Coupling pathways and empirical measurement in mechanical design
Wendong Cai
Hainan Vocational University of Science and Technology
DOI: https://doi.org/10.59429/esta.v12i3.11347
Keywords: regional development disparities (UUDI); digital finance (DFI); TOPSIS-entropy weight method; PCA; mechanical design
Abstract
This study constructs the "Regional Development Gap Index (UUDI)"based on prefecture-level city data from Guangdong Province spanning 2013–2023. Employing the TOPSIS-entropy weighting method and validating robustness through PCA, it examines the impact of digital financial development (DFI) on regional disparities. Construction and mechanical design, manufacturing, and automation (MD&A) are embedded as pivotal sectors for in-depth analysis. The indicator system incorporates green and digital elements from construction (prefabrication share, green building area, energy/carbon intensity per unit floor area) and MD&A linkage metrics (prefabricated component standardization rate, BIM-MES-SCADA integration, CNC/robotic equipment density, on-site automated sensor coverage) to reflect the transmission chain: "capital accessibility—Manufacturing capacity—Engineering supply—Urban performance."Descriptive statistics reveal: UUDI mean 0.52, standard deviation 0.18, with significant regional variation; DFI mean 0.68, standard deviation 0.23, showing high concentration in the Pearl River Delta; 67% of cities have high-speed rail access, with financing convenience significantly better in the Pearl River Delta. Mechanism analysis reveals that digital finance alleviates financing constraints for equipment renewal, promotes coordination within the prefabricated construction supply chain, and stimulates automation investment. This enhances engineering supply efficiency and quality, ultimately narrowing regional disparities. This study contributes by: proposing a unified measurement framework linking "digital finance—MD&A—Construction industry—Urban performance”; developing reusable UUDI metrics and embedded MD&A indicators; and establishing a data and methodological foundation for future causal identification and policy simulation.
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