The Frontiers of Society, Science and Technology, 2024, 6(9); doi: 10.25236/FSST.2024.060912.
Jingqiao Guo, Zhiyuan Wang, Zhanjie Wen
Department of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, China
This research applies Double Machine Learning (DML) to investigate the impact of infrastructure investment on urban economic resilience in four major Chinese cities from 2018 to 2024. Utilizing indicators such as Internet and mobile phone penetration rates, per capita GDP, educational attainment, and unemployment rates, a comprehensive evaluation model was developed. The DML approach effectively addresses endogeneity issues and biases in traditional econometric methods, providing precise estimations. The study finds a significant positive correlation between infrastructure improvements and urban economic resilience. Infrastructure investment not only enhances economic resilience but also improves a city's adaptability to recover swiftly from economic shocks. The causal effects of infrastructure on economic resilience vary among cities, with Shanghai and Shenzhen showing particularly strong positive impacts, likely due to their economic structures and population densities. This study offers valuable insights into the role of infrastructure in fostering urban economic resilience and provides a scientific basis for urban development planning and resource allocation. It also highlights the need for policymakers to consider regional characteristics when formulating infrastructure investment strategies.
infrastructure, Urban economic resilience, Dual machine learning, Empirical analysis
Jingqiao Guo, Zhiyuan Wang, Zhanjie Wen. Economic Resilience Enhancement of Cities through Digital Infrastructure: A Dual Machine Learning Approach. The Frontiers of Society, Science and Technology (2024), Vol. 6, Issue 9: 76-80. https://doi.org/10.25236/FSST.2024.060912.
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