trends, India has no offshore wind or mixed hydro installations and heavily relies on onshore wind and solid biofuels. While India demonstrates a balanced technological approach, significant disparities remain in per capita metrics and technological diversity. These findings underscore the need for targeted policy to improve equity and expand innovation in renewable deployment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".