ExpertiseUpdated on 18 June 2025
Value retention and avoided emissions
Senior Expert LCA for Circularity at RISE Research Institutes of Sweden
Stockholm, Sweden
About
I investigate how retaining product value in circular economy systems—through reuse, refurbishment, and remanufacturing—translates into avoided emissions. My work quantifies the relationship between value retention rates and environmental benefits using life cycle assessment and other sustainability metrics to support evidence-based circular strategies.
Organisation
Similar opportunities
Expertise
Rafael Laurenti, PhD
Senior Expert LCA for Circularity at RISE Research Institutes of Sweden
Stockholm, Sweden
Project cooperation
Circular Adaptation for Road Vehicle Sustainability (CARS)
- Early idea
- Already defined
- Consortium seeks Partners
- Enabling technologies | Network design of reverse supply chains
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Tools and solutions addressing challenges emerging from product focused regulations (such as the ESPR)
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Alireza Ahmadi
Professor-Operation and Maintenance Engineering at Luleå University of Technology-Division of Operation and Maintenance Engineering
Luleå, Sweden
Project cooperation
- Early idea
- Already defined
- Consortium seeks Partners
- Data technologies | Assistance and Expert systems
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Robotic / handling - and assistance systems
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Tools and solutions addressing challenges emerging from product focused regulations (such as the ESPR)
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Vivek Chavan
Research Associate at Fraunhofer IPK
Berlin, Germany