Factors Influencing Profitability in Eco-design: Lessons from European and Canadian Firms
Bibliographic record
Abstract
Eco-design is a response to the collective desire to engage in sustainable development combining innovation, environment, and profitability and participates in the development of products that serve circular economy. While some authors attempt to provide evidence on the link between eco-design and profitability, very few analyze the drivers of profitability in this case. To reduce this gap, we try to identify factors influencing profitability for eco-designed products. Through direct collaboration with professional organizations, we conduct an original phone survey with European and Canadian firms adopting eco-design. We perform an econometric analysis using a robust order probit regression. The results prove that regulation and market motivations are important factors to achieve superior financial performance. Moreover, firms using rigorous eco-design tools increase the probability to improve their financial performance. We demonstrate that in Europe the motivations and characteristics of eco-design have significant effects on profitability, in Canada only the latter is influential.JEL Codes: O31, Q55
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 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".