Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) are set to play an increasingly crucial role in the ecological transition amid rising demand for sustainability. Many expectations are placed on artificial intelligence (AI) as a lever for responsible transformation. However, its deployment raises ecological concerns, particularly the energy and resource intensity of digital infrastructures. This article explores the potential of green AI, an approach reconciling technological performance with sustainability principles, as a driver of SME sustainability. Based on a literature review and a qualitative case study with DigiTips, a Quebec SME, the study highlights the advantages, challenges, and enabling conditions of sustainability-oriented AI. Findings reveal that the contribution of green AI depends on dimensions such as project intentionality, strategic alignment, responsible governance, type of AI mobilized, and tools for monitoring environmental impacts. By addressing these considerations, the article proposes a conceptual framework offering key insights into how green AI can serve as a credible driver of sustainability in SMEs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".