A framework for integrating sustainable production practices along the product life cycle
Bibliographic record
Abstract
In response to urgent global challenges posed by climate change and environmental degradation, integrating sustainable production practices into the entire product life cycle (PLC) has become essential. This paper proposes a comprehensive framework addressing the gap between sustainability models and product life cycle assessment (PLCA), emphasizing the need for a holistic approach encompassing economic, social, and environmental dimensions. The framework outlines optimal sustainable practices from material extraction to end-of-life disposal. It emphasizes reduced ecological footprints, resource conservation, pollution mitigation, and enhanced sustainability. Furthermore, it underscores the role of governmental and non-governmental organizations (GOs and NGOs) in promoting this integrated approach. This research further explores key questions about integrating sustainable practices, implementation challenges, and economic feasibility, aiming to guide businesses toward holistic approaches that balance economic growth, environmental stewardship, and social equity across the entire PLC. • Integrating sustainability across all product life cycle stages reduces ecological footprints, boosts efficiency, and minimizes pollution. • Research offers strategies to overcome challenges in adopting sustainable practices and enhances industry implementation. • Policy and resource support from governmental and non-governmental organizations are key to sustainable production transitions. • The proposed framework highlights the economic feasibility of sustainable practices for businesses.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".