Social upgrading does not inherently follow economic upgrading; rather, they present a complex interplay. This paper, focusing on China, utilizes the coupling coordination degree and a panel regression model to shed light on the intricate interaction between social upgrading and economic upgrading. It is found that the coupling coordination degree of social and economic upgrading in China has improved from 0.33 to 0.49 since the mid-1990s, undergoing a shift from a stage of slight imbalance to low-level coordination. However, significant regional disparities are present in terms of economic upgrading, social upgrading, and their coupling coordination degree. Developed areas exhibit a higher degree of coupling coordination compared to less developed regions, indicating a connection between the coupling coordination degree and the level of economic growth. Economic globalization, public governance, and the legal environment positively impact the coupling coordination between social and economic upgrading, while economic privatization and corporate violations of law exert negative effects. The paper concludes with policy discussions for enhancing the coupling coordination between social and economic upgrading.
Construction land expansion is a key driver of urbanization and industrialization, yet it poses the risk of losing farmland and cascading impacts on food supply. The spatial characteristics of farmland occupied by construction land and its association with grain yield in China were unclear. We analyzed the characteristics of farmland converted into construction land, and its relationship with grain yield in China for 2000-2020. Construction land increased in area in central and western regions of China, and farmland decreased in area in southeastern China. The expansion of construction land in the North China Plain, Northeast China Plain, and the Loess Plateau, occurred at the expense of farmland. Except the southeast coast of China, grain yield increase was only weakly dependent on farmland area. Patterns in which farmland was converted into construction land and grain-yield change were highly coupled in southeastern coastal China, Sichuan Basin, Shandong Peninsula, and the Hu Huanyong Line. It should be noted that expansion in construction land area does have some influence on grain production; ultimately it is greatly affected by yield per unit area.
Urbanization interacts with land use through resource consumption and space encroachment. Clarifying the spatial correlations of the interactive relationship between urbanization and land use, along with their spatiotemporal dynamics, is of vital importance for addressing the complex interplay between urban development and land resources and identifying regional differences. However, previous studies have not sufficiently explored these issues. Herein, we introduce a coupling coordination degree (CCD) model and present the results of exploratory spatiotemporal analyses involving in-depth investigation of the CCD between urbanization quality and land-use intensity in 290 Chinese cities. The results demonstrate that the CCD for most cities was at the transition-period or basic-coordination stage. The dynamics of the spatial correlation of the CCD was found to increase from the east to the central and western regions, but this was found to decline overall. The movement direction and spatial dependence of the local spatial structure of the CCD exerted a dominant synergistic effect. The transition of the spatial correlation was mainly Type I (stable local and neighboring morphology), showing strong transfer inertia, path dependence, and locking features. Dynamic transitions occurred more in central and eastern cities. The results suggest that more cross-city cooperation could contribute to moderate land-resource exploitation for high-quality urbanization.
Improving the supply-demand balance of ecosystem services (SDBES) from the perspective of land use is essential for managing regional ecosystem and realizing sustainable development. By combining land use with the supply and demand of ecosystem services (SDES), a technical framework for defining land use threshold and optimizing its structure to improve the SDBES state was constructed and applied to a practical case. The spatial pattern of supply and demand of each ES in Lancang county was distinctly heterogeneous, with significant differences in SDES across different land use types. Strong spatial heterogeneity existed in the ESDR of each ES at the grid scale, and the areas of deficit were ranked as carbon sequestration > water conservation > habitat quality > food production. The structure of dry land, paddy field, tea, evergreen broad-leaved forest, grassland, urban construction land, and industrial and mining construction land were the focus of land use optimization. Based on the land use area thresholds under the SDBES, the optimal land use structure for maximizing comprehensive benefits contributed to a balanced relationship between SDES and promoted sustainable regional development. The study provides a new perspective and method for improving the SDBES state, alleviating land conflicts, and managing ecological environment.
Landscape in the middle and lower reaches of the Yellow River in China has undergone significant changes for thousands of years due to agricultural expansion. Lack of reliable long-term and high-resolution historical cropland data has limited our ability in understanding and quantifying human impacts on regional climate change, carbon and water cycles. In this study, we used a data-driven modeling framework that combined multiple sources of data (historical provincial cropland area, historical coastlines, and satellite data-based maximum cropland extent) with a new gridding allocation model for croplands distribution to reconstruct a historical cropland dataset for the middle and lower reaches of the Yellow River at a 10-km resolution for 58 time points ranging from the period 1000 to 1999. The cropland area in the study area increased by 2.3 times from 21.87 million ha in 1000 to 50.64 million ha in 1999. Before 1393, the area of cropland increased slowly and was primarily concentrated in the Weihe and Fenhe plains. From 1393‒1820, the area of cropland increased rapidly, particularly on the North China Plain. Since 1820, cropland cover has tended to become saturated. Our newly reconstructed results agreed well with remotely sensed data as well as historical document-based facts regarding cropland distribution.
Gully erosion, an integrated result of various social and environmental factors, is a severe problem for sustainable development and ecology security in southern China. Currently, the dominant driving forces on gully distribution are shown to vary at different spatial scales. However, few systematic studies have been performed on spatial scaling effects in identifying driving forces for gully erosion. In this study, we quantitatively identified the determinants of gully distribution and their relative importance at four different spatial scales (southern China, Jiangxi province, Ganxian county, and Tiancun township, respectively) based on the Boruta algorithm. The optimal performance of gully susceptibility mapping was investigated by comparing the performance of the multinomial logistic regression (MLR), logistic model tree (LMT), and random forest (RF). Across the four spatial scales, the total contributions of gully determinants were classified as lithology and soil (32.65%) > topography (22.40%) > human activities (22.31%) > climate (11.32%) > vegetation (11.31%). Among these factors, precipitation (7.82%), land use and land cover (6.16%), rainfall erosivity (10.15%), and elevation (11.59%) were shown to be the predominant factors for gully erosion at the individual scale of southern China, province, county, and township, respectively. In addition, contrary to climatic factors, the relative importance of soil properties and vegetation increased with the decrease of spatial scale. Moreover, the RF model outperformed MLR and LMT at all the investigated spatial scales. This study provided a reference for factor selection in gully susceptibility modeling and facilitated the development of gully erosion management strategies suitable for different spatial scales.
The imbalance in global streamflow gauge distribution and regional data scarcity, especially in large transboundary basins, challenge regional water resource management. Effectively utilizing these limited data to construct reliable models is of crucial practical importance. This study employs a transfer learning (TL) framework to simulate daily streamflow in the Dulong-Irrawaddy River Basin (DIRB), a less-studied transboundary basin shared by Myanmar, China, and India. Our results show that TL significantly improves streamflow predictions: the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872, showing a marked improvement in the Hkamti sub-basin. Despite data scarcity, TL achieves a mean NSE of 0.817, surpassing the 0.655 of the process-based model MIKE SHE. Additionally, our study reveals the importance of source model selection in TL, as different parts of the flow are affected by the diversity and similarity of data in the source model. Deep learning models, particularly TL, exhibit complex sensitivities to meteorological inputs, more accurately capturing non-linear relationships among multiple variables than the process-based model. Integrated gradients (IG) analysis further illustrates TL’s ability to capture spatial heterogeneity in upstream and downstream sub-basins and its adeptness in characterizing different flow regimes. This study underscores the potential of TL in enhancing the understanding of hydrological processes in large-scale catchments and highlights its value for water resource management in transboundary basins under data scarcity.
China’s outward foreign direct investment (FDI) is different from traditional FDI in various ways, for example being rooted in “Guanxi” in Chinese culture, influenced by government, and located in developed economies where they have limited ownership advantages compared with local firms. Chinese investment in the United States (the U.S.) is an example of how the location is influenced by economic factors, social linkages, as well as geopolitical events, such as the U.S.-China trade conflict, which deserves more academic attention. It is such a complex phenomenon that cannot be fully explained by traditional FDI theories, which mainly focus on economic factors. In this paper, we illustrate the historical development, distribution and firm heterogeneity of Chinese investment in the U.S. from 2000 to 2020, and use a conditional logit model to investigate the location factors. Our study reveals that the number of Chinese investment projects in the U.S. peaked in 2017 and has declined year-over-year since then. These projects are mainly located along the East and West coasts of the U.S. and around the Great Lakes, with the largest numbers in California and New York. Previous Chinese investment agglomeration and ethnic networks both influence the location choice of China’s outward FDI, even when controlling for regional attributes and economic embeddedness. In terms of firm heterogeneity, Chinese firms that enter the American market with greenfield investment modes, state-owned enterprises and firms in high-tech sectors are more likely to follow previous Chinese investment, but place less emphasis on Chinese ethnic linkages, implying that previous Chinese investment agglomeration can replace the role of Chinese ethnic networks for these firms. Finally, the U.S.-China trade conflict has significantly lessened the active role of Chinese ethnic networks and has reduced Chinese investment in states with higher industrial output.
In the context of accelerated globalization, intercity factor flows are becoming increasingly dependent on a reasonable and orderly spatial structure. Therefore, an in-depth study of the optimization and adjustment of spatial structure is essential for coordinated development. This study quantitatively evaluated urban development levels and introduced network analysis methods to analyse the spatial structure and robustness of development. The results indicated the following: (1) The urban development level in the Beijing-Tianjin- Hebei (BTH) region increased in all dimensions, and the transmission efficiency significantly improved. (2) The spatial structure of the BTH region has been relatively stable, as illustrated by the main pattern of the spatial distribution of central cities, with a trend towards contiguous development. (3) The ranking of network robustness is environment>society>economy, and the core network and key nodes are primarily located within the radiation of the three central cities of Beijing, Tianjin, and Shijiazhuang. (4) The coordinated development of the BTH region is effective but still needs to be optimized and adjusted, and the strategic significance of edge cities has not been completely exploited. This study aims to provide an emerging analytical perspective for optimizing regional spatial structure and promoting regional coordinated development.
