Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-261
Analyzing the Impact of Digital Economy Development on Carbon Finance Using Differences-in-Differences and Spatial Error Model
1 Lecturer,
School of Finance and Economics, Wuxi University of Technology, Wuxi,
214121, China; Ph.D., School of Finance and Economics, Jiangsu
University, Zhenjiang, 212013, China, E-mail: xiebei1996@126.com
(corresponding author).
2 Lecturer, General Education
Department, Changzhou Vocational Institute of Engineering, Changzhou,
213164, China
3 Lecturer, School of Finance and
Economics, Wuxi University of Technology, Wuxi, 214121, China
4 Associate Professor, School of
Finance and Economics, Wuxi University of Technology, Wuxi, 214121,
China
Project Management
Received February 16, 2026; revised May 18, 2026; June 17, 2026; accepted June 17, 2026
Available online June 30, 2026
Abstract: Traditional measurement methods often produce biased estimates when assessing the impact of digital economy development on carbon finance, as they neglect spatial dependencies between regions and the endogeneity of policy shocks. This failure to account for these factors hinders a comprehensive reflection of the multidimensional mechanisms at play. To enhance the accuracy and scientific rigor of such assessments, this study proposes an integrated analytical framework combining the Differences-In-Differences (DID) approach with a Spatial Error Model (SEM). This framework aims to identify the causal effects of digital economy development on carbon finance and its spatial transmission pathways. Experimental results indicated that the proposed model consistently produced the smallest estimation bias for the average treatment effect on the treated and maintained the highest estimation precision across all sample sizes. When the number of observed units was 30, the estimation bias was only 0.05%, significantly lower than the 0.65% bias observed in the traditional differences-in-differences model. As the number of samples increases, the precision of spatial parameter estimates improves across all models. However, the analytical framework integrating differences-in-differences and spatial error models consistently demonstrates the highest estimation stability. At 1,000 samples, the standard error of the spatial error coefficient dropped to 0.018. The proposed method effectively addresses the limitations of traditional models in adapting to complex spatial data structures. This provides methodological support and practical guidance for optimizing carbon finance policy frameworks and advancing regional green collaborative development.
Keywords: Digital economy development; spatial error model; differences-in-differences (DID); carbon finance; principal component analysis.
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Citation: Xie, B., Yang, J., Wang, J., and Jin, Q. (2026). Analyzing the Impact of Digital Economy Development on Carbon Finance Using Differences-in-Differences and Spatial Error Model. Journal of Engineering, Project, and Production Management, 16(6), 2026-261.
