Enhancing environmental management through big data: spatial analysis of urban ecological governance and big data development

Lei, Yunliang (2024) Enhancing environmental management through big data: spatial analysis of urban ecological governance and big data development. Frontiers in Environmental Science, 12. ISSN 2296-665X

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Abstract

Introduction: This research focuses on exploring the impact of Big Data Development (BDD) on Urban Ecological Governance Performance (EGP), with a particular emphasis on environmental dimensions within and among various regions. It aims to understand the complex interplay between technological advancements, urbanization, and environmental management in the context of urban ecological governance. Methods: Employing the Spatial Durbin Model (SDM), the study rigorously investigates the effects of BDD on EGP. It also examines the mediating role of Industrial Structure Level (ISL) and the moderating effects of both Level of Technological Investment (LTI) and Urbanization Level (URB), to provide a comprehensive analysis of the factors influencing urban ecological governance. Results: The findings reveal that big data significantly strengthens urban ecological governance, characterized by pronounced spatial spillover effects, indicating interregional interdependence in environmental management. Urbanization level notably amplifies the influence of BDD on EGP, whereas the magnitude of technological investments does not show a similar effect. Moreover, the industrial structure acts as a partial mediator in the relationship between BDD and EGP, with this mediating role demonstrating variability across different regions. Discussion: The research highlights the critical role of big data in enhancing urban ecological governance, particularly in terms of environmental aspects. It underscores the importance of technological advancements and urbanization in augmenting the effectiveness of ecological governance. The variability of the mediating role of industrial structure across regions suggests the need for tailored strategies in implementing big data initiatives for environmental management.

Item Type:	Article
Official URL:	https://www.frontiersin.org/journals/environmental...
Additional Information:	© 2024 The Author
Divisions:	LSE
Subjects:	T Technology > T Technology (General) H Social Sciences > HD Industries. Land use. Labor
Date Deposited:	09 Apr 2024 08:12
Last Modified:	09 Apr 2024 08:12
URI:	http://eprints.lse.ac.uk/id/eprint/122571

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