School of Statistics, Institute of Quantitative Economics, Huaqiao University, Xiamen 361021, China.
Int J Environ Res Public Health. 2023 Feb 24;20(5):4097. doi: 10.3390/ijerph20054097.
Against the backdrop of the pressing issue of global warming, the concept of green development, which emphasizes the rational utilization of resources and energy, has emerged as a viable model for future economic growth. Despite this, the interplay between big data technology and green development has yet to receive due consideration. This study aims to shed light on the role of big data in green development from the perspective of factor configuration distortion. To this end, a panel data analysis of 284 prefecture-level cities spanning from 2007 to 2020 was conducted, utilizing the Difference-in-Differences (DID) and Propensity Score Matching-Difference-in-Differences (PSM-DID) models to assess the impact of the establishment of the National Big Data Comprehensive Experimental Zone on green total factor productivity. The findings reveal that the establishment of the National Big Data Comprehensive Experimental Zone has a positive impact on green total factor productivity, primarily through optimizing the capital and labor allocation distortions, with the effect being more pronounced in areas with high levels of human capital, financial development, and economic activity. This research provides empirical evidence to evaluate the impact of the establishment of the National Big Data Comprehensive Experimental Zone and offers valuable policy implications for the pursuit of high-quality economic development.
在全球变暖这一紧迫问题的背景下,绿色发展理念强调资源和能源的合理利用,已成为未来经济增长的可行模式。然而,大数据技术与绿色发展之间的相互作用尚未得到应有的重视。本研究旨在从要素配置扭曲的角度探讨大数据在绿色发展中的作用。为此,利用 2007 年至 2020 年 284 个地级市的面板数据,采用双重差分(DID)和倾向得分匹配双重差分(PSM-DID)模型,评估国家大数据综合试验区的设立对绿色全要素生产率的影响。研究结果表明,国家大数据综合试验区的设立对绿色全要素生产率具有正向影响,主要通过优化资本和劳动力配置扭曲来实现,在人力资本、金融发展和经济活动水平较高的地区,效果更为显著。本研究为评估国家大数据综合试验区设立的影响提供了经验证据,并为追求高质量经济发展提供了有价值的政策启示。