Search Results (5)

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and western regions. Empirical analysis is conducted using panel data from 22 provinces and municipalities across eastern and western China spanning 2014–2024, employing both a spatial difference-in-differences model and a dual machine learning model. Findings reveal that both AI technology spillovers and sustainable innovation significantly enhance the efficiency of industrial leapfrogging across regions. Their synergistic effects are pronounced, generating positive spatial spillovers. Institutional environments exert a significant influence on leapfrog industrial development. By regulating AI technology environments and sustainable innovation environments, institutional frameworks enhance leapfrogging efficiency, though this mediation exhibits a dual-threshold effect: most western provinces have yet to cross the first threshold. Industrial and economic heterogeneity weaken the efficiency of AI technology spillovers and sustainable innovation in facilitating industrial leapfrogging between eastern and western regions. This research provides robust empirical support for addressing industrial development imbalances and enhancing industrial resilience between eastern and western regions. Full article

Artificial intelligence (AI) is the driving force for the leapfrog development of science and technology, the optimization and upgrading of industry, as well as the overall leap in productivity. Using panel data of strategic emerging firms in Chinese A-Share Listed companies from 2012 to 2022, this study empirically examines the impact of AI on technological innovation through a two-way fixed-effects model. The study discovered that technological innovation capability can be greatly enhanced by the degree of AI present in strategic emerging industry businesses. This conclusion remains valid following a series of robustness tests. The mechanism study demonstrates how the degree of AI increases businesses’ capacity for technological innovation by lowering funding constraints and boosting R&D investment. According to heterogeneity analysis, AI has varying empowering effects on different industries within strategic emerging industries. Its strongest empowering effect is observed in the western region, with the central and eastern regions seeing the weakest effects. Additionally, the promotion effect of AI is greater for state-owned enterprises than for non-state-owned enterprises. To better play the role of AI in encouraging the technical innovation of firms in strategic emerging industries, it is required to establish dedicated funds, create an AI technology innovation platform, and develop differentiated regulations. Full article

In the 21st century’s global push for sustainable development, strategies for new urbanization and rural revitalization in China have transitioned from traditional geographic expansion to a focus on high-quality integration across ecological, social, and economic dimensions. Employing advanced methods such as the entropy weight TOPSIS, coupling coordination model, kernel density estimation, Markov chain, and geographic detector, this study comprehensively explores the spatiotemporal dynamics and driving mechanisms of urban–rural integration in China from 2001 to 2022. Key findings reveal increasing coupling coordination degrees in each province, with significant spatial variations. Notably, during the 15th Five-Year Plan, all regions, including eastern, central, and western areas, exhibited low-level coupling coordination. However, a decreasing ladder-like distribution emerged during the 13th and 14th Five-Year Plans, forming a development pattern centered on eastern coastal regions and spreading inland. The central regions experienced significant changes in development kernel density, while the national eastern and western regions remained relatively stable. Looking ahead, highly coupled regions are expected to maintain leadership, positively influencing neighboring areas and propelling overall urban–rural development towards sustainable goals. Conversely, low-level coupled regions require deeper reforms for leap-frog development. The core driving forces behind spatiotemporal differences in coupling coordination degrees involve innovation within the environment, government capabilities, openness to the outside world, and population agglomeration. Secondary roles are played by factors like non-agricultural industrialization, per capita GDP, government investment, and market conditions, while education, healthcare, transportation, and natural resource levels act as bridges in spatiotemporal differentiation. Overall, this study provides a concise spatiotemporal interpretation and strategic recommendations for urban–rural sustainable integration development, advancing towards a more harmonious, green, and just future in alignment with the core principles of sustainable development. Full article

The green transformation of the manufacturing industry is crucial for high-quality development of the Yangtze River Economic Belt, and environmental regulation and technological innovation may play key roles. Considering the undesirable output of the manufacturing industry, this paper adopted the undesirable-SE-SBM Model to measure the green transformation efficiency, which can reflect the core transformation performance. On this basis, this paper respectively adopted system generalized method of moments (SYS-GMM) and differential generalized method of moments (DIF-GMM) to explore the driving factors of green transformation, which fully considered the lag variable of transformation efficiency. The estimated results of green transformation showed that the efficiency of the Yangtze River Economic Belt has maintained an overall growth trend, while that of the eastern regions was higher than that of the central and western regions. The regional difference of transformation efficiencies showed a trend of convergence first and then expansion, however, a few regions such as Chongqing have achieved leapfrog development. The estimated results of driving factors showed the first-stage lag affected the green transformation positively, while the second-stage lag had a significantly negative effect. The ratchet effect and cumulative effect led to the continued efforts on green transformation, however, the timeliness of policy might cause a rebound in practice. As mentioned in green paradox, the environmental regulation had a negative effect, which might bring compliance costs. The technology innovation level indeed promoted the green transformation of manufacturing, but the scientific research investment did not exert the expected positive effect, while the utilization of many research funds lacked market orientation. Economic development level had a negative effect on green transformation, and it would play a positive effect only if it reached a certain stage. The industrialization and urbanization affected the efficiency positively, and the external dependence degree had a significant negative effect. It was not clear whether foreign direct investment (FDI) brought a pollution haven or pollution halo effect. In view of these conclusions, local governments should strictly enforce environmental regulations, build the regional green innovation system, improve marketization of research funds, optimize the export structure, and promote new urbanization and new industrialization. Full article

Over the past 30 years, both banks of the Strait of Malacca have experienced rapid economic growth and urbanization. Because of the difficulties in obtaining multi-phase urban land information over a large tropical area, spatial-temporal evolution analysis of the urban land areas in this region has been restricted. In this study, we developed an object-oriented retro-updating method for the rapid extraction of multi-phase urban land information to conduct a comprehensive study of the urban land area changes and to investigate the regional differentiation of both banks of the Strait of Malacca from 1990 to 2018, using grid analysis, zoning statistics, and gradient-direction analysis. The results revealed that from 1990 to 2018, the urban land areas on the eastern and western banks of the Strait of Malacca had increased by 1.51 times and 1.76 times, respectively, and in 2018, the urban land areas on the eastern bank were approximately four times greater than those on the western bank. The urban land areas on the eastern bank exhibit clusters centered around large cities and small strips along roads. Furthermore, the development of the urban land on the western bank was slow. As of 2018, the low development intensity areas on the western bank accounted for 98.73% of the total area, with only Medan achieving a higher development intensity. The development speeds of both banks followed the same zonal law (i.e., decreasing with increasing distance from the coastline). In addition, the urban land areas on both banks were mainly distributed within 60 km of the coast. Over the past 30 years, in decreasing order of their expansion scales, the major ports on both banks of the strait are Port Kelang, the Port of Singapore, Malacca Port, Penang Port, Belawan Port, and Dumai Port. In addition to the Port of Singapore and Dumai Port, which are in leapfrog expansion mode, the other ports exhibit edge-expansion patterns. Finally, investigation of the factors influencing the intensity of the urban land development revealed that natural resource conditions, economic and industrial structures, port development, and government policies all have caused the development of the western bank to lag behind that of the eastern bank. Full article