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

Marco Cucculelli, Noemi Giampaoli and Matteo Renghini

No 188, Mo.Fi.R. Working Papers from Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences

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.

Keywords: PATSTAT; Orbis; Patents; Text mining; Innovation; STEP; NZIA; Unconventional data (search for similar items in EconPapers)
Pages: 32
Date: 2024-10
New Economics Papers: this item is included in nep-eec, nep-ino, nep-ipr and nep-tid
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