Socio-Technical Drivers for Community Renewable Energy Systems – Analysis of Case Studies from India
Bibliographic record
Abstract
Renewable energy sector of India has the potential to be a credible alternative for households and communities. The sector accounts for approximately 13 per cent of the national energy capacity, including technologies such as solar grid/ off – grid generation, wind power, small hydro power, biomass/ biogas, and waste to energy. Statistics related to implementation illustrate the scope of tapping the renewable energy sector comprehensively, esp. for decentralized, household and community level applications. This paper analyses on – ground case studies and projects from various renewable energy categories, specifically the socio – technical and community aspects that play a significant role in successful Renewable Energy Technologies (henceforth RET) implementation. The distilled positive and negative drivers include technical, economic considerations, policy and regulatory, environmental aspects, market/ business models, and social/ community linked aspects. Case studies describe the need for a system based and stakeholder engagement approach where all possible stakeholders and their issues are envisaged and taken into account. Use of an evaluating framework is suggested to ensure successful community models of RETs and mass adoption of renewable technologies.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".