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Water: Textile’s Danger—Financial Risks, State of Practice and Moving Forward

In: Water Risk Modeling

Author

Listed:
  • Navya Bhayana

    (West Bengal National University of Juridical Sciences)

  • Laureline Josset

    (Columbia University in the City of New York)

Abstract

The worsening state of water supply on a global level has highlighted the water risks posed by the textile sector. The water depletion and pollution risks posed throughout the production chain result in manufacturing, financial and environmental risks for the companies and investors engaged in the sector. This state of the industry exists despite concrete consequences that companies face, including regulatory penalties and reputational risks. The textile economy, due to its expansion to the labor-oriented developing countries, is globalized, giving rise to complex supply chains undertaking transnational outsourcing. However, unlike carbon emissions tracking, water impacts are predominantly experienced locally, thus requiring disclosure practices that reconcile these two scales. In this chapter, we first introduce a conceptual framework to relate water risks to financial risks. From this, we derive two survey instruments and use them to review the existing reporting frameworks (e.g., the Sustainability Accounting Standards Board, Climate Disclosure Standards Board, the CDP Water Questionnaire and the Global Reporting Initiative) through the lens of the textile industry and evaluate the actual disclosures as practiced by five of the main corporations. By uncovering the inadequacies at both levels, we highlight the risk modeling gaps, and we conclude by discussing the potential and limitations of science-based targets for water to quantify and manage water risks exposure in the textile industry.

Suggested Citation

  • Navya Bhayana & Laureline Josset, 2023. "Water: Textile’s Danger—Financial Risks, State of Practice and Moving Forward," Springer Books, in: Dieter Gramlich & Thomas Walker & Maya Michaeli & Charlotte Esme Frank (ed.), Water Risk Modeling, pages 51-90, Springer.
  • Handle: RePEc:spr:sprchp:978-3-031-23811-6_3
    DOI: 10.1007/978-3-031-23811-6_3
    as

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