Spatiotemporal Differentiation and Evolution Pattern of Digital Twin Urban Construction Potential from the Perspective of New Infrastructure

Advancing digital-twin city development through new infrastructure and promoting the upgrading and transformation of smart cities is critical for building new competitive advantages for cities and is an inevitable requirement for modernizing urban governance systems and capabilities. Since new infrastructure and digital-twin cities share similar construction goals, this study examines 149 smart city pilot projects in China from 2011 to 2022, treating new infrastructure as a critical path to the construction of digital twin cities. First, a three-stage data envelopment analysis was used to assess the potential of digital twin city construction from the perspective of new infrastructure. Subsequently, a multi-stage traditional Markov model and a multi-stage spatial Markov model were used to study the potential of digital twin city construction from the perspectives of temporal evolution and spatial spillover, respectively. The results show that: 1) In the first stage, the nationwide overall technical efficiency generally maintained an upward trend, with the efficiency values in the Northeast, West, and Central regions showing a fluctuating upward trend, whereas the overall technical efficiency value in the eastern region remained at a low level. 2) In the third stage, after removing environmental factors and random disturbances, high-level areas were concentrated mainly in the Beijing-Tianjin-Hebei urban agglomeration, eastern Shandong, and some provincial capitals. Further comparison of the comprehensive technical efficiency values between the first and third stages revealed that the eastern region had a higher efficiency than the central region, indicating greater potential for digital twin urban construction. In contrast, the efficiency values of the northeastern, western, and central regions declined. 3) Traditional Markov models show that digital twin urban construction potential has a significant path-dependent effect, and the upgrading trend of digital twin urban construction potential gradually strengthens with an increase in the number of phases. Further introduction of spatial Markov models, accounting for the spatial correlation between neighboring regions, revealed that the evolution of digital twin urban construction potential was substantially affected by the development levels from neighboring areas, with spatial spillover and siphon effects coexisting. Simultaneously, the impact of neighboring cities at different levels of development on the potential evolution of their respective regions also showed significant differences. The contributions of this study are as follows: 1) From a research perspective, it regards the construction of new infrastructure as an important supporting path for the construction of digital twin cities, constructs an analytical framework for new infrastructure to empower the construction of digital twin cities, and measures the potential of digital twin city construction from the perspective of new infrastructure, thus expanding the empirical dimension of cross-disciplinary research on new infrastructure and digital twin cities. 2) In terms of indicator construction, it systematically constructs an indicator system covering the industrial space, social space, governance space, and information space of digital twins, more comprehensively depicting the intrinsic characteristics and development potential of digital twin cities. 3) In terms of research methodology, this study adopts a three-stage Data Envelopment Analysis model to effectively eliminate the influence of environmental factors and random disturbances on efficiency measurement, combining multi-period traditional Markov models and spatial Markov models to reveal the path dependence, gradient evolution, and evolutionary mechanism of spatial spillover and siphon coexistence of the potential of digital twin city construction from the dual dimensions of temporal evolution and spatial correlation.