Spatiotemporal impacts of scientific and technological innovation and industry chain resilience on ecological Efficiency: Evidence from 284 Chinese cities

• Published in: Journal of environmental management Vol. 384; p. 125532
• Main Authors: Wu, Meiling, Liu, Wei, Zhang, Rongguang, Fan, Zhiran, Zhong, Xueyan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2025
• Subjects: autocorrelation; China; Cities; clean energy; Conservation of Natural Resources; Ecological efficiency; environmental management; ESDA; governance; Industry; Industry chain resilience; Inventions; landscapes; MGWR; resource allocation; Scientific and technological innovation; spatial data; Sustainable Development; sustainable technology; Technology; China; MGWR; ESDA; Scientific and technological innovation; Industry chain resilience; Ecological efficiency
• ISSN: 0301-4797; 1095-8630; 1095-8630
• DOI: 10.1016/j.jenvman.2025.125532

In the face of escalating global ecological challenges, the rising trend of deglobalization, and profound shifts in the competitive global landscape, exploring the roles of technological innovation and industrial chain resilience in enhancing ecological efficiency is vital for achieving sustainable development goals. This study uses panel data from 284 Chinese cities spanning 2000 to 2020 and is the first to propose a dual-driver framework that integrates scientific and technological innovation (STI) with industrial chain resilience (ICR). Furthermore, it innovatively applies exploratory spatial data analysis (ESDA) and multiscale geographically weighted regression (MGWR) to the investigation of ecological efficiency (EE), systematically capturing the multiscale spatial effects and driving mechanisms of EE determinants, thereby improving research precision. The findings reveal that: (1) STI and ICR exhibited an upward trend, while EE displayed a ‘U-shaped’ temporal pattern; (2) STI and EE showed significant positive spatial autocorrelation, whereas ICR’s spatial variability intensified; (3) The MGWR model outperformed OLS and GWR in robustness; (4) The number of tertiary institutions had the most substantial impact on EE, followed by industrial diversity, green governance, and industrial structure. To improve EE, efforts must focus on cultivating green technology talent and overcoming challenges in clean energy and low-carbon technologies. Supportive policies should reduce costs and promote the adoption of green technologies, while regional innovation centers and optimised resource allocation will facilitate sustainable industrial transformation. This study deepens understanding of the mechanisms influencing EE and provides a scientific foundation and policy insights for improving global ecological governance. [Display omitted] •Explores the roles of STI and ICR in enhancing EE.•Proposes a dual-driver framework using panel data from 284 cities.•Uses MGWR to capture multiscale spatial effects on EE.•Precisely identifies the driving factors of STI and ICR influencing EE.•Emphasizes need to cultivate green tech talent and optimize resource allocation.