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Record W4367017400 · doi:10.1049/icp.2023.0313

Green innovation for sustainable development

2023· article· en· W4367017400 on OpenAlex
Amrita Chaurasia, Ruchi Tyagi, Suresh Vishwakarma

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET conference proceedings. · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsSustainable developmentBusinessComputer sciencePolitical science

Abstract

fetched live from OpenAlex

In recent years, organisations have notably taken up Green Innovation for sustainable development to maintain the customer base and keep the natural environment safe. Consumers are aware of the current environmental issues, such as global warming and the consequence of environmental pollution. As a result, organisations are demanding to craft green strategies and embryonic to advance holistic methods towards maximising shareholders' values. This paper attempts to provide valuable insights into the going green concepts and their association with the value creation in the automobile industry regarding the e-vehicle by examining the effects of green innovations in Mahindra Electric Mobility Limited, India towards the launching of Mahindra e2oPlus on the Reva platform. Furthermore, the authors analyse the performance of this innovation on the organisational financial performance with the help of event study methodology.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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