Evolution and Evaluation of Energy Policies in India Between 2000 and 2017
Bibliographic record
Abstract
India, as one of the world's largest energy consumers, has implemented a wide array of energy policies to meet the needs of its substantial population of 1.4 billion. This paper provides a comprehensive analysis of these policies, from the establishment of the National Electricity Board in 1950 to the introduction of the Pradhan Mantri Ujjwal Yojana in 2017. Covering a broad spectrum of energy forms, including electricity and LPG, it illuminates the evolution of India's energy policies and their effectiveness. Significant strides have been made since the 1950s when the government initiated rural and urban development programs. Landmark actions such as the establishment of nuclear power plants and the initiation of renewable energy source research and development have been critical in addressing the energy requirements of India's growing population and booming industries. Public domain data evaluation reveals a mixed performance of various schemes. While some have fallen short of their targets, others like the Saubhagya scheme have made impressive progress towards achieving universal electrification, with about 90% of households in India now having electricity access. However, concerns remain regarding the quality and reliability of this electricity provision. Furthermore, significant efforts are still required to adequately address the needs of institutional clients, small enterprises, and agricultural consumers. As the population continues to grow, it is recommended that an efficient evaluative strategy be established to monitor and address any emerging gaps in energy provision.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".