Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/297119 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
LEM Working Paper Series No. 2023/11
Verlag: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Zusammenfassung: 
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.
Schlagwörter: 
Climate change mitigation technologies
Labour-saving technologies
Search heuristics
Natural Language Processing
Labour markets
JEL: 
C38
J24
O33
Q55
Dokumentart: 
Working Paper

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