Biodegradable alternatives offer a promising solution to plastic pollution and waste littering in the open environment in some specific contexts, as they break down naturally under specific environmental conditions. However, it is often reported that many biodegradable plastics do not fully degrade in their receiving environment. MAGICBIOMAT aims at developing a portfolio of circular bio-based materials with programmed biodegradability demonstrated through 2 applications with highly concerning rates of littering: mulching films and paper-based packaging, tested in open environments conditions (soil, fresh water and marine) and different EU climates. Moreover, MAGICBIOMAT will address circularity by improving the durability of the developed bio-based materials for extending lifespan of products, as well as assessing mechanical recycling, remanufacturing, and reuse. To enable programming of biodegradability of the bio-based materials, MAGICBIOMAT will develop a trustworthy Artificial Intelligence (AI) powered software to guide the design and manufacturing of novel materials according to applications requirements (properties, manufacturing) and end-of-life needs and conditions, exploiting data from the project material development and biodegradation tests, complemented with open access data. This tool will foster adoption of novel bio-based biodegradable materials by the industry. Prevention of waste littering will also be tackled through consumers’ and end users’ perspective, for which behavioural studies will be carried out to develop interactive labelling and behaviour change strategies that foster user-acceptance of the novel biodegradable materials.

Artificial Intelligence (AI) is increasingly used in the employment sector to manage and control individual workers. One type of AI is Natural Language Processing (NLP) based tools that can analyze text to make inferences or decisions. A recent Sage study found that 24% of companies used AI for hiring purposes. In an employment context, this can involve analyzing text created by an employee or recruitment candidate in order to assist management in deciding to invite a candidate for an interview, to training and employee engagement, or to monitor for infractions that could lead to disciplinary proceedings. However, the models that NLP-based systems are based on are biased. Additionally, it has been shown that bias in an underlying AI model is reproduced in applications based on that model). This can lead to biased decisions that run contrary to the goals of the European Pillar of Social Rights in relationship to work and employment, specifically Pillar 2 (Gender Equality), Pillar 3 (Equal Opportunity), Pillar 5 (Secure and Adaptable Employment) and the United Nations’ (UN) Sustainable Development Goals (SDGs), specifically SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth). It is therefore necessary to identify and mitigate biases that occur in applications used in a Human Resources Management (HRM) context. Addressing such concerns in an employment context is especially relevant, as most existing European studies on employment discrimination have indeed found that discrimination exists, both when considering individual diversity criteria and multiple criteria in intersectional analyses. In order to investigate and mitigate these biases, we apply this “BIAS”-project, for mitigating diversity biases of AI in the labor market. The chief technical objective of BIAS is the development of a proof-of-concept for an innovative technology based on Natural Language Processing (NLP) and Case Based Reasoning (CBR) for use in an HR recruitment use case.

The textile industry is the fourth largest industry in the world with the global volume of fiber production for textile manufacturing reaching 110 million metric tons in 2020. At the same time, the textile industry is one of the most polluting industries worldwide with the highest greenhouse gas (GHG) emissions corresponding to 10% of the global emissions. Polyester (PET) is the most widely used fibre in the industry, making up 52% of the global market volume. No technology available today is capable of addressing the textile industry’s sustainability and virgin PET produced from primary petrochemical sources remains predominant with a fossil fuel consumption of 98 Mt annually which is expected to reach 300 Mt by 2050. Addressing the key challenges of carbon neutrality, circularity, cost, value chain adaption, and textile properties is the ambition of Threading-CO2, a disruptive project that will demonstrate on an industrial scale a first-of-its-kind technology that converts CO2 waste streams into sustainable PET textiles. Threading-CO2 aims to scale-up and demonstrate its first-of-its-kind technology producing high-quality commercially viable sustainable PET textile products from CO2 waste streams at industrial scale (TRL7) using a circular manufacturing approach and running on renewable energy sources. The overall outcome of the Threading-CO2 project is a 70% GHG emissions reduction compared to existing PET manufacturing processes. In addition, Threading-CO2 will enable the creation of a European value chain for sustainable PET textiles, from feedstock to final textile products in the clothing, automotive and sports/outdoor industries.

openDBL intends to integrate multidisciplinary know-how to cover the requirements of the Call and solve the issues of the current situation. The challenge of the project is to allow, through the development of openAPI, the disposal of openDBL in a unique standardized platform and create useful content, to simplify the workload of the AECO industry. The project pursues 3 objectives: 1) create a DBL with useful content and functionalities, 2) ensure openDBL is usable and simple to use, reducing the time spent to upload, search and process the information and data to facilitate usage and gain wide adoption, 3) ensure attractive economics, through value propositions and convenient pricing. We’ll provide any user with an integrated platform for their digitization needs; ensure that information and data conform to the latest trends and needs of our target clients and support the EU's circular economy and green policies; develop automatic classification systems and data standards; facilitate the operation and maintenance activities of the buildings. This will be achieved creating an Information Delivery Manual and a Data Model and further developing our existing platform used to create a DBL for an important Italian Public Contracting Authority. openDBL will support data matching with external databases and will integrate state of-the art technologies (AI, Blockchain, IoT and VR). Our ambition is to make openDBL the platform of reference for the monitoring of building consumption, transparencies of transactions and official documents, and the positive impact on maintenance and environment.

Agriculture is being managed more tightly than ever before and is generating more data than ever before, but the potential of a data economy in agriculture remains unexplored. The reasons for this are varied, and include technical interoperability, business relationships between stakeholders, and social acceptability issues around data ownership and market transparency. Individual stakeholders make use of the data they generate at their own particular stage in the agri-food supply chain. However, the sharing of this data with others along the chain and its collective analysis needs more development and demonstration if more efficiencies are to be introduced and further value added to the agri-data economy. While some sharing is taking place on an ad-hoc basis, each new set of potential data sharers must start from scratch and work through the same issues common to all such arrangements. Equally, the lack of data sharing precedents in agriculture inhibits data owners from taking a more exploratory view of the world. Several dimensions must be considered in policy-making if a fully functioning data economy in the agriculture domain is to emerge. Such a multi-disciplinary approach is at the core of the DIVINE consortium, which encompasses technical (agriculture and ICT), markets, and social sciences expertise. It will build an agri-data ecosystem that incorporates existing common agri data spaces while deploying industry-led pilots built on data sharing arrangements, to demonstrate the cost-benefit and added value in sharing agri data. DIVINE will assess its ecosystem at the level of policy impacts, the uptake of digital technologies, and economic and environmental performance. DIVINE will promote its ecosystem and its assessments to technology providers, policy-makers, farm representatives, and various other agri-data stakeholders. It will take the first real concrete steps towards mature data markets in European and global agriculture.