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.
Belfanti et al. (2026) studied this question.