Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-216
Spatial Econometric Research on Digital Finance-Driven Industrial Economic Performance Based on Block Algorithm Data
Professor, College of Accounting, Ningbo University of Finance and Economics, Ningbo, 315175, China, E-mail: zhanghang202504@163.com
Project Management
Received September 28, 2025; revised December 19, 2025; accepted December 22, 2025
Available online May 29, 2026
Abstract: Digital finance, as the core driving force of the digital economy era, has a noticeable effect on optimizing the industrial structure. The paper is based on panel data from 285 prefecture-level cities from 2011 to 2020, and it builds a theoretical framework for optimizing the digital finance industrial structure. This framework employs a block algorithm combined with a spatial econometric model to systematically examine the driving mechanism and spatial spillover effects of digital finance on industrial economic performance. The study has found that the development of digital finance has had a significant and positive impact on the optimization of industrial structure, with coefficients ranging from 0.045 to 0.049 (p < 0.01). This impact is transmitted through two pathways: residential consumption and technological innovation levels. The spatial correlation analysis showed that the global Moran’s I values for digital finance and industrial structure optimization were 0.309-0.412*** and 0.099-0.240*** (*** indicates p<0.01, the same below), exhibiting high highs and low lows clustering characteristics. The decomposition of block SDM effect showed that the direct impact of digital finance was 0.111***. Still, there was an adverse spillover effect of -0.048* (* indicates p<0.1), with a net total impact of 0.065**, confirming the coexistence of the siphon effect and the radiation effect. The regional heterogeneity test revealed that digital finance had the strongest impact on optimizing the industrial structure in the eastern area, followed by the central location, with no significant effect on the western region, and 0.022** (** indicates p<0.05) in the northeast area. Research has confirmed that block algorithms effectively enhance the efficiency of high-dimensional data processing, providing new methods for revealing the complex mechanisms of digital financial spatial spillover. Policy recommendations suggest building regional collaborative development mechanisms to optimize the allocation of digital financial resources.
Keywords: Block algorithm, digital finance, industrial economic performance, optimization of industrial structure, spatial econometrics.
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Citation: Zhang, H. (2026). Spatial Econometric Research on Digital Finance-Driven Industrial Economic Performance Based on Block Algorithm Data. Journal of Engineering, Project, and Production Management, 16(4), 2025-216.
