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arXiv · 2606.21530

Exploratory Modelling of Multi-System Transformation Pathways from Real-World Data: A SINDy-Inspired Sparse Orthogonal Regression Technique

Sabin Roman·Vitaliy Soloviy·Klaus Kubeczko
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Abstract

Sustainability transitions unfold through interacting dynamics across social, technological, economic, environmental, and governance dimensions. However, many modelling approaches either isolate subsystems or rely on optimisation-based pathways that do not explicitly represent feedbacks, path dependence, and institutional constraints. This study develops a Sparse Orthogonal Regression Technique (SORT), a data-driven dynamical-systems framework for reconstructing multi-system transformation dynamics from harmonised European indicators. Inspired by Sparse Identification of Nonlinear Dynamical Systems (SINDy), SORT infers parsimonious cross-domain dependencies from observed data rather than specifying a full structural model ex ante. The prototype covers energy, emissions, innovation, digitalisation, resource productivity, environmental stress, policy, finance, wellbeing, and resilience. The compact dynamical system is interpreted as a structural representation of medium-term interactions, not as a forecasting tool. It reproduces differentiated empirical patterns, including steady renewable expansion, nonlinear scaling in transition finance, diffusion-like digitalisation, oscillatory environmental stress, and sustained reduction in ETS-regulated emissions. Forward simulations diverge from simple linear extrapolation where reinforcing feedbacks are present, illustrating the conditional nature of accelerated decarbonisation. By translating inferred dependencies into a feedback structure, the analysis identifies reinforcing decarbonisation sequences alongside balancing ecological constraints. The model contributes to sustainability transitions research by providing an empirically grounded representation of multi-system feedback dynamics that bridges quantitative modelling and transition theory. SORT offers a compact technique for exploratory reconstruction from real-world transition data.

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Sabin Roman, Vitaliy Soloviy, Klaus Kubeczko. 2026-06-19. Exploratory Modelling of Multi-System Transformation Pathways from Real-World Data: A SINDy-Inspired Sparse Orthogonal Regression Technique. https://arxiv.org/abs/2606.21530

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