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The determining mechanism of technology catch-up in China's photovoltaic (PV) industry: Machine learning approaches.
- Source :
-
Journal of Cleaner Production . Apr2024, Vol. 450, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- The unexpected success of China's PV industry in technology catch-up has been noted. However, existing research has overlooked the multidimensional nonlinear complexities in its underlying mechanisms. This study introduces the "Seven-Element Diamond Model" theoretical framework and employs machine learning (ML) methods to address this. Using observed data from 84 listed China's PV companies from 2000 to 2021, we systematically examine the intricate mechanisms of technology catch-up in China's PV industry. Key findings include: 1) The self-enhancing mechanism stemming from R&D effort, government subsidies, and domestic demand is pivotal for the successful technology catch-up of China's PV industry. 2) Key regulatory directions are identified from R&D effort to market competition, technology spillover, productivity, and government subsidies. 3) R&D effort forms the underlying logic of China's PV industry technology catch-up, and domestic market demand is an extremely important driver for China's PV technology catch-up. Government subsidies exhibit a "blowout" incentive effect, and the synergistic coupling between key variables is also significant in driving China's PV technology catch-up. This study offers valuable insights into the catch-up of clean energy or sustainable technologies in China and other emerging economies. [Display omitted] • Constructed the theoretical framework of the Seven-Element Diamond Model. • The self-reinforcing mechanism among R&D efforts, government subsidies, and domestic demand are investigated. • Key regulatory directions from R&D efforts to market competitiveness, technology spillovers, productivity, and government subsidies are identified. • R&D efforts are the underlying logic of China's photovoltaic industry technology catch-up. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*MACHINERY industry
*CLEAN energy
*SUBSIDIES
*EMERGING markets
Subjects
Details
- Language :
- English
- ISSN :
- 09596526
- Volume :
- 450
- Database :
- Academic Search Index
- Journal :
- Journal of Cleaner Production
- Publication Type :
- Academic Journal
- Accession number :
- 176500152
- Full Text :
- https://doi.org/10.1016/j.jclepro.2024.142028