Bibliographic Details
| Title: |
Can Open Government Data Really Empower Urban Ecological Resilience: A Double Machine Learning Perspective. |
| Authors: |
Wang, Lulu1,2 (AUTHOR) luckyw@hnu.edu.cn, Chen, Wen2 (AUTHOR) |
| Source: |
Emerging Markets Finance & Trade. Aug2025, p1-18. 18p. 1 Illustration. |
| Subject Terms: |
*INFORMATION economy, *INFORMATION asymmetry, *RESOURCE dependence theory, ECOLOGICAL resilience, DIGITAL technology, DEEP learning, TRANSPARENCY in government, HETEROGENEITY |
| Abstract: |
Open government data (OGD) is a crucial driver of digital economy strategies and digital government development. The double machine learning (DML) model enables accurate estimation in high-dimensional contexts and accommodates nonlinearities. This study applies a double machine learning (DML) model to examine OGD’s impact on urban ecological resilience (UER). Results show a positive link between OGD and UER, supported by robustness tests. Drawing on resource dependence, information asymmetry, and Jacob’s externality theories, the study finds that OGD promotes UER through digital inclusive finance, digital technology innovation, government and public environmental concerns, and industrial collaborative agglomeration. Heterogeneity analysis reveals that OGD’s effects are stronger in large cities, those with higher administrative status, and non-resource-based economies. [ABSTRACT FROM AUTHOR] |
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| Database: |
Business Source Index |