Barriers and enablers of life cycle assessment in small and medium enterprises: a systematic review
Bibliographic record
Abstract
Abstract Businesses are facing increasing pressure from multiple stakeholders to integrate sustainability into their practices and business models. Although Small and Medium-sized Enterprises (SMEs) represent at least 90% of businesses worldwide and contribute approximately 60% of environmental impacts, assessing and improving their sustainability performance is not a priority for them. SMEs can address sustainability issues through the application of the different Life Cycle Assessment (LCA) approaches. LCA focuses solely on the environment; however, other forms, such as social, costing, sustainability, and organizational LCA, enable practitioners to assess impacts across the entire life cycle of the studied system, each with different scopes and approaches. However, LCA remains in the domain of large companies. This article aims to identify the main barriers and enablers of LCA in SMEs for wider use as a tool to improve sustainability performance. Through a systematic review of the scientific literature on LCA among SMEs applying the Standardized Technique for Assessing and Reporting Reviews of LCA data, a sample of 61 articles provides a 20-year history. Our results characterize the application of LCA in SMEs through six main aspects. Our main conclusions identify three main barriers to the application of LCA among SMEs: lack of trained personnel, lack of data, and high costs. To overcome these barriers, we found that narrowing down the scope using simplified methods in clusters can increase the use of LCA among SMEs. A simplified SME cluster-elaborated LCA can be used to qualitatively identify sustainability hotspots, develop suitable strategies to improve sustainability performance, and respond to market requests.
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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 | 0.057 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".