Assessing the Impact of Strategic Implementation of Circular Economy on the Competitive Advantage of Canadian Manufacturing Firms
Bibliographic record
Abstract
This literature-based study assessed the influence of strategically implementing circular economy principles on the competitive advantage of manufacturing firms in Canada. Circular economy strategies, which are characterized by reduced resource input, waste, emission, and energy leakage, have risen in importance due to growing global sustainability concerns. Despite the known environmental and economic benefits of transitioning to a circular economy, a comprehensive understanding of its specific impact on competitive advantage has remained relatively unexplored, particularly within the context of the Canadian manufacturing sector. Through a systematic review and analysis of existing literature, this study illuminated the methods that firms adopted for this strategic transition. Such methods included the establishment of closed-loop supply chains, the integration of eco-design, and the shift towards product-as-a-service models. The study also identified and analyzed the challenges encountered during this process, including technical, financial, regulatory, and market barriers, and how firms strategically adapted to overcome these obstacles. The study finally examined the competitive outcomes of these circular economy strategies, assessing their impact on operational efficiency, cost savings, brand reputation, customer loyalty, and market differentiation. The research suggested that effectively implemented circular economy strategies could significantly enhance the sustainability and long-term resilience of Canadian manufacturing firms, while also contributing to competitive advantage. . Keywords: Circular Economy, Strategic Implementation, Competitive Advantage, Canadian Manufacturing Firms, Literature-Based Study
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".