Design for the Environment: A Quality-Based Model for Green Product Development
Bibliographic record
Abstract
Green product development, which addresses environmental issues through product design and innovation as opposed to the traditional end-of-pipe-control approach, is receiving significant attention from customers, industries, and governments around the world. In this paper we develop a quality-based model for analyzing the strategic and policy issues concerning the development of products with conflicting traditional and environmental attributes. On the demand side of the problem, we use the framework of conjoint analysis to structure the preferences of the ordinary and green customers. On the supply side, we apply the theories in optimal product design and market segmentation to analyze the producer's strategic decisions regarding the number of products introduced and their prices and qualities. On the policy side, we evaluate the effects of environmental standards on the economic and environmental consequences of green product development. By jointly considering the interactions among the customers' preferences, the producer's product strategies, and the environmental standards imposed by governments, we present some interesting findings that can be used to manage and regulate the development of green products. Two major findings show that green product development and stricter environmental standards might not necessarily benefit the environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".