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Using machine learning to map the European cleantech sector

Author

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  • Ambrois, Matteo
  • Butticè, Vincenzo
  • Caviggioli, Federico
  • Cerulli, Giovanni
  • Croce, Annalisa
  • De Marco, Antonio
  • Giordano, Andrea
  • Resce, Giuliano
  • Toschi, Laura
  • Ughetto, Elisa
  • Zinilli, Antonio

Abstract

This working paper uses machine learning to identify Cleantech companies in the Orbis database, based on self-declared business descriptions. Identifying Cleantech companies is challenging, as there is no universally accepted definition of what constitutes Cleantech. This novel approach allows to scale-up the identification process by training an algorithm to mimic (human) expert assessment in order to identify Cleantech companies in a large dataset containing information on millions of European companies. The resulting dataset is used to construct a mapping of Cleantech companies in Europe and thereby provide a new perspective on the functioning of the EU cleantech sector. The paper serves as an introductory chapter to a series of analyses that will result from the CLEU project, a collaboration between the universities of Politecnico di Torino, Politecnico di Milano and Università degli Studi di Bologna. Notably, the project aims to deepen our understanding of the financing needs of the EU Cleantech sector. It was funded by the EIB's University Research Sponsorship (EIBURS) programme and supervised by the EIF's Research and Market Analysis Division.

Suggested Citation

  • Ambrois, Matteo & Butticè, Vincenzo & Caviggioli, Federico & Cerulli, Giovanni & Croce, Annalisa & De Marco, Antonio & Giordano, Andrea & Resce, Giuliano & Toschi, Laura & Ughetto, Elisa & Zinilli, An, 2023. "Using machine learning to map the European cleantech sector," EIF Working Paper Series 2023/91, European Investment Fund (EIF).
    • Handle: RePEc:zbw:eifwps:202391
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    References listed on IDEAS

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