Abstract
Purpose: This paper aims to develop a scalable, patent-based framework to map clusters' repositioning towards environmental technologies and to distinguish current green technological specialisation from transition readiness. Design/methodology/approach: The framework is applied to Italy by combining PATSTAT patent data with the Italian Cluster Mapping Project. Green technologies are identified through CPC-Y tagging. The authors compute cluster-level Revealed Technological Advantage (RTA) in environmental technologies (2000-2019) and estimate Potential RTA (pRTA) as a forward-looking indicator based on technology relatedness networks at CPC subclass level. The authors compare alternative predictive strategies (zero-inflated beta regression, Artificial Neural Networks, Random Forests) and retain the best-performing model to generate pRTA for 2020-2024. Finally, the authors classify clusters into a four-quadrant typology combining RTA and pRTA. Findings: Green inventive activity is geographically concentrated in a small set of regions, while it is more dispersed across cluster categories. Current green specialisation (RTA) varies substantially across region-cluster combinations and does not fully overlap with transition readiness (pRTA). The combined mapping reveals four profiles: green pioneers, emerging green clusters, mature green and green laggards, enabling a trajectory-oriented interpretation beyond static rankings. Originality/value: The study offers one of the first cluster-level, forward-looking measures of environmental-technology transition readiness in Italy, combining cluster mapping with relatedness-based prediction to support more differentiated research and place-based policy design.