Proposals are expected to have their main focus in only one of the following sub-topics:
a. new techniques for modelling and predicting socio/environmental evolution across different temporal and spatial scales. These will combine, analyse and interpret data from in-situ and remote (e.g. satellite) sensing technologies, other public data sources (e.g. historical data, planning documents, legislation), and data or models/theories from human behaviour (including gender differences), economics and the social sciences by making recourse to advanced artificial intelligence techniques, if/as needed. The focus is on modelling and tracking of the interplay between natural and societal systems, for example on how policies and economics modelling predict human behaviours’ impact on the environment, how explicit or implicit incentives and social norms interact with the environment’s evolution and exploitation, how real-time environmental awareness and intelligence can improve behaviour towards more sustainability, or how the decisions based on changes in the environment in turn affect the state of the natural environment.
b. radically novel approaches to resilient, reliable and environmentally responsible in-situ monitoring.In-situ sensing technologies (physical, chemical, biological, behavioural) for environmental monitoring, in particular favouring sensors for parameters and environments that are currently under-sampled but at the same time critical for improving predictive models for understanding environmental processes. Proposals should look for ground-breaking concepts of affordable sensor design and deployment, maintenance, retrieval and/or recycling, based on concepts such as self-deployment, self-awareness, adaptation, artificial evolution, self-repair and controlled decomposition; or using edge computing, distributed Artificial Intelligence or new concepts from micro-robotics to optimise sensing or monitoring frequency. Advanced research on the networking aspects is not addressing this sub-topic.
Projects are to focus on one or a few critical resources (e.g. water, air) and to establish fundamental advances on the most critical challenges that will underpin a step improvement in monitoring, analysis and management of important social/environmental processes for improving quality of life and environmental sustainability (possibly including aspects of waste, noise, …). Citizen involvement, for example for prioritizing resource challenges, data collection, raising awareness towards environmental issues or better understanding of behavioural change in relation to environmental sustainability, is encouraged, in line with the discussion on Responsible Research and Innovation (RRI) in the introduction to this FET work programme. The collected and simulated data should adhere to the FAIR data principle and be compliant with European Standards.
Selected projects under this topic will be expected to collaborate, jointly aiming at delivering a blueprint for a full-fledged system for environmental intelligence.
The Commission considers that proposals requesting a contribution from the EU of up to EUR 4 million and with a duration of up to 4 years would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts or project duration.
new synergies between the distant disciplines of environmental modelling, advanced sensor research, social sciences, and Artificial Intelligence can lead to radically new approaches for creating and using dynamic models of the environment, including predictive modelling, scenario testing and real-time tracking. The ultimate goal is to build a systemic understanding of the socio-environmental inter-relationships, for instance to regulate or design policies and incentives for environmental sustainability and to track their effectiveness over time and to provide intelligible options for adjusting them.
- Enabling new approaches to monitoring, analysis and management of critical resources in Europe;
- Availability of reliable data and models at multiple levels of granularity for environmental policy making;
- Reduced environmental footprint for environmental ICT;
- Increased local and citizen awareness of environmental impacts.