Abstract
Promoting a virtuous cycle among science, technology, finance, and industry is essential for advancing a modernized industrial system. Using provincial panel data from China spanning 2010–2023, this study applies a Double Machine Learning framework to investigate the causal impact of Sci-Tech finance efficiency (STFE) on the construction of a modernized industrial system(CMIS) and to uncover its internal mechanisms. The empirical results demonstrate that higher STFE significantly promotes industrial modernization by enhancing structural upgrading and innovation capacity. Mechanism analysis further reveals that STFE accelerates the transformation of scientific and technological achievements, strengthens the integration between digital technologies and the real economy, and optimizes the allocation of key production factors—including capital, talent, and technology. These mechanisms collectively foster the coordinated upgrading of industrial systems. Moreover, the heterogeneity analysis shows that the positive impact of STFE is more pronounced in regions with stronger economic foundations, higher degrees of marketization, and lower fiscal constraints, highlighting regional disparities in policy effectiveness. Overall, this study extends the theoretical understanding of the finance–technology–industry nexus under the DML framework and provides actionable insights for promoting regional coordination and differentiated policy design in the process of industrial modernization.
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Funding
This work was supported by the 2025 Special Project for Research in Philosophy and Social Science in Shaanxi Province (Grant No. 2025YB0295), the Xi’an Social Science Planning Fund Project (Grant No. 25JX147), and the Xi’an International Studies University Research Project (Grant No. 25XWC05).
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Conceptualization, R.H.; writing—original draft preparation and writing—review and editing, R.H., X.L.; writing—review and editing, X.L.; methodology, R.H., J.T.; data curation, S.W., C.L., Q.Z.; All authors have read and agreed to the published version of the manuscript.
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This study uses aggregated, publicly available province-level panel data (2010–2023) compiled from official statistical yearbooks and research databases. It does not involve human participants, human biological materials, or the collection, processing, or analysis of any personally identifiable information. Therefore, ethics approval and informed consent are not required. This determination is consistent with the Measures for the Ethical Review of Life Science and Medical Research Involving Humans (National Health Commission of the People’s Republic of China, 2023), which govern ethical review requirements for research involving human participants. In addition, the academic ethics review body of the School of Economics and Finance, Xi’an International Studies University, has confirmed that this study falls outside the scope of human-subject research and issued a formal waiver of ethics approval.
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Huang, R., Liu, X., Tian, J. et al. Sci-Tech finance efficiency promotes the construction of a modernized industrial system evidence from double machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35019-1
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DOI: https://doi.org/10.1038/s41598-026-35019-1
