Analysis of Environmental and Social Performance of Sustainability-linked Bonds and Loans (SLBLs) in the Fashion Industry
Bibliographic record
Abstract
The fashion industry has been criticized for its adverse effects on the environment and society. To address this, fashion retailers and brands are increasingly using Sustainability Linked Bonds and Loans (SLBLs) which aim to fund their operations in sustainability related activities and goals. However, there is a lack of academic research on the sustainability objectives targeted by these financial tool. Moreover, they are criticized for being driven by market participants rather than addressing sustainability concerns. This study aims to analyze the environmental and social performance of these SLBLs issued by fashion retailers and brands, using the Higg BRM, which is a comprehensive sustainability assessment tool which, using a set of questions and guidelines, helps companies measure and improve their sustainability performance solely for the apparel and footwear industry. The study used an exploratory sequential mixed method research approach, conducting content analysis of bond, sustainability and loan reports of SLBLs issuing fashion brands and retailers and coding based on the Higg BRM guideline. The findings suggest that three main sections of the Higg BRM namely brand, store, operations and logistics are primarily addressed through sub-sections related to the environment. 100% of SLBLs focus on reducing Greenhouse Gas (GHG) emissions, and nearly 65% focus on sustainable materials used in products. Other factors such as packaging and water and wastewater management are covered in around 30% and 20% respectively. However, other environmental issues like water usage, wastewater management, post-consumer waste, and social and human rights issues associated with the fashion industry have the lowest coverage on the Higg BRM scale. \nIn comparing information from sustainability reports, bond reports, and loan reports, the reports’ respective adherence to the Higgs BRM questions was explored. It was found that sustainability reports covered 2% more Higgs BRM questions overall compared to bond or loan reports. Regarding specific topics, bond or loan reports covered 53% of the questions related to GHG emission reduction, while sustainability reports covered 47%. In the product section, sustainability reports addressed 33% of the related questions, while bond or loan reports covered 26%. Similarly, in terms of water and wastewater, sustainability reports had slightly over 30% coverage, which was 10% higher than that of bond or loan reports. Finally, in terms of packaging and other sections, both reports had the same coverage numbers, 35% and 100% respectively. \nUtilizing the study's findings will help the decision-makers who develop SLBLs in the fashion industry to develop SLBLs that emphasize broader material sustainability concerns. Furthermore, this research can aid institutional and private investors in gaining a clear understanding of the current emphasis placed on SLBLs within the fashion industry and be a catalyst to help bridge the gap between sustainability concerns addressed by SLBLs. This research will enhance the existing literature on sustainability finance within the fashion industry by presenting a comprehensive overview of Sustainable Linked Bonds and Loans (SLBLs) in this sector. Future researchers can leverage this study as a foundation for conducting in-depth investigations into investor behavior within the realm of SLBLs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".