Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model
Abstract The need for green innovation in the high-tech industry has become a critical path to sustainable economic development. However, evaluating green innovation efficiency (GIE) and its spatial characteristics within China’s high-tech industry remains underexplored. This study uses the three-st...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16189-w |
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| author | Bo-Wen An Pei-Yuan Xu Long-Zhan Liu Chun-Bo Li Qiu-Ping Guo |
| author_facet | Bo-Wen An Pei-Yuan Xu Long-Zhan Liu Chun-Bo Li Qiu-Ping Guo |
| author_sort | Bo-Wen An |
| collection | DOAJ |
| description | Abstract The need for green innovation in the high-tech industry has become a critical path to sustainable economic development. However, evaluating green innovation efficiency (GIE) and its spatial characteristics within China’s high-tech industry remains underexplored. This study uses the three-stage undesirable SBM model to assess GIE in China’s high-tech industry from 2006 to 2022. Various spatial analysis methods, including the Theil index, Moran index, Standard Deviation Ellipse, Spatial Markov Chain, and β-convergence model, are applied to examine spatial differences, clustering patterns, and convergence trends of GIE across eight economic regions in China. The model adjusts input indicators to incorporate technological and environmental factors, providing a deeper understanding of the relationship between GIE and regional dynamics. The quantitative results show an increase in GIE from 0.350 to 0.566, with technological and environmental factors playing a significant role. The study highlights increasing spatial disparities in GIE, with the Northern Coastal Region achieving the highest levels. Spatial clustering analysis reveals distinct patterns: the Southern Coastal Region shows High-High clustering, while the Northeast Region exhibits Low-Low clustering. GIE demonstrates club convergence, β-convergence, and spatial spillover effects. These findings underscore the effectiveness of green innovation practices and offer insights into spatial dynamics, providing guidance for targeted interventions and promoting inclusive growth across regions. |
| format | Article |
| id | doaj-art-b85c7a7556ef4238a5f7522e4fa584ec |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b85c7a7556ef4238a5f7522e4fa584ec2025-08-20T04:03:07ZengNature PortfolioScientific Reports2045-23222025-08-0115111810.1038/s41598-025-16189-wAssessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM modelBo-Wen An0Pei-Yuan Xu1Long-Zhan Liu2Chun-Bo Li3Qiu-Ping Guo4College of Economics and Finance, Huaqiao UniversityCollege of Economics and Finance, Huaqiao UniversityDepartment of Basic Teaching and Research, Xinjiang College of Science & TechnologyCollege of Artificial Intelligence, Hebei Oriental UniversityPan Asia Business School, Yunnan Normal UniversityAbstract The need for green innovation in the high-tech industry has become a critical path to sustainable economic development. However, evaluating green innovation efficiency (GIE) and its spatial characteristics within China’s high-tech industry remains underexplored. This study uses the three-stage undesirable SBM model to assess GIE in China’s high-tech industry from 2006 to 2022. Various spatial analysis methods, including the Theil index, Moran index, Standard Deviation Ellipse, Spatial Markov Chain, and β-convergence model, are applied to examine spatial differences, clustering patterns, and convergence trends of GIE across eight economic regions in China. The model adjusts input indicators to incorporate technological and environmental factors, providing a deeper understanding of the relationship between GIE and regional dynamics. The quantitative results show an increase in GIE from 0.350 to 0.566, with technological and environmental factors playing a significant role. The study highlights increasing spatial disparities in GIE, with the Northern Coastal Region achieving the highest levels. Spatial clustering analysis reveals distinct patterns: the Southern Coastal Region shows High-High clustering, while the Northeast Region exhibits Low-Low clustering. GIE demonstrates club convergence, β-convergence, and spatial spillover effects. These findings underscore the effectiveness of green innovation practices and offer insights into spatial dynamics, providing guidance for targeted interventions and promoting inclusive growth across regions.https://doi.org/10.1038/s41598-025-16189-wGIE of high tech industrySpatial differentiation characteristicsSpatial agglomeration characteristicsSpatial convergence characteristicsThree stage undesirable SBM |
| spellingShingle | Bo-Wen An Pei-Yuan Xu Long-Zhan Liu Chun-Bo Li Qiu-Ping Guo Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model Scientific Reports GIE of high tech industry Spatial differentiation characteristics Spatial agglomeration characteristics Spatial convergence characteristics Three stage undesirable SBM |
| title | Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model |
| title_full | Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model |
| title_fullStr | Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model |
| title_full_unstemmed | Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model |
| title_short | Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model |
| title_sort | assessing green innovation efficiency and spatial characteristics of china s high tech industry based on the three stage undesirable sbm model |
| topic | GIE of high tech industry Spatial differentiation characteristics Spatial agglomeration characteristics Spatial convergence characteristics Three stage undesirable SBM |
| url | https://doi.org/10.1038/s41598-025-16189-w |
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