Scholarlink

The Corporate Sustainability Reporting Directive (CSRD) has been in effect throughout the European Union since January 1, 2024, requiring companies to disclose their sustainability activities. Along with qualitative data, companies must also provide numerous quantitative key figures that were not previously required. Central to these disclosures is the double materiality assessment (DMA), which determines the relevant sustainability quantitative and qualitative data for each company. This paper explores the automation of DMA as a solution for businesses, particularly small and medium-sized enterprises (SMEs), which may lack the resources for detailed sustainability reporting. This paper explores the automation of DMA as a transformative solution for businesses, facilitating the initial and subsequent compilations of sustainability reports. It specifically seeks to identify and evaluate existing automation techniques that can effectively support this analysis, thereby answering a critical question: Which automation approaches are best suited to increase the efficiency and accuracy of a DMA methodology? Through a literature review, suitable AI-driven automation techniques are identified to enhance the efficiency and accuracy of DMA. The research proposes a new approach to help SMEs embrace sustainability reporting as a strategic opportunity, simplifying compliance while improving operations.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond Compliance - Towards Automated Double Materiality Analysis Under the CSRD Framework

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

查看全文
  • Maximilian Nowak,
  • Josef Baumüller,
  • Ruben Hetfleisch,
  • Sebastian Schlund,
  • Stephan Martineau

摘要

The Corporate Sustainability Reporting Directive (CSRD) has been in effect throughout the European Union since January 1, 2024, requiring companies to disclose their sustainability activities. Along with qualitative data, companies must also provide numerous quantitative key figures that were not previously required. Central to these disclosures is the double materiality assessment (DMA), which determines the relevant sustainability quantitative and qualitative data for each company. This paper explores the automation of DMA as a solution for businesses, particularly small and medium-sized enterprises (SMEs), which may lack the resources for detailed sustainability reporting. This paper explores the automation of DMA as a transformative solution for businesses, facilitating the initial and subsequent compilations of sustainability reports. It specifically seeks to identify and evaluate existing automation techniques that can effectively support this analysis, thereby answering a critical question: Which automation approaches are best suited to increase the efficiency and accuracy of a DMA methodology? Through a literature review, suitable AI-driven automation techniques are identified to enhance the efficiency and accuracy of DMA. The research proposes a new approach to help SMEs embrace sustainability reporting as a strategic opportunity, simplifying compliance while improving operations.