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https://elib.bsu.by/handle/123456789/339957Полная запись метаданных
| Поле DC | Значение | Язык |
|---|---|---|
| dc.contributor.author | Jiacheng, G. | |
| dc.contributor.author | Zhalezka, B. A. | |
| dc.date.accessioned | 2026-01-13T10:14:42Z | - |
| dc.date.available | 2026-01-13T10:14:42Z | - |
| dc.date.issued | 2025 | |
| dc.identifier.citation | Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the XIV Intern. Conf., Minsk, Sept. 24–27, 2025 / Belarusian State Univ. ; eds.: Yu. Kharin (ed.-in-chief) [et al.]. – Minsk : BSU, 2025. – Pp. 95-98. | |
| dc.identifier.isbn | 978-985-881-830-2 | |
| dc.identifier.uri | https://elib.bsu.by/handle/123456789/339957 | - |
| dc.description.abstract | Based on China’s provincial panel data, this study uses entropy weight model and vector autoregressive model to measure the economic growth quality of high-tech industries in China and test the interaction effect among regions. It is found that the quality of economic growth of high-tech industries in China has shown a steady growth trend during the study period, but there are obvious differences in the growth level among regions. The economic growth quality of high-tech industries in the eastern region is obviously stronger than that in the central region and the western region. In addition, the economic growth quality of high-tech industries in the eastern region is likely to have a siphon effect on the central region and the western region, while the central region is likely to have a radiation effect on the western region | |
| dc.language.iso | en | |
| dc.publisher | Minsk : BSU | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.subject | ЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Экономика и экономические науки | |
| dc.subject | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика | |
| dc.title | A method to evaluate the economic growth quality of high-tech industry in China and its regional linkage | |
| dc.type | conference paper | |
| Располагается в коллекциях: | 2025. Computer Data Analysis and Modeling: Stochastics and Data Science | |
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