DATA-DRIVEN EVALUATION OF REGIONAL SCI-TECH FINANCE EFFICIENCY
Volume 6, Issue 7, Pp 10-20, 2024
DOI: https://doi.org/10.61784/ejst3051
Author(s)
Yuan Wang1, YaLiu Yang1*, Cui Wang1, XiaoWei Zheng1, XiaoXiao Si2
Affiliation(s)
1Business School, Suzhou University, Suzhou 234000, Jiangsu, China.
2Economics & Management School, Huaibei Institute of Technology, Huaibei 235000, Anhui, China.
Corresponding Author
YaLiu Yang
ABSTRACT
Sci-tech finance is the catalyst for the transformation of technological progress into real productivity. Hence, to improve sci-tech finance efficiency and promote high-quality regional economy development, a data-driven evaluation model of sci-tech finance efficiency is constructed. The collected data are processed using the DEA-Malmquist index. The efficiency level of sci-tech finance is determined according to the Total Factor Productivity index, and the main influencing factors of sci-tech finance efficiency are determined through the decomposition analysis of this index. This study calculates the efficiency level and dynamic change of sci-tech finance in the Yangtze River Delta. The results show that technological progress is an important factor affecting sci-tech finance efficiency. Surprisingly, the growth of R&D personnel has a negligible effect on sci-tech financial efficiency instead, and Shanghai ranked third in terms of sci-tech financial efficiency, which is no match for Zhejiang and Jiangsu provinces. Hence, we propose targeted suggestions to improve sci-tech finance efficiency in the Yangtze River Delta. This study not only provides theoretical and methodological support for the evaluation of regional sci-tech financial efficiency but also provides a reference for sci-tech finance policymakers and researchers.
KEYWORDS
DEA-Malmquist index; Sci-tech finance; High-quality development
CITE THIS PAPER
Yuan Wang, YaLiu Yang, Cui Wang, XiaoWei Zheng, XiaoXiao Si. Data-driven evaluation of regional sci-tech finance efficiency. Eurasia Journal of Science and Technology. 2024, 6(7): 10-20. DOI: https://doi.org/10.61784/ejst3051.
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