Chinese Geographical Science最新文献

This study selected the Sino-US route data from the top 30 global container liner companies between December 1, 2019, and December 29, 2019, as the data source utilizing the complex network research methodology. It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural features. The network’s evolution is examined from three perspectives, namely, time, space, and event influence, aiming to comprehensively explore the network’s evolution mechanism. The results revealed that: 1) the weighted Sino-US container shipping network exhibits small-world and scale-free properties. Key hub ports in the United States include NEW YORK NY, SAVANNAH GA, LOS ANGELES CA, and OAKLAND CA, whereas SHANGHAI serving as the hub port in China. The geographical distribution of these hub ports is uneven. 2) Concerning the evolution of the weighted Sino-US container shipping network, from a temporal perspective, the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and development, with a need for further enhancement in competitiveness and development speed. The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage. The shipping development in Northern China, the Western and Eastern United States, and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances. From a spatial perspective, the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani (BBV) model are confirmed through theoretical derivation. The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network. From an event impact perspective, the Corona Virus Disease 2019 (COVID-19) pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity. The network lacks sufficient resilience and stability in emergency situations. 3) Based on the analysis of the structural characteristics and evolution of the weighted Sino-US container shipping network, recommendations for network development are proposed from three aspects: emphasizing the development of hub ports, focusing on the balanced development of the network, and optimizing the layout of Chinese ports.

Ice and snow tourism in China has grown significantly since the country successfully hosted the Beijing Winter Olympics. Climatic conditions profoundly impact the development of ice and snow tourism; however, most studies have focused on constructing different climate suitability indicators for ice and snow tourism to evaluate individual regions, lacking horizontal comparative studies across multiple regions. This study aims to enrich the connotation of climate suitability for ice and snow sports, establish an evaluation model based on snowfall amount, temperature, and wind speed, and use daily meteorological data from 1991 to 2021 to horizontally compare the climate suitability for ice and snow sports in major ski tourism destinations in China. This study boasts four major findings: 1) the average ice and snow sports climate index of each region decreases over time, and the overall suitability of the climate for ice and snow sports is reducing; 2) northern Xinjiang exhibits the most evident regional differentiation from ‘very suitable’ to ‘generally suitable’; 3) the spatial zoning of climate suitability for ice and snow sports exhibits heterogeneity, as northern Xinjiang is divided into two ‘suitable and above’ zones with rotating empirical orthogonal function (REOF). Correspondingly, the four provinces of Hebei, Heilongjiang, Jilin, and Liaoning are divided into three ‘generally suitable and above’ zones; 4) snowfall amount is the main factor affecting the climate suitability of ice and snow sports in the major ski tourist destinations in China.

In an era of rising trade protectionism and frequent antiglobalization events, strengthening regional economic connections has important practical significance for resisting external economic shocks, improving economic resilience, and promoting regional economic development. Based on input-output analysis, value-added decomposition, and network analysis, this paper uses long-term, multiregional input-output data to measure the spatiotemporal patterns of Asian production networks (APNs) and the influence of the Belt and Road Initiative (BRI). The results demonstrate that Asian countries account for a high and growing proportion, with a weak ability to capture value in global production networks (GPNs). The BRI has significantly strengthened production cooperation among Asian nations, promoting participation and strengthening abilities to capture value in GPNs. The continuous stability and strengthening of internal cooperation of APNs improves resilience from external shocks. Inside APNs, the proportions of East Asia and Southeast Asia show an overall downward trend, while South, West, and Central Asia show an increasing trend. China has also replaced Japan as the largest participant, and the rise of South Asian countries, led by India, has transformed APNs from a binary to a triple structure. In addition, China’s upstream degree index increased significantly, whereas Japan experienced the largest decline, causing a level of high-end vacancy in APNs. We propose that the most urgent task for the Asian countries to enhance APNs is to achieve stratified development and build more complete production circles.

Under the background of complex international situation, how to build the special geo-economic space of China-Russia border lies in strengthening their foreign trade resilience against external shocks. Based on empirical evidence from ten prefecture-level China-Russia border regions in Northeast China, this paper analyzed the spatiotemporal evolution of foreign trade resilience under different shocks. Furthermore, through the Panel Regression model, the mechanism of the industrial structure on the foreign trade resilience in contraction period and expansion period was discussed. The results showed that: 1) from 2004 to 2021, foreign trade in China-Russia border regions experienced five phases. The overall foreign trade resilience was higher than expected, showing a rising volatility trend, but there was significant spatial heterogeneity in the ability of cities to cope with shocks. 2) Highly specialized clusters were mainly concentrated in Yichun, Heihe and Da Hinggan Ling Prefecture, while Mudanjiang and Yanbian performed better in related and unrelated diversification. 3) In different stages of economic system evolution, the response mode, degree and result of border foreign trade resilience to regional industrial structure showed obvious stage characteristics. During the contraction period, related diversification was more conducive to improving the resistance through risk spillovers. During the expansion period, specialization played a more significant role in improving regional resilience through self-reinforcing effect. These results are beneficial for expanding the resilience theory, ensuring border economic security and optimizing border industrial investment layout.

Data on discrete, isolated attributes of the marine economy are often used in traditional marine economic research. However, as the focus of urban research shifts from internal static attributes to external dynamic linkages, the importance of marine economic network research is beginning to emerge. The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development. Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China, we use social network analysis (SNA) to discuss the characteristics of its evolution as of 2010, 2015, and 2020 and its governance. The following results were obtained. 1) In terms of topological characteristics, the scale of the marine economic network in China’s coastal areas has accelerated and expanded, and the connections have become increasingly close; thus, this development has complex network characteristics. 2) In terms of spatial structure, the intensity of the connection fluctuates and does not form stable development support; the group structure gradually becomes clear, but the overall pattern is fragmented; there are spatial differences in marine economic agglomeration radiation; the radiation effect of the eastern marine economic circle is obvious; and the polarization effect of northern and southern marine economic circles is significant. On this basis, we construct a framework for the governance of a marine economic network with the market, the government, and industry as the three governing bodies. By clarifying the driving factors and building objectives of marine economic network construction, this study aims to foster the high-quality development of China’s marine economy.

Recycled moisture is an important indicator of the renewal capacity of regional water resources. Due to the existence of Yulong Snow Mountain, Lijiang in Yunnan Province, southeast of the Qinghai-Tibet Plateau, China, is the closest ocean glacier area to the equator in Eurasia. Daily precipitation samples were collected from 2017 to 2018 in Lijiang to quantify the effect of sub-cloud evaporation and recycled moisture on precipitation combined with the d-excess model during monsoon and non-monsoon periods. The results indicated that the d-excess values of precipitation fluctuated between −35.6‰ and 16.0‰, with an arithmetic mean of 3.5‰. The local meteoric water line (LMWL) was δD = 7.91δ¹⁸O + 2.50, with a slope slightly lower than the global meteoric water line (GMWL). Sub-cloud evaporation was higher during the non-monsoon season than during the monsoon season. It tended to peak in March and was primarily influenced by the relative humidity. The source of the water vapour affected the proportion of recycled moisture. According to the results of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the main sources of water vapour in Lijiang area during the monsoon period were the southwest and southeast monsoons. During the non-monsoon period, water vapour was transported by a southwesterly flow. The recycled moisture in Lijiang area between March and October 2017 was 10.62%. Large variations were observed between the monsoon and non-monsoon seasons, with values of 5.48% and 25.65%, respectively. These differences were primarily attributed to variations in the advection of water vapour. The recycled moisture has played a supplementary role in the precipitation of Lijiang area

City cluster is an effective platform for encouraging regionally coordinated development. Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management, realize sustainable development, and assist China in achieving the carbon peaking and carbon neutrality goals. This paper applies the improved gravity model and social network analysis (SNA) to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the the Yangtze River Delta (YRD) city cluster in China and its influencing factors. The results demonstrate that: 1) the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure. The network association number and density show an upward trend, indicating closer spatial association between cities, but their values remain generally low. Meanwhile, the network hierarchy and network efficiency show a downward trend but remain high. 2) The spatial association network of carbon emissions in the YRD city cluster shows an obvious ‘core-edge’ distribution pattern. The network is centered around Shanghai, Suzhou and Wuxi, all of which play the role of ‘bridges’, while cities such as Zhoushan, Ma’anshan, Tongling and other cities characterized by the remote location, single transportation mode or lower economic level are positioned at the edge of the network. 3) Geographic proximity, varying levels of economic development, different industrial structures, degrees of urbanization, levels of technological innovation, energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster. Finally, policy implications are provided from four aspects: government macro-control and market mechanism guidance, structural characteristics of the ‘core-edge’ network, reconfiguration and optimization of the spatial layout of the YRD city cluster, and the application of advanced technologies.

Abstract

Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to global warming conditions. However, the spatiotemporal snow cover patterns are challenging in western Jilin, China due to natural conditions and sparse observation. Hence, this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive microwave (PMW) snow depth (SD) data from 1987 to 2018, and revealed the potential influence of climate factors on SD variations. The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend (P > 0.05) at a rate of 0.009 cm/yr. In snowmelt periods, the snow cover contributed to an increase in volumetric soil water, and the change in SD was significantly affected by air temperature. The correlation between SD and air temperature was negative, while the correlation between SD and precipitation was positive during December and March. In March, the correlation coefficient exceeded 0.5 in Zhenlai, Da’an, Qianan, and Qianguo counties. However, the SD and precipitation were negatively correlated over western Jilin in October, and several subregions presented a negative correlation between SD and precipitation in November and April.

One of the greatest challenges in the agroecosystem is to improve cropland intensification while preserving agroecosystem services. While many studies have investigated the effect of cropland intensification on agroecosystem service, the interactive coupling and coordination among these factors remain largely unexplored. In view of this, this study performed a case study of the Loess Plateau in Shaanxi Province, China and constructed comprehensive evaluation models to quantify the cropland intensification and agroecosystem service in this area. Balance analysis and the coupling coordination degree model were used to evaluate the interactive relationship between cropland intensification and agroecosystem service, and statistical analysis and spatial autocorrelation were used to analyze the spatial characteristics and potential mechanism of the coupling coordination. Results show that both the cropland intensification and agroecosystem service in the study area were relatively low yet gradually increased from 2000 to 2020. Agroecosystem service lag was identified as the dominant unbalanced development type. Improving the supply capacity of agroecosystem services plays a key role in the balanced development of cropland in the Loess Plateau. The coupling coordination degree between cropland intensification and agroecosystem service ranges from basic coordination to serious incoordination. Therefore, cropland intensification practices in the area should be optimized to enhance this coordination degree. An upward trend was also observed in the coupling coordination degree from 2000 to 2020. The withdrawal of marginal cropland in the Grain for Green program is one of the most important reasons for this trend, especially for the northern region. Around 83.6% of the high-high clusters are concentrated in the southern region of the Loess Plateau, whereas 70.5% of the low-low clusters are distributed in the northern region. These clustering characteristics are mainly attributed to the environmental suitability of these areas for agriculture and their degree of economic development.

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity. In the recent past, a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts. However, existing research mainly focuses on physical effects, such as geographical distance and high-speed railway connections. These studies ignore the intangible drivers in a changing environment, the more digitalized economy and the increasingly solidified innovation network structure. Thus, the focus of this study is on estimating determinants of innovation networks, especially on intangible drivers, which have been largely neglected so far. Using city-level data of Chinese patents (excluding Hong Kong, Macao, and Taiwan Province of China), we trace innovation networks across Chinese cities over a long period of time. By integrating a measure on Information and Communications Technology (ICT) development gap and network structural effects into the general proximity framework, this paper explores the changing mechanisms of Chinese innovation networks from a new perspective. The results show that the structure of cross-region innovation networks has changed in China. As mechanisms behind this development, the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity, such as geographical distance. Since digitalization and coordinated development are the mainstream trends in China and other developing countries, these countries’ inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.