ECOWISR (Excessive Consumption, Overproduction, and Waste: Impact Solutions through Reduction): The case of the fast fashion industry
Bibliographic record
Abstract
The fashion industry faces environmental challenges, including overproduction, waste, and impactful production processes. This thesis examines how a deeper understanding of consumer behaviour can help retailers develop more attractive assortments and adopt sustainable practices. By studying and integrating richer behavioural models to assortment decision, this thesis provides data-driven insights to reduce waste and environmental impacts, as well as targeted policy recommendations that promote sustainability and collaborative action across the industry. In the first manuscript, in collaboration with a large European fast fashion retailer, we examine how assortment variety affects customer choices. Using a large clickstream dataset, we model assortment variety as a bipartite graph along three main dimensions: styles, colours, and graph density. Using a richer choice model, we identify three main customer segments with varying preferences for economic and variety variables. Our findings emphasize the need for retailers to balance offering a wide variety with efficient management and accounting for heterogeneity in customer preferences. The second manuscript addresses confusion and the lack of regulation around environmental impact in the fashion industry. By combining experimental choice data to measure preferences to a product line and pricing optimization model, and counterfactual simulation, it studies how a retailer can design and price a product line that balances profitability and environmental impact. We find that Price is the primary driver of consumer choice with limited sensitivity to environmental attributes. However, our counterfactual simulation shows that increased consumer awareness on environmental attributes can raise the importance and willingness-to-pay for environmental attributes, reducing the profitability-environmental trade-off. Our findings suggest that gradual policies are needed to bridge the gap between consumer intentions and behaviour, and support retailer’s shift toward more sustainable practices
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".