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Record W4388097540 · doi:10.18280/ijsdp.181022

Evolution and Evaluation of Energy Policies in India Between 2000 and 2017

2023· article· en· W4388097540 on OpenAlexvenueno aff
Nigam Dave, Sriram Divi, Neeta Khurana

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsElectrificationRural electrificationRenewable energyElectricityPopulationBusinessGovernment (linguistics)Quality (philosophy)Environmental economicsEconomic growthNatural resource economicsEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.276
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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