Analysis of Environmental and Social Performance of Sustainability-linked Bonds and Loans (SLBLs) in the Fashion Industry
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».