Emergent Behavioural Patterns in Energy Systems: A Systems‐Analytics Framework for Renewable Investment Decisions for G20 Countries

This study takes the conceptualization of sociotechnical systems of different nation‐states regarding systems and behavioural research and studies investment decisions on renewable energy as emergent behaviours of these systems. Using a cybernetics and systems complexity approach, the study introduces a new systems‐analytics framework that provides a hybrid configuration of PCA (principal component analysis), multiple MCDM (multicriteria decision‐making) methods and machine learning‐based weighting. Analysis of the onshore wind, solar PV and hydropower investments in the 2023 G20 countries showed a cross‐national, stable and emergent convergence and systemic attractors, with hydropower demonstrating systemic resistance, which was explained by the economic, technology, policy and environment (ETPE) subsystems. The machine learning weight uncovered a nonlinear decision framework that was superior to traditional weighting methods, which were poorly aligned with the investment data. The study demonstrates the interaction of feedback systems, and the preferences toward technology, expanded behavioural systems, sociotechnical systems, and shows that the investment decisions of a nation are the outcome of a set of systems and interactions in a given context.