Imagine a world where some cities get all the high-tech green jobs while others get left behind. This work looks at who wins and who loses as we switch to digital and eco-friendly tech. It uses data to spot these gaps early so leaders can fix them before they become permanent problems.
Companies and regions face a 'green and digital divide' where some areas lack the skills and infrastructure to compete. This creates a risk of investing in regions that are not 'ready' for the twin transition.
A set of quantitative models (econometrics, machine-learning, agent-based) and qualitative case studies to measure regional readiness and social inequalities.
If you are a consultancy dealing with skill gaps in the green economy — this project developed a way to measure skill-related inequalities that helps identify which regions lack the talent needed for the twin transition.
If you are an agency dealing with uneven economic growth in cities — this project developed regional readiness assessments that pinpoint vulnerabilities in NUTS2 regions to better allocate investment.
If you are a software provider dealing with outdated policy tools — this project developed agent-based modelling and machine-learning tools that predict how new digital policies impact different social groups.
Based on available project data, no pricing or cost information is provided as this is a research-funded project.
Based on available project data, the project is currently in the research and modeling phase, focusing on developing recommendations and scenarios rather than a scalable industrial product.
Based on available project data, specific licensing terms are not mentioned, though the project aims to support the exploitation of its results.
The project provides knowledge-based policymaking tools to help design social cohesion and place-based innovation policies.
The project period runs from 2024-01-01 to 2026-12-31.
The consortium is heavily academic, consisting of 6 universities and 1 research institution, which suggests a strong focus on theoretical modeling and data analysis. However, the inclusion of 1 industry partner and 2 SMEs indicates an effort to translate these econometric findings into practical business or policy applications across 6 European countries.
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