Based on panel data from 2010 to 2019, this study examines the relationship between defense science and technology innovation and high‐quality economic development at the provincial level in China. Using the gray correlation analysis method of panel data and an evaluation index system of high‐quality economic development, we calculate the gray correlation coefficient and decompose its time‐series gray correlation degree and cross‐sectional. Our findings indicate that the correlation between defense science and technology innovation and high‐quality economic development in China is generally stable during 2010–2019. However, Beijing stands out as an exception with a correlation coefficient of less than 0.5. Furthermore, there is regional gray correlation heterogeneity with the smallest correlation degree found in the eastern region and the largest in the northeast region. Based on our results, we propose three countermeasure suggestions: first, to balance national defense construction and economic construction; second, to optimize national defense science and technology strategy according to local conditions; and third, to effectively utilize industrial structure advantages. In summary, our study contributes to the literature by highlighting the relationship between defense science and technology innovation and high‐quality economic development in China. Our findings provide implications for policymakers and practitioners to make informed decisions in achieving sustainable and high‐quality economic development.
In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world.
Nighttime lights remote sensing has a significant advantage in exploring the economic development of cities. Based on nighttime lighting data, this study employed spatial direction analysis, exploratory spatial data analysis, and social network analysis to explore the spatial characteristics of economic development and analyzed the economic connection network structures within urban agglomerations in the New Western Land-sea Corridor (NWLSC) in western China. The results show that the spatial pattern of the Tianshan North slope urban agglomeration, Guanzhong Plain urban agglomeration, and Lanzhou–Xining urban agglomeration shrank, while other urban agglomerations expanded. The city economy of the Chengdu–Chongqing urban agglomeration (CCUA) and the Beibu Gulf urban agglomeration varied dramatically according to a LISA space-time transition analysis, which indicates a strong spatial dependence between cities in the local space. Within urban agglomerations, the economic connection between cities increased significantly, and central cities were at the core of the network and significantly influenced other cities. Among the urban agglomerations, economic connections among neighboring urban agglomerations in geographic space increased during the study period. The CCUA gradually developed into the center of the economic network in the NWLSC. Network density positively influenced economic connections. The degree centrality, closeness centrality, and betweenness centrality significantly enhanced the economic connections between city agglomerations. The study’s conclusions and methods can serve as the policy support for the cooperative development of urban agglomerations in NWLSC serve as a guideline for the development of other economically underdeveloped regions in the world.
Venture capital plays a vital role in boosting economic growth by providing an inexhaustible impetus for economic innovation and development. We use all the joint venture capital events of Chinese listed companies in the past 10 years to describe the characteristics of the joint venture capital network structure, identify the dynamic evolution characteristics of the community, and introduce random attacks and deliberate attacks to explore the resilience of joint venture capital cooperation. The study finds that the joint venture capital network in China has expanded in scale, with an increasing number of participants and a diversified investment industry. However, the connection between members within the network remains relatively loose, indicating fragmentation and a need to improve network quality. The community structure of core members is significant, with evident differences in scale. The network exhibits weak robustness, relying heavily on key enterprises and demonstrating a poor ability to resist external interference. The study proposes countermeasures and suggestions for optimizing the network structure of joint venture capital, aiming to enhance the environment and performance of joint venture capital and promote the high-quality development of China’s joint venture capital market.
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.