Twin Transition – Understanding the Why and Building the How
The Community for Educational Innovation (CEI) hosted a webinar on March 5, 2026: ”Twin Transition – Understanding the Why and Building the How.” This webinar explored the concept of the twin transition, focusing on the relationship between digital innovation and environmental sustainability. It also examined the role of universities, the opportunities and challenges associated with technologies such as AI.
To learn more about the session, you can access the following resources:
Three Expert Perspectives
Stefan Muench, Head of Science Insights, Joint Research Centre (JRC): This presentation introduced the twin transition as the combined transformation driven by digital technologies and the transition towards environmentally sustainable economies. Stefan Muench highlighted five sectors where the interaction between digital and green transitions is particularly important: energy systems, mobility and transport, construction, agriculture and energy-intensive industries. In these sectors, digital technologies can improve monitoring systems, enable predictive modelling, and support more efficient resource management, among other benefits. The presentation emphasised that achieving successful twin transitions requires coordinated action across several dimensions.
Christina Kakderi, Associate Professor of Spatial Development and Innovation Systems, Aristotle University of Thessaloniki; Coordinator, Horizon Europe project ST4TE: Christina Kakderi presented findings which examines how the green and digital transitions are affecting inequalities across European regions. The presentation offered a place-based diagnostic lens across three layers of asymmetry, integrating macro-level regional mapping, occupational data, and results from 402 in-depth qualitative interviews across Europe. The central argument was that, unless addressed proactively, the twin transition risks reproducing rather than reducing existing territorial and social divides. The presentation highlighted three key dimensions of inequality in the twin transition. These include uneven research and training capacity across European regions, unequal access to green and digital job opportunities, and differences in how people experience these transitions in everyday working life. Together, these differences suggest that without targeted policies and institutional action, the twin transition may reinforce existing regional and social inequalities rather than reduce them.
Morten Dæhlen, Professor, Department of Informatics, University of Oslo; Co-Director, The Norwegian Centre for Trustworthy AI (TRUST): Morten Dæhlen framed artificial intelligence as sitting at the core of the twin transition challenge, simultaneously a major environmental burden and a potential catalyst for the green transition. Dæhlen argued that AI holds substantial catalytic potential for the green transition. Applications include smarter energy grids enabling renewable integration, precision agriculture reducing waste, mobility reinvention, preventive healthcare systems, proactive ecosystem stewardship, new materials discovery for energy efficiency, and AI as a force multiplier for scientific discovery. The overarching, unresolved question he posed was whether AI could decouple economic growth from the overuse of Earth’s resources. Dæhlen closed by presenting the TRUST Centre’s framework for trustworthy AI, which positions sustainability, green AI for the green transition, as one of seven core dimensions of trustworthiness, alongside accuracy, safety, explainability, fairness, governance, and scalability.
Key Takeaways
- Synergy, not parallelism. Despite an extensive EU policy framework on twin transition, the green and digital policy streams still largely run in parallel rather than as one integrated agenda. Speakers underlined the need for transdisciplinary research, curricula integration, and institutional strategies that treat the two transitions as genuinely interdependent.
- Equity is a structural, not incidental, challenge. The distribution of twin transition opportunities is uneven across territories and social groups. Advanced research capacity is concentrated, digital jobs are skill-selective and spatially clustered, and the rewards of transition-linked work are stratified. Addressing this requires not only broad access to higher education but also a wider territorial distribution of research capacity and deliberate inclusion policies.
- AI is both a problem and a solution for the green transition. The material environmental costs of AI, in minerals, water, and energy, are real and risk delaying the green transition. Addressing them requires simultaneous action on technology (smaller models, neuromorphic computing, data reduction) and governance (ESG frameworks, KPIs, taxation). Yet AI’s catalytic potential for sustainability applications is equally substantial.
- Universities are central to making the twin transition work. All three presentations converged on the role of higher education institutions as capability pipelines, regional anchors, and transition translators. This includes reskilling through micro-credentials, supporting enabling occupations, and connecting institutional research and teaching strategies to regional development needs.
- What participants prioritised. A Mentimeter poll on embedding the twin transition in education produced the following ranking: training educators in green and digital competencies (29%); redesigning curricula around GreenComp and DigComp frameworks (19%); adopting challenge-based and interdisciplinary teaching methods (19%); engaging students in regional green and digital initiatives (14%); aligning institutional research, teaching, and operations around a coherent twin transition strategy (10%).
- Global dimensions of the twin transition. Participants raised the risk of burden-shifting to the Global South through mineral extraction and data colonialism. The discussion acknowledged that equity must be addressed globally, not only within the EU, and that AI governance frameworks must actively resist the reproduction of Global North worldviews in model design and deployment.
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