Artificial Intelligence for Climate Change : A Patent Analysis in the Manufacturing Sector
(2024) In IEEE Transactions on Engineering Management 71. p.15005-15024- Abstract
This study analyzes the current state of artificial intelligence (AI) technologies for addressing and mitigating climate change in the manufacturing sector and provides an outlook on future developments. The research is grounded in the concept of general-purpose technologies, motivated by a still limited understanding of innovation patterns for this application context. To this end, we focus on global patenting activity between 2011 and 2023 (5919 granted patents classified for 'mitigation or adaptation against climate change' in the 'production or processing of goods'). We examined time trends, applicant characteristics, and underlying technologies. A topic modeling analysis was performed to identify emerging themes from the... (More)
This study analyzes the current state of artificial intelligence (AI) technologies for addressing and mitigating climate change in the manufacturing sector and provides an outlook on future developments. The research is grounded in the concept of general-purpose technologies, motivated by a still limited understanding of innovation patterns for this application context. To this end, we focus on global patenting activity between 2011 and 2023 (5919 granted patents classified for 'mitigation or adaptation against climate change' in the 'production or processing of goods'). We examined time trends, applicant characteristics, and underlying technologies. A topic modeling analysis was performed to identify emerging themes from the unstructured textual data of the patent abstracts. This allowed the identification of six AI application domains. For each of them, we built a network analysis and ran growth trends and forecasting models. Our results show that patenting activities are mostly oriented toward improving the efficiency and reliability of manufacturing processes in five out of six identified domains ('predictive analytics,' 'material sorting,' 'defect detection,' 'advanced robotics,' and 'scheduling'). Instead, AI within the 'resource optimization' domain relates to energy management, showing an interplay with other climate-related technologies. Our results also highlight interdependent innovations peculiar to each domain around core AI technologies. Forecasts show that the more specific technologies are within domains, the longer it will take for them to mature. From a practical standpoint, the study sheds light on the role of AI within the broader cleantech innovation landscape and urges policymakers to consider synergies. Managers can find information to define technology portfolios and alliances considering technological coevolution.
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- author
- Podrecca, Matteo ; Culot, Giovanna ; Tavassoli, Sam LU and Orzes, Guido
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial intelligence (AI), climate change, patent analysis, sustainability, technology foresight
- in
- IEEE Transactions on Engineering Management
- volume
- 71
- pages
- 20 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85205698252
- ISSN
- 0018-9391
- DOI
- 10.1109/TEM.2024.3469370
- language
- English
- LU publication?
- yes
- id
- b2702059-401f-477a-afcc-ae5eb486866c
- date added to LUP
- 2024-12-20 09:54:49
- date last changed
- 2025-04-04 15:02:02
@article{b2702059-401f-477a-afcc-ae5eb486866c,
abstract = {{<p>This study analyzes the current state of artificial intelligence (AI) technologies for addressing and mitigating climate change in the manufacturing sector and provides an outlook on future developments. The research is grounded in the concept of general-purpose technologies, motivated by a still limited understanding of innovation patterns for this application context. To this end, we focus on global patenting activity between 2011 and 2023 (5919 granted patents classified for 'mitigation or adaptation against climate change' in the 'production or processing of goods'). We examined time trends, applicant characteristics, and underlying technologies. A topic modeling analysis was performed to identify emerging themes from the unstructured textual data of the patent abstracts. This allowed the identification of six AI application domains. For each of them, we built a network analysis and ran growth trends and forecasting models. Our results show that patenting activities are mostly oriented toward improving the efficiency and reliability of manufacturing processes in five out of six identified domains ('predictive analytics,' 'material sorting,' 'defect detection,' 'advanced robotics,' and 'scheduling'). Instead, AI within the 'resource optimization' domain relates to energy management, showing an interplay with other climate-related technologies. Our results also highlight interdependent innovations peculiar to each domain around core AI technologies. Forecasts show that the more specific technologies are within domains, the longer it will take for them to mature. From a practical standpoint, the study sheds light on the role of AI within the broader cleantech innovation landscape and urges policymakers to consider synergies. Managers can find information to define technology portfolios and alliances considering technological coevolution.</p>}},
author = {{Podrecca, Matteo and Culot, Giovanna and Tavassoli, Sam and Orzes, Guido}},
issn = {{0018-9391}},
keywords = {{Artificial intelligence (AI); climate change; patent analysis; sustainability; technology foresight}},
language = {{eng}},
pages = {{15005--15024}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Engineering Management}},
title = {{Artificial Intelligence for Climate Change : A Patent Analysis in the Manufacturing Sector}},
url = {{http://dx.doi.org/10.1109/TEM.2024.3469370}},
doi = {{10.1109/TEM.2024.3469370}},
volume = {{71}},
year = {{2024}},
}