This study evaluates the effectiveness of the European Union's Corporate Sustainability Reporting Directive (CSRD) in quantitatively measuring the transition of companies to a circular economy. First, using the most recent literature review on circularity metrics, a complete overview of the currently available circularity metrics is developed. Subsequently, it is determined which circularity metrics can be generated with the available quantitative datapoints of CSRD. The metrics that can be generated were analyzed on their ability to cover all circular strategies, to represent different Product-as-a-Service systems and to acknowledge the key role of Critical Raw Materials in a circular economy. The study finds that, with data disclosed under CSRD, metrics can be generated to cover all circular strategies. However, gaps remain in representing pay-per-use and pay-perperformance systems and the use of Critical Raw Materials. Recommendations are to include ‘Product utilization’ and ‘Mass of Critical Raw Materials used’ in the data disclosed under CSRD and to have an independent institution report data to enable benchmarking of performances. Finally, this study concludes with an overview of the metrics which enable to measure circular transitions using data disclosed by CSRD
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Purpose: The purpose of this study is to find determinants about risk resilience and develop a new risk resilience approach for (agricultural) enterprises. This approach creates the ability to respond resiliently to major environmental challenges and changes in the short term and adjust the management of the organization, and to learn and transform to adapt to the new environment in the long term while creating multiple value creation. Design/methodology: The authors present a new risk resilience approach for multiple value creation of (agricultural) enterprises, which consists of a main process starting with strategy design, followed by an environmental analysis, stakeholder collaboration, implement ESG goals, defining risk expose & response options, and report, learn & evaluate. In each step the organizational perspective, as well as the value chain/area perspective is considered and aligned. The authors have used focus groups and analysed literature from and outside the field of finance and accounting, to design this new approach. Findings: Researchers propose a new risk resilience approach for (agricultural) enterprises, based on a narrative about transforming to multiple value creation, founded determinants of risk resilience, competitive advantage and agricultural resilience. Originality and value: This study contributes by conceptualizing risk resilience for (agricultural) enterprises, by looking through a lens of multiple value creation in a dynamic context and based on insights from different fields, actual ESG knowledge, and determinants for risk resilience, competitive advantage and agricultural resilience.
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Poster KIM voor de ECR is nu online te zien via EPOS: https://epos.myesr.org/poster/esr/ecr2022/C-16092 posternummer: C-16092, ECR 2022 Purpose Artificial Intelligence (AI) has developed at high speed the last few years and will substantially change various disciplines (1,2). These changes are also noticeable in the field of radiology, nuclear medicine and radiotherapy. However, the focus of attention has mainly been on the radiologist profession, whereas the role of the radiographer has been largely ignored (3). As long as AI for radiology was focused on image recognition and diagnosis, the little attention for the radiographer might be justifiable. But with AI becoming more and more a part of the workflow management, treatment planning and image reconstruction for example, the work of the radiographer will change. However, their training (courses Medical Imaging and Radiotherapeutic Techniques) hardly contain any AI education. Radiographers in the Netherlands are therefore not prepared for changes that will come with the introduction of AI into everyday work.
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Abstract: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. Clinical relevance statement: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. Key Points: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.
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The DPP4CD project, “Digital Product Passport(s) for Circular Denim: From Pilot to Practice,” focuses on delivering pilot and scalable Digital Product Passports (DPPs) in the circular denim industry. This aligns with the upcoming European Ecodesign for Sustainable Products Regulation (ESPR), making DPPs mandatory for textiles from 2027. A DPP for circular denim should clearly detail material composition, production methods, repair records, and recycling options to meet EU rules like ESPR, Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS). It combines dynamic lifecycle data into a standard, interoperable system that boosts traceability, cuts SME admin burdens, and supports sustainable, circular practices. Led by Saxion and HvA, the multidisciplinary project is based on a real-world Dutch use case with MUD Jeans, a leader in circular denim. The project combines circular economy principles with existing digital technologies, working with partners such as tex.tracer, Tejidos Royo, bAwear, Denim Deal, MODINT, EuFSI and, GS1 Netherlands. Instead of developing new tools, the project applies scalable technologies (augmented DPP extension) and methods e.g. blockchain, life cycle assessments, and traceability standards to denim supply chains. The project defines legal, environmental, technical, and user requirements for DPPs in circular denim and designs a modular, data-driven, and ESPR-compliant system that integrates offline and online components while ensuring interoperability, affordability, reliability, accountability, and scalability. It develops a data framework for material tracking, supported by interoperable digital solutions to improve data-sharing and transparency. A pilot DPP with MUD Jeans will cover the full lifecycle from production to recycling, enabling scalable DPP. The project aims to address societal challenges related to circularity, ensure scalable and implementable solutions, and create a digital platform where knowledge can be developed, shared, and utilised. By combining circular practices with digital technologies, DPP4CD will help textile businesses transition towards sustainable, transparent, and future-proof supply chains.
Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.
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