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Identification of STEP and NZIA technologies through text mining: An empirical analysis of patent data

Author

Listed:
  • Marco Cucculelli

    (Universita' Politecnica delle Marche, Dipartimento di Scienze economiche e sociali)

  • Noemi Giampaoli

    (Polytechnic University of Marche, Department of Economics and Social Sciences,)

  • Matteo Renghini

    (LUISS "Guido Carli" University, Department of Economics and Finance)

Abstract
Assessing the presence and distribution of strategic and net-zero technologies in companies is crucial for European competitiveness. However, due to the complexity and evolving nature of these technology areas, this is a challenging task. This paper presents a process for identifying and mapping strategic and net-zero technologies (as described in the Strategic Technologies for Europe Platform (STEP) and the Net-Zero Industry Act (NZIA)) in European companies. STEP and NZIA technologies are identified using text mining techniques based on the titles and abstracts of patents filed with the EPO and retrieved in PATSTAT for the years 2002 to 2022. The paper describes the classification process of STEP and NZIA technologies based on IPC codes of file patents. The IPC codes were then matched with the patent portfolio of almost 100,000 European companies to determine the company's technological profile and the distribution of these technologies by sector, geographic area, and company characteristics in the European panorama.

Suggested Citation

  • Marco Cucculelli & Noemi Giampaoli & Matteo Renghini, 2024. "Identification of STEP and NZIA technologies through text mining: An empirical analysis of patent data," Mo.Fi.R. Working Papers 188, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    • Handle: RePEc:anc:wmofir:188
    as

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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    PATSTAT; Orbis; Patents; Text mining; Innovation; STEP; NZIA; Unconventional data;
    All these keywords.

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