Measuring Environmental Performance in Digital Transformation within SMEs
Bibliographic record
Abstract
Climate change and industrialization have led to major environmental challenges. Manufacturing Small and Medium Enterprises (SMEs), play a crucial role in this context, contributing significantly to emissions and energy consumption. Industry 4.0 (I4.0), with its technological advancements, offers opportunities to improve the environmental impact (EI) of SMEs. However, the increase in automation and digitization raises questions about their true ecological impact. This paper identifies a critical gap in the literature: the lack of alignment between environmental indicators and digital transformation (DT), which complicates the ability of SMEs to measure and enhance their ecological footprint. This research aims to bridge this gap by identifying and compiling relevant environmental indicators for SMEs undergoing digital transformation. Employing a mixed-method approach through an extensive literature review in SCOPUS and subsequent thematic and content analyses, this research aims to identify and compile relevant environmental indicators for SMEs in DT. The paper’s findings categorize environmental indicators into three groups: resource usage, environmental repercussions, and durability. These indicators provide a framework for assessing the EI of SMEs in DT.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".