{"title":"人工智能应对气候变化:制造业专利分析","authors":"Matteo Podrecca;Giovanna Culot;Sam Tavassoli;Guido Orzes","doi":"10.1109/TEM.2024.3469370","DOIUrl":null,"url":null,"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 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.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703137","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence for Climate Change: A Patent Analysis in the Manufacturing Sector\",\"authors\":\"Matteo Podrecca;Giovanna Culot;Sam Tavassoli;Guido Orzes\",\"doi\":\"10.1109/TEM.2024.3469370\",\"DOIUrl\":null,\"url\":null,\"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 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.\",\"PeriodicalId\":55009,\"journal\":{\"name\":\"IEEE Transactions on Engineering Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703137\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Engineering Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10703137/\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10703137/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Artificial Intelligence for Climate Change: A Patent Analysis in the Manufacturing Sector
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.
期刊介绍:
Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.