{"title":"中国长江经济带工业绿色技术创新效率的时空聚合效应及影响因素分析","authors":"Meng-Chao Yao, Ren-Jie Zhang, Hui-Zhong Dong","doi":"10.1007/s13132-024-02286-0","DOIUrl":null,"url":null,"abstract":"<p>This study aims to explore the spatiotemporal convergence effects of industrial green technological innovation efficiency and its influencing factors to facilitate the transformation of the Yangtze River Economic Belt from a traditional high-pollution, high-emission, and high-energy-consumption industrial model to a green, efficient, and sustainable economic development model. By applying the Super-SBM model, the absolute beta convergence model, the conditional beta convergence model, and the spatial dynamic Durbin model, this study reveals the dynamic changes in industrial green technological innovation efficiency and its influencing factors in the Yangtze River Economic Belt. The research findings are as follows: (1) Regions with lower industrial green technological innovation efficiency can rapidly improve by learning from more efficient regions, demonstrating a significant “catch-up” effect. The upstream and downstream areas exhibit specific spatial dependencies, while the midstream area does not pass the significance level test. (2) The conditional convergence rate is significantly higher than the absolute convergence rate, indicating the presence of spatial conditional convergence in industrial green technological innovation efficiency among different regions. (3) This study further analyzes the impact mechanisms of six factors—enterprise size, industry-university-research cooperation, enterprise R&D level, environmental regulation, energy consumption structure, and foreign direct investment—on industrial green technological innovation efficiency. The results show that these factors have significant differences in their effects. Finally, this study proposes strategies to optimize green technological innovation efficiency, aiming to provide a reference for the Yangtze River Economic Belt and other regions worldwide to achieve high-quality development with green and low-carbon growth.</p>","PeriodicalId":47435,"journal":{"name":"Journal of the Knowledge Economy","volume":"16 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the Spatiotemporal Convergence Effect and Influencing Factors of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt in China\",\"authors\":\"Meng-Chao Yao, Ren-Jie Zhang, Hui-Zhong Dong\",\"doi\":\"10.1007/s13132-024-02286-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study aims to explore the spatiotemporal convergence effects of industrial green technological innovation efficiency and its influencing factors to facilitate the transformation of the Yangtze River Economic Belt from a traditional high-pollution, high-emission, and high-energy-consumption industrial model to a green, efficient, and sustainable economic development model. By applying the Super-SBM model, the absolute beta convergence model, the conditional beta convergence model, and the spatial dynamic Durbin model, this study reveals the dynamic changes in industrial green technological innovation efficiency and its influencing factors in the Yangtze River Economic Belt. The research findings are as follows: (1) Regions with lower industrial green technological innovation efficiency can rapidly improve by learning from more efficient regions, demonstrating a significant “catch-up” effect. The upstream and downstream areas exhibit specific spatial dependencies, while the midstream area does not pass the significance level test. (2) The conditional convergence rate is significantly higher than the absolute convergence rate, indicating the presence of spatial conditional convergence in industrial green technological innovation efficiency among different regions. (3) This study further analyzes the impact mechanisms of six factors—enterprise size, industry-university-research cooperation, enterprise R&D level, environmental regulation, energy consumption structure, and foreign direct investment—on industrial green technological innovation efficiency. The results show that these factors have significant differences in their effects. Finally, this study proposes strategies to optimize green technological innovation efficiency, aiming to provide a reference for the Yangtze River Economic Belt and other regions worldwide to achieve high-quality development with green and low-carbon growth.</p>\",\"PeriodicalId\":47435,\"journal\":{\"name\":\"Journal of the Knowledge Economy\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Knowledge Economy\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1007/s13132-024-02286-0\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Knowledge Economy","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s13132-024-02286-0","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Analysis of the Spatiotemporal Convergence Effect and Influencing Factors of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt in China
This study aims to explore the spatiotemporal convergence effects of industrial green technological innovation efficiency and its influencing factors to facilitate the transformation of the Yangtze River Economic Belt from a traditional high-pollution, high-emission, and high-energy-consumption industrial model to a green, efficient, and sustainable economic development model. By applying the Super-SBM model, the absolute beta convergence model, the conditional beta convergence model, and the spatial dynamic Durbin model, this study reveals the dynamic changes in industrial green technological innovation efficiency and its influencing factors in the Yangtze River Economic Belt. The research findings are as follows: (1) Regions with lower industrial green technological innovation efficiency can rapidly improve by learning from more efficient regions, demonstrating a significant “catch-up” effect. The upstream and downstream areas exhibit specific spatial dependencies, while the midstream area does not pass the significance level test. (2) The conditional convergence rate is significantly higher than the absolute convergence rate, indicating the presence of spatial conditional convergence in industrial green technological innovation efficiency among different regions. (3) This study further analyzes the impact mechanisms of six factors—enterprise size, industry-university-research cooperation, enterprise R&D level, environmental regulation, energy consumption structure, and foreign direct investment—on industrial green technological innovation efficiency. The results show that these factors have significant differences in their effects. Finally, this study proposes strategies to optimize green technological innovation efficiency, aiming to provide a reference for the Yangtze River Economic Belt and other regions worldwide to achieve high-quality development with green and low-carbon growth.
期刊介绍：
In the context of rapid globalization and technological capacity, the world’s economies today are driven increasingly by knowledge—the expertise, skills, experience, education, understanding, awareness, perception, and other qualities required to communicate, interpret, and analyze information. New wealth is created by the application of knowledge to improve productivity—and to create new products, services, systems, and process (i.e., to innovate). The Journal of the Knowledge Economy focuses on the dynamics of the knowledge-based economy, with an emphasis on the role of knowledge creation, diffusion, and application across three economic levels: (1) the systemic ''meta'' or ''macro''-level, (2) the organizational ''meso''-level, and (3) the individual ''micro''-level. The journal incorporates insights from the fields of economics, management, law, sociology, anthropology, psychology, and political science to shed new light on the evolving role of knowledge, with a particular emphasis on how innovation can be leveraged to provide solutions to complex problems and issues, including global crises in environmental sustainability, education, and economic development. Articles emphasize empirical studies, underscoring a comparative approach, and, to a lesser extent, case studies and theoretical articles. The journal balances practice/application and theory/concepts.
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