Tesi etd-05152025-173525
Link copiato negli appunti
Tipo di tesi
Corso Ordinario Secondo Livello
Autore
EMER, LORENZO
URN
etd-05152025-173525
Titolo
Mapping Green AI Technologies: A Patent Analysis Using BERTopic
Struttura
Classe Scienze Sociali
Corso di studi
SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI
Commissione
Tutor Prof. BELLE', NICOLA
Relatore Prof. MINA, ANDREA
Relatore Prof. VANDIN, ANDREA
Presidente Prof. MONETA, ALESSIO
Membro Dott.ssa FURLAN, MANUELA
Membro Prof. FAGIOLO, GIORGIO
Membro Prof. NUVOLARI, ALESSANDRO
Relatore Prof. MINA, ANDREA
Relatore Prof. VANDIN, ANDREA
Presidente Prof. MONETA, ALESSIO
Membro Dott.ssa FURLAN, MANUELA
Membro Prof. FAGIOLO, GIORGIO
Membro Prof. NUVOLARI, ALESSANDRO
Parole chiave
- Green AI
- patent analysis
- technological impact analysis
- topic modeling
Data inizio appello
13/06/2025;
Disponibilità
parziale
Riassunto analitico
Artificial intelligence (AI) is a key enabler of innovation against climate change.
In this study, we investigate the intersection of AI and climate adaptation and mitigation technologies through patent analyses of a novel dataset of approximately 63 000 “Green AI” patents. We analyze patenting trends, corporate ownership of the technology, the geographical distributions of patents, their impact on follow-on inventions and their market value. We use topic modeling (BERTopic) to identify sixteen major technological domains, track their evolution over time, and identify their relative impact. We uncover a clear shift from legacy domains such as combustion engines technology to emerging areas like data processing, microgrids, and agricultural water management. We find evidence of growing concentration in corporate patenting against a rapidly increasing number of patenting firms. Looking at the technological and economic impact of patents, while some Green AI domains combine technological impact and market value, others reflect weaker private incentives for innovation, despite their relevance for climate adaptation and mitigation strategies. This is where policy intervention might be required to foster the generation and use of new Green AI applications.
In this study, we investigate the intersection of AI and climate adaptation and mitigation technologies through patent analyses of a novel dataset of approximately 63 000 “Green AI” patents. We analyze patenting trends, corporate ownership of the technology, the geographical distributions of patents, their impact on follow-on inventions and their market value. We use topic modeling (BERTopic) to identify sixteen major technological domains, track their evolution over time, and identify their relative impact. We uncover a clear shift from legacy domains such as combustion engines technology to emerging areas like data processing, microgrids, and agricultural water management. We find evidence of growing concentration in corporate patenting against a rapidly increasing number of patenting firms. Looking at the technological and economic impact of patents, while some Green AI domains combine technological impact and market value, others reflect weaker private incentives for innovation, despite their relevance for climate adaptation and mitigation strategies. This is where policy intervention might be required to foster the generation and use of new Green AI applications.
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