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Climate change and labour-saving technologies: the twin transition via patent texts

Author

Listed:
  • Tommaso Rughi
  • Jacopo Staccioli
  • Maria Enrica Virgillito
Abstract
This paper provides a direct understanding of the twin transition from the innovative activity domain. It starts with a technological mapping of the technological innovations characterised by both climate change mitigation/adaptation (green) and labour-saving attributes. To accomplish the task, we draw on the universe of patent grants in the USPTO since 1976 to 2021 reporting the Y02-Y04S tagging scheme and we identify those patents embedding an explicit labour-saving heuristic via a dependency parsing algorithm. We characterise their technological, sectoral and time evolution. Finally, after constructing an index of sectoral penetration of LS and non-LS green patents, we explore its impact on employment share growth at state level in the US. Our evidence shows that employment shares in sectors characterised by a higher exposure to LS (non-LS) technologies present an overall negative (positive) growth dynamics.

Suggested Citation

  • Tommaso Rughi & Jacopo Staccioli & Maria Enrica Virgillito, 2023. "Climate change and labour-saving technologies: the twin transition via patent texts," LEM Papers Series 2023/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    • Handle: RePEc:ssa:lemwps:2023/11
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    References listed on IDEAS

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    Cited by:

    1. Nicoletta Corrocher & Daniele Moschella & Jacopo Staccioli & Marco Vivarelli, 2024. "Innovation and the labor market: theory, evidence, and challenges," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 33(3), pages 519-540.
    2. Frank Neffke & Angelica Sbardella & Ulrich Schetter & Andrea Tacchella, 2024. "Economic Complexity Analysis," Papers in Evolutionary Economic Geography (PEEG) 2430, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2024.

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    More about this item

    Keywords

    Climate change mitigation technologies; Labour-saving technologies; Search heuristics; Natural Language Processing; Labour markets.;
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