The impact of population structure on carbon emission has always been a key area of research in modern society. In this paper, we propose a new expanded STIRPAT model and panel co-integration method to analyze the relationship between population aging and carbon emission, based on the provincial panel data in China from 1999 to 2014. Empirical results show that there exists a significant inverted U-shaped curve between the population aging and carbon emission. There also exist regional discrepancies, where the impact of the population aging on carbon emission in the eastern region is significantly positive. By contrast, a negative relationship arises in the central and western regions. Finally, several suggestions for low carbon development are provided.
The digital economy has aroused widespread concern. This paper studies the impact of the digital economy on innovation using a panel threshold model. Taking 30 provinces, municipalities, and autonomous regions in China as the research object, the time span is from 2013 to 2019. The data are from the National Bureau of Statistics of China (NBSC), China National Intellectual Property Administration (CAIPA), the China Stock Market and Accounting Research (CSMAR), and the Ministry of Industry and Information Technology (MIIT)of China. Data analysis is performed with ArcGIS 10.2 and STATA 16 software. The influence mechanism of digital economy on innovation is innovatively analyzed from the aspects of innovation elements, innovation tools, innovation subjects, and innovation environment. A digital economy development level index system is constructed using the entropy method, and the development level of China’s digital economy in time and space is analyzed. On this basis, the nonlinear impact of digital economy on innovation, i.e., the threshold effect, is innovatively studied using the panel threshold model. It is found that China’s digital economy develops rapidly, but there is a serious spatial imbalance, and there are great differences in the different dimensions of the digital economy. At the same time, the impact of digital economy on innovation has a double threshold effect with industrial structure as the threshold variable and a single threshold effect with urbanization level as the threshold variable. Specifically, the promoting effect of digital economy on innovation increases with the optimization of industrial structure or the improvement of urbanization level. This study enriches the theoretical research on the impact of digital economy on innovation, and it has important support and reference value for China’s development of digital economy and improvement of innovation capacity.
The construction of China’s high-speed rail has been arousing controversy for the possibility of exacerbating regional imbalance. This paper provides an empirical analysis based on the panel data of 276 prefecture-level cities during 2007–2018 to explore the authenticity of this inference. The panel threshold model is adopted to investigate whether the economic growth becomes stronger and more equal among China’s cities under the impact of the rapidly expanding high-speed rail network by taking per capita gross domestic product (pGDP) as the threshold variable. To fully explore the dynamic function, we incorporate three progressive indices to measure the role of cities in China’s high-speed rail network: the existence of high-speed rail, the number of lines, and the betweenness centrality of the city in the entire network. The result shows that high-speed rail can promote economic growth and that there is a threshold effect in this process. Specifically, cities with higher pGDP can benefit more from high-speed rail. Another significant conclusion can be drawn that high-speed rail can intensify regional disparities, yet the marginal economic gap tends to decline as the high-speed rail network gets more optimized. Meanwhile, this study recognized nine circle-like high-speed rail urban agglomerations based on empirical results, reflecting the polycentric developing pattern of China.
This paper explores the relationship between high-speed rail (HSR) and industrial agglomeration within urban agglomerations. The paper selects the data of the Beijing–Tianjin–Hebei Urban Agglomeration (BJHUA) and Central Plains Urban Agglomeration (CPUA) from 2002 to 2016 as the research object. The time-varying difference-in-difference (TVDID) model is innovatively applied to analyze the impact of HSR on the agglomeration of secondary and tertiary industries in urban agglomerations, and the industrial agglomeration effects of the two urban agglomerations are compared. The results show that the influence of high-speed railways on the industrial agglomeration of urban agglomerations is heterogeneous. In the BJHUA, the impact of HSR on the agglomeration of secondary and tertiary industries is not particularly significant. On the other hand, in the CPUA, HSR does not have a significant impact on the agglomeration of secondary industry. However, it does have a significant negative effect on the agglomeration of tertiary industry. In addition, further analysis reveals significant variations in the impact of HSR on the agglomeration of industries within urban agglomerations after excluding the central cities. It is important to note that the impact of HSR on regional industries can be complex and multifaceted. The findings enrich the theoretical understanding of the relationship between HSR and industrial agglomeration.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.