Nicos Komninos

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance.

Nature-based solutions (NBSs) are central to urban climate-neutrality strategies, but their implementation still lags behind policy ambition. One reason is that most NBS benefits are public goods and rarely generate direct revenue, so we know far less about the institutional and financial conditions needed to deliver them than about their ecological performance. This paper develops a business model framework linking NBS interventions to financing and governance configurations, applying established NBS typologies and the Pestoff Triangle of state, market, and community provision to thirteen interventions across eleven sites in the Railway District of Thessaloniki, Greece. The interventions are organised into five business model categories: public space greening, private space upgrades, public–private hybrids, building retrofits, and renewable energy. Their financing logic is shaped less by intervention type than by ownership structure and stakeholder configuration. To address this gap, we followed category-specific blended finance strategies combining grants, private investment, regulatory incentives, and community resources, underpinned by stewardship-oriented governance. The findings suggest that scaling NBSs depends less on technical readiness than on institutional capacity to match financing and governance to local ownership and stakeholder contexts, offering a pathway from climate-neutrality strategy to implementable urban regeneration.

Manufacturing sectors and ecosystems are undergoing profound transformations as digital platforms converge with generative and agentic AI. This paper argues that manufacturing ecosystems can improve their innovation performance through digital platforms, connected intelligence, and collaborative settings that bring together experts and ecosystem members. This convergence of distributed capabilities across humans, communities, and machines generates intelligent environments that enable ecosystemic and transformative innovation. To examine this hypothesis, the paper follows a three-stage methodology. First, it proposes a modelling framework based on a vector autoregressive model, in which a weighted matrix representing binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, it presents the SmartGreenEcos experiment, which creates an intelligent environment adapted to a manufacturing ecosystem by combining digital platforms, e-services, and AI agents to support inter-company collaboration, experimentation and innovation. Third, it conducts a simulation-based analysis of the internal dynamics of intelligent environments, with particular attention to the eigenvalues and eigenvectors of the weighted matrix representing connected-intelligence couplings. The results provide insights into the design of intelligent environments and the interaction parameters of connected intelligence that drive innovation, with relevance not only for manufacturing ecosystems but also for other sectoral ecosystems seeking to enhance innovation through intelligent environments.