Cleantech and policy framework in Europe: A machine learning approach

The pursuit of a sustainable and clean energy future has emerged as a paramount global imperative of the 21st century. Achieving this transition is a multifaceted and complex endeavour that requires a harmonious interplay of factors: effective policy frameworks, cleantech firms, and the transformative power of data science. By focusing on the European context, this paper advances the field in several directions. First, it explores the use of machine learning (ML) techniques to identify cleantech firms by analysing their mission statements and addressing the weaknesses of the existing methods. Second, it collects a unique and comprehensive dataset of national-level policies addressing the different topics covered by the European Green Deal. Third, in a regression analysis at country level, it examines the interplay between the national regulatory framework and the birth and growth of the cleantech landscape, by distinguishing between innovators (firms which develop the cleantech) and ecosystem firms (which adopt the cleantech). Our results indicate that the introduction of policies favours by itself the birth of cleantech innovator firms and their growth in the country. An increasing number of policies has a regulatory effect in the cleantech ecosystem limiting the number of newborn firms while favouring the growth of existing ones