GECKO will focus on accountable, responsible, and transparent artificial intelligence (ART AI) to address urgent environmental needs and support the European Green Deal and AI ecosystem, ensuring that all citizens benefit from the sustainable green transition. The EC AI flagship report highlights the need for the input of civil society to discuss values being embedded into AI (via social sciences) and responsible design practices (via information sciences) that encode societal values and guidelines into AI system so that they are ethical by design in order to build and retain trust in AI. ART AI is a promising approach for EU, but has not yet reached the level of maturity and urgent research and new research skills are needed to bridge the gap between disciplines. GECKO will establish a synergy between science, humanities, and technology research, currently missing, through a ground-breaking integrated research programme giving Europe a competitive global advantage. GECKO’s ambition is to create a sustainable, European-wide network with a critical and innovative mass, driving innovative, socio-technical AI, driving future policies with ground-breaking and responsible AI, reducing the carbon footprint of the domestic sector, tackling social justice in the energy sector, and leading the pathway towards the first carbon neutral continent. The ‘EU added-value’ for research excellence is unique as it includes: (i) a well-balanced mixture of world-leading expertise in social science, information science, and engineering not present in individual countries; (ii) industrial stakeholders covering different aspects of technology innovation across EU; (iii) access to diverse data, resources, and stakeholders across the EU enabling public debates in different fora to translate societal values into AI system design strategies; (iv) studying cross-country (-cultural) related issues related to home technology use in civil society, identifying commonalities and differences.

Eco-Bot aims to utilize recent advances in chatbot tools and advanced signal processing (i.e. energy disaggregation) using low-resolution smart meter-type data with the goal of changing their behaviour towards energy efficiency. Eco-Bot targets to a personalized virtual energy assistant to deliver information on itemized (appliance-level) energy usage through a chat-bot tool. The "chat-bot" functionality will be use an attractive frontend interface, permitting seamless communication in a more natural and interactive way than a traditional mobile application. This way, Eco-Bot aims to achieve a higher level of engagement with consumers than previous efforts (i.e. serious games, gamification, competitions or other interactive ICT), by adding a more engaging form of interaction with existing platforms that has been proven in different market settings. The proposed system considers knowledge of the delivered multi-factorial models, including rebound-effects, as a result of the baseline research on both European and International activities. Then, based on advanced ICT, such as knowledge engineering, machine learning, expert systems, the project transforms the multi-factorial models for energy reduction to interactive, personalized and targeted recommendations to consumers on how to save energy. Eco-Bot uses also existing NILM, e.g. energy disaggregation methods, and data analytics to break down consumption to the appliance level, where this is possible (smart meters at reasonable granularity, adequate number of information collected) so as to make consumers aware of their most energy-consuming devices. The project will demonstrate the system in three different use cases, each one representing a different business model (B2B / B2B2C /B2C). We aim to validate our system across real and diverse conditions such as socio-cultural, environmental, demographic, climate and consumption, so as to draw concrete conclusions regarding performance, effectiveness, affordability, etc.

ChArGED addresses the energy consumption in public buildings and proposes a framework that aims to facilitate achieving greater energy efficiency and reductions of wasted energy in public buildings. The framework leverages IoT enabled, low-cost devices (NFC or iBeacons) to improve energy disaggregation mechanisms that provide energy use and (consequently) wastages at the device, area and end user level. These wastages will be targeted by a gamified application that feeds personalized real-time recommendations to each individual end user. The design of the game will follow a cleanweb approach and implement a novel social innovation process that will be designed based on human inceptives factors and will help users to understand the environmental implications of their actions and adopt a more green, active and responsible behaviour. The blend of social interaction and competitions with its personalized character are expected to eventually contribute to the user engagement and commitment to generate savings in the long term leading to tackle energy efficiency targets in public buildings while emphasizing on cost effectiveness. Furthermore, users will become more educated on energy efficiency actions and their impacts which has an impact beyond the actual public building. Efficient energy use will render its consumption predictable and this will be exploited by the ChArGED gamified application to optimize use of the micro-generated energy. Users will be motived to reduce energy consumption when power comes from the grid. Predictable energy consumption will also support more informed decisions of micro-generation sources to match the use patterns. The ChArGED solution will be developed with iterative end users representatives’ engagement during analysis, design and development. Further users at least 150 real building occupants in three (3) countries (50 in each building- validation country) will be engaged for deployment and validation.

Pharmaceuticals have undoubtably made our world a better place, ensuring longer and healthier lives. However, pharmaceuticals and their active metabolites are rapidly emerging environmental toxicants. It is thus critical that we fully understand, and mitigate where nec-essary, the environmental impact resulting from their production, use and disposal. In this direction, ENVIROMED addresses two aspects of the environmental impact of pharmaceuticals, a) impact of the processes in manufacturing the compound, and b) impact of the compound itself, during its lifecycle. The project narrows the knowledge gap when it comes to the effect of pharmaceutical compounds, and their derivatives, in the environment as it enables the better understanding the environmental impact of such compounds, throughout their lifecycle. It aims to offer (via extensive monitoring campaigns & scientific studies) information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity (via in-vitro & in-vivo models) as well as application of in-silico methods to provide information about the basic risk management and fate prediction in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals will also be taken into account, in order to have a comprehensive approach to pharmaceuticals' Lifecycle Assessment (LCA). Moreover, the project aims at developing a set of technologies that enable greener and overall, more efficient pharmaceuticals production, which include: a) Green-by-design in-silico drug development; b) Novel sensing to allow reduction of rinsing chemicals and cycles; c) a robust Continuous Biomanufacturing line (CBM), which makes use of AI-enabled process optimisation and prediction, using data assimilation based on chemical sensing and energy disaggregation/monitoring. Training activities and a robust exploitation

ExPEDite aims to create and deploy a novel Digital Twin (DT) for real-time monitoring, visualization, and management of district-level energy flows. The project will deliver a suite of replicable modeling tools, allowing stakeholders to analyze planning actions towards positive energy and climate neutrality in a cost-effective manner. The tools will be able to model the district’s energy production and demand, building stock performance, optimize for flexibility and simulate mobility and transport. The DT’s design-platform will analyze various what-if planning actions, aiding energy and urban planners’ evidence-based decision-making processes, while its run-time engine will optimize the districts energy utilization efficiency. The DT will follow a modular open architecture to support multi-sectoral and multi-organizational stakeholder requirements. By employing gamification and co-creation approaches, the project will enhance public awareness and engagement in energy efficiency. The ExPEDite DT will be applied to a district in Riga, Latvia, and will provide practical guidelines, reusable models, algorithms, and training materials to aid other cities in replicating the DT for their districts, fostering widespread adoption of sustainable energy practices.