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The emergence and enduring diffusion of COVID-19 has had a dramatic impact on cities worldwide. The scientific aim of this study was to introduce geospatial thinking to research related to infectious diseases, while the practical aim was to explore the impact on population movements and urban linkages in the longer term following a pandemic outbreak. Therefore, this study took 366 cities in China as the research subjects while exploring the relationship between urban contact and the outbreak of the pandemic from both national and regional perspectives using social network analysis (SNA), Pearson correlation analysis and multi-scale geographically weighted regression (MGWR) modeling. The results revealed that the number of COVID-19 infections in China fluctuated with strain variation over the study period; the urban contact network exhibited a significant trend of recovery. The pandemic had a hindering effect on national urban contact, and this effect weakened progressively. Meanwhile, the effect exhibited significant spatial heterogeneity, with a weakening effect in the eastern region ≈ northeast region > central region > western region, indicating a decreasing phenomenon from coastal to inland areas. Moreover, the four major economic regions in China featured border barrier effects, whereby urban contact networks constituted by cross-regional flows were more sensitive to the development of the pandemic. The geostatistical approach adopted in this study related to infectious disease and urban linkages can be used in other regions, and its findings provide a reference for China and other countries around the world to respond to major public health events.

Intangible cultural heritage (ICH) represents the outstanding crystallization of human civilization and it has received extensive attention from scholars in various countries. Studying the spatial distribution and influencing factors of ICH in the Three Gorges Reservoir Area can help to improve the protection and utilization of ICH. Using quantitative statistical analysis methods, GIS spatial analysis methods, and Geodetector, we analyzed the level structure (provincial and national levels), category structure (ten categories), and spatial distribution of 509 national and provincial ICH items in the Three Gorges Reservoir Area and then explored their influencing factors. We concluded that: (1) The structural characteristics of ICH vary significantly, and the level structure is dominated by provincial ICH items; the category structure is complete and mainly includes traditional skill and traditional music. (2) The spatial distribution of ICH in the Three Gorges Reservoir Area is dense in the west and sparse in the east, with a pattern of “one main core, three major cores, and two minor cores”. There are large differences in the degree of concentration of ICH at the county level; different categories of ICH have different distribution densities and concentration areas. Yuzhong District, Shizhu County, and Wanzhou District are dense areas of distribution for different categories of ICH. (3) The influences of different factors on the spatial distribution of ICH in the Three Gorges Reservoir Area vary greatly. Socioeconomic and historical–cultural factors are more influential than natural geographic factors, among which economic development, culture, and ethnicity are the most influential, but the interaction between the two dimensions of natural geography and socioeconomic and historical culture has a more significant influence on the spatial distribution of ICH than single-dimensional factors. (4) Proposals for optimizing the spatial layout, protection, and development of ICH in the Three Gorges Reservoir Area are provided from the perspectives of culture and tourism integration and sustainable development.

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

Human activities and land transformation are important factors in the growth of carbon emissions. In recent years, construction land for urban use in China has expanded rapidly. At the same time, carbon emissions in China are among the highest in the world. However, little is known about the relationship between the two factors. This study seeks to estimate the carbon emissions and carbon sequestrations of various types of land based on the land cover data of 137 county-level administrative regions in Shandong Province, China, from 2000 to 2020.The study estimated the carbon emissions for energy consumption using energy consumption data and night-time light images, hence, net carbon emissions. The Tapio decoupling coefficient was used to analyze the decoupling between the net carbon emissions and construction land, and where the model for the decoupling effort was constructed to explore the driving factors of decoupling. The results showed that net carbon emissions in Shandong Province continued to increase, and the areas with high carbon emissions were concentrated primarily in specific districts of the province. The relationship between net carbon emissions and construction land evolved from an expansive negative decoupling type to a strong negative decoupling type. Spatially, most areas in the province featured an expansive negative decoupling, but the areas with a strong negative decoupling have gradually increased. The intensive rate of land use and efficiencies in technological innovation have restrained carbon emissions, and they have contributed to an ideal decoupling situation. Although the intensity of carbon emission and the size of the population have restrained carbon emissions, efforts towards decoupling have faded. The degree of land use has facilitated carbon emissions, and in recent years, efforts have been made to achieve an ideal decoupling. The method of estimation of net carbon emissions devised in this research can lend itself to studies on other regions, and the conclusions provide a reference for China, going forward, to balance urbanization and carbon emissions.

Since the beginning of 2020, the COVID-19 epidemic has dramatically influenced the human socioeconomic system. If we conceive of the city as a complex organism with a metabolism, then the daily flows of people, materials, and information into and out of a city can be regarded as its metabolism. To evaluate the real-time impact of COVID-19 on a city’s economy and society, we construct a health index of cities (HIC) using human mobility big data from Baidu and analyze the temporal and spatial changes of the HIC in China. The results show that both internal and intercity population movements have been significantly affected by the COVID-19 epidemic, and the decline in both was more than 50% at some points. The intercity movement is more affected than the intracity movement, and the impact is more sustained. Compared with the same period before the outbreak, the HIC in China decreased by 28.6% from January 20 to April 21, 2020. The deterioration rate of the HIC was faster than the growth rate of COVID-19 cases, but the improvement in the HIC was much slower than the decline in COVID-19 cases. Although the HIC is highly correlated with COVID-19 in both the spatial and temporal dimensions, the effect of the epidemic on the HIC varied across regions. The HIC fell more significantly in provincial capitals, such as Beijing, Shanghai, Guangzhou, and Zhengzhou, and in urban agglomerations, and these cities’ HICs were lower with a longer-lasting reduction. This study can serve as a frame of reference for studying the real-time impact of the epidemic, helping cities’ policymakers to quickly assess its socioeconomic impact. By extension, this index can be applied to other countries and other public health emergencies.

The Tibetan Plateau (TP) is undergoing rapid urbanization. To improve urban sustainability and construct ecological security barriers, it is essential to quantify the spatial patterns of urbanization level on the TP, but the existing studies on the topic have been limited by the lack of socioeconomic data. This study aims to quantify the urbanization level on the TP in 2018 with Luojia1-01 (LJ1-01) high-resolution nighttime light (NTL) data. Specifically, the compounded night light index is used to quantify spatial patterns of urbanization level at multiple scales. The results showed that the TP had a low overall urbanization level with a large internal difference. The urbanization level in the northeast, southeast and south of the TP was relatively high, forming three hotspots centered in Xining City, Lhasa City and Shangri-La City, while the urbanization level in the central and western regions was relatively low. The analysis of influencing factors, based on the random forest model, showed that transportation and topography were the main factors affecting the TP's spatial patterns of urbanization level. The comparison analysis with socioeconomic statistics and traditional NTL data showed that LJ1-01 NTL data can be used to more effectively quantify the urbanization level since it is more advantageous for reflecting the spatial extent of urban land and describing the spatial structure of socioeconomic activities within urban areas. These advantages are attributed to the high spatial resolution of the data, appropriate imaging time and unaffected by saturation phenomena. Thus, the proposed LJ1-01 NTL-based urbanization level measurement method has the potential for wide applications around the world, especially in less-developed regions lacking statistical data. Using this method, we refined the measurement of the TP's urbanization level in 2018 for multiple scales including the region, basin, prefecture and county levels, which provides basic information for the further urban sustainability research on the TP.

The Beautiful China Initiative (BCI) is a plan for the sustainable development of the Chinese nation as well as for China to fulfill the United Nations’ 2030 Agenda for Sustainable Development. The Chinese government’s “five-in-one” approach provides strategic arrangements for developing the BCI, and President Xi Jinping proposed a timetable and “road map” for the BCI at the National Conference on Ecological and Environmental Protection. Nevertheless, the theoretical basis, evaluation index system, evaluation criteria and effectiveness of the BCI are currently unclear. This paper begins by exploring the basic content of the BCI from narrow and broad perspectives. It regards the theory of human-nature harmonious coexistence and the five-in-one beauty theory as the core theoretical bases of the BCI and constructs a five-element BCI evaluation index system (ecological environment, green development, social harmony, system perfection and cultural heritage) and utilizes the assessment method of the United Nations’ Human Development Index to assess scientifically the effectiveness of the BCI in 341 prefecture-level cities. The results show the average BCI index (the Chinese Academy of Sciences Beauty Index) score to be 0.28, which is quite low, while the average scores for the individual element indexes of the ecological environment index, green development index, social harmony index, system perfection index and cultural heritage index are 0.6, 0.22, 0.29, 0.22 and 0.07, respectively. All of these are relatively low values, with relatively large discrepancies in regional development, indicating that progress in the BCI is generally slow and unbalanced. To realize the BCI’s timetable and roadmap to a high quality and high standard, it is suggested that a common system for evaluating the progress of the BCI is developed and promulgated so that dynamic monitoring and phased evaluations can take place; BCI technical assessment standards are compiled and published; BCI comprehensive zoning is undertaken; pilot projects adapted to local conditions are launched in BCI sample areas; and BCI results are incorporated into performance indicators at all levels of government.

Urbanization is one of the most consequential human activities on earth. The contradictions between urbanization and the eco-environment are particularly prominent in urban agglomerations with high industrial concentrations. Based on the Environmental Kuznets Curve (EKC) theory, we propose the hypothesis of the Urbanization-EKC (UEKC), which states that in the process of urbanization, eco-environmental quality first deteriorates and then improves, following a “U” trend. Taking 19 Chinese urban agglomerations as empirical cases, this paper analyzed the multi-dimensional coupling curves between urbanization and the eco-environment. The results show that the coupling curves between urbanization and the eco-environment are heterogeneous, as a result of differences among urban agglomerations and various eco-environmental indicators. In five mega-urban agglomerations, we found that the coupled curve of urbanization and eco-environmental quality index (EQI) is a compound function containing logarithm and quadratic terms, following the “U” curve shape of “quick down, slow up”; the turning point of EQI improvement was found at an urbanization rate of 47%. The empirical results validate that the UEKC hypothesis is tenable for developed urban agglomerations. This study provides a methodological reference for researching the evolutionary relationship between urbanization and the eco-environment and provides decision support for a more harmonious development of humans and nature in China's urban agglomerations.

Understanding the interactions between humans and nature in the Anthropocene is central to the quest for both human wellbeing and global sustainability. However, the time-space compression, long range interactions, and reconstruction of socio-economic structures at the global scale all pose great challenges to the traditional analytical frameworks of human-nature systems. In this paper, we extend the connotation of coupled human and natural systems (CHANS) and their four dimensions—space, time, appearance, and organization, and propose a novel framework: “Coupled Human and Natural Cube” (CHNC) to explain the coupling mechanism between humans and the natural environment. Our proposition is inspired by theories based on the human-earth areal system, telecoupling framework, planetary urbanization, and perspectives from complexity science. We systematically introduce the concept, connotation, evolution rules, and analytical dimensions of the CHNC. Notably there exist various “coupling lines” in the CHNC, connecting different systems and elements at multiple scales and forming a large, nested, interconnected, organic system. The rotation of the CHNC represents spatiotemporal nonlinear fluctuations in CHANS in different regions. As a system continually exchanges energy with the environment, a critical phase transition occurs when fluctuations reach a certain threshold, leading to emergent behavior of the system. The CHNC has four dimensions—pericoupling and telecoupling, syncoupling and lagcoupling, apparent coupling and hidden coupling, and intra-organization coupling and inter-organizational coupling. We mainly focus on the theoretical connotation, research methods, and typical cases of telecoupling, lagcoupling, hidden coupling, and inter-organizational coupling, and put forward a human-nature coupling matrix to integrate multiple dimensions. In summary, the CHNC provides a more comprehensive and systematic research paradigm for understanding the evolution and coupling mechanism of the human-nature system, which expands the analytical dimension of CHANS. The CHNC also provides a theoretical support for formulating regional, sustainable development policies for human wellbeing.

Urbanization and eco-environment coupling is a research hotspot. Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited. Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops. We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that: (1) The use of dynamic simulation is an increasing trend, the relevant theory is being developed, and modeling processes are being improved; (2) Dynamic simulation technology has become diversified, refined, intelligent and integrated; (3) Simulation is mainly performed for three aspects of the coupling, multiple regions and multiple elements, local coupling and telecou-pling, and regional synergy. However, we also found some shortcomings: (1) Basic theories are inadequately developed and insufficiently integrated; (2) The methods of unifying systems and sharing data are behind the times; (3) Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified. Additionally, simulation of telecoupling does not quantify parameters and is not systemically unified, and therefore cannot be used to represent spatial synergy. In the future, we must promote communication between research networks, technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations. Finally, we must build decision support systems to plan and ensure regional sustainable urbanization.

The development zone (DZ) is a typical representative of China's regional economic development model. By comparing the development pattern of DZs in China from 2005 to 2017, we find that there was strong imbalance in 2005, but this trend decreased in 2017, mainly due to the rapid increase in the number of DZs in the central and western regions. Moreover, there is a significant correlation between the spatial pattern of the DZs and economic development. But compared with the spatial pattern of economic development, DZs have dispersed more strongly from 2005 to 2017. Indeed, the rapid dispersion of DZs has not fundamentally changed the imbalanced pattern of China's economic development from 2005 to 2017.

China's rapid urbanization has produced problems of excessive resource use and environmental pollution, threatening the country's sustainable development. Previous studies mainly focused on empirical observation of the interactions between urbanization and the eco-environment, mainly using econometric models which lacked detailed explanations of the coupling mechanisms between various elements. No quantitative models have been developed to describe the complex nonlinear relationships between various elements, so our understanding of urbanization and eco-environment coupling is vague, and therefore not conducive to coordinating the relationship between them. Coupling urbanization with the eco-environment allows us to simulate interactions between them and enables us to explore the most suitable scenarios for sustainable development. We designed and developed the Urbanization and Eco-environment Coupler (UEC) using system dynamics to simulate regional urbanization and eco-environment coupling and to compare different sustainable development scenarios. UEC integrates human and natural elements. It includes four urbanization submodels (the economy, society, population and construction land) and five eco-environment submodels (water, arable land, ecology, pollution and energy). UEC can fully represent the nonlinear interactions between these submodels by identifying feedback linkages. This allows us to identify an optimal sustainable regional development pattern. We chose the Beijing-Tianjin-Hebei urban agglomeration as a case study research area and obtained the following results: (1) prioritizing urbanization will accelerate economic growth and increase pollution emissions whereas prioritizing the eco-environment will negatively affect both total population and arable land; (2) when sufficient policy and technical support is directed to a particular area, urbanization may not further degrade the eco-environment; and (3) simulation results for various scenarios show that the key to guaranteeing sustainable development is improving technical and political support rather than further restricting urbanization. The UEC model is a significant aid to improving sustainable regional planning.

China’s rapid urbanization has aggravated its air pollution problems and subsequently results in increased interests in the relationship between urbanization and PM2.5 concentrations. Currently, research on the ways that urbanization directly influences air quality are comparatively mature; however, quantification of the spillover effect of urbanization on PM2.5 concentrations in adjacent regions remains understudied. Additionally, urbanization is a multi-dimensional phenomena in China and it is important to distinguish the ways in which the multiple aspects of urbanization influence PM2.5 concentrations. Taking the Beijing-Tianjin-Hebei Urban agglomeration of China as an example, this study explored the spatio-temporal change and spatial dependence pattern of PM2.5 concentrations, and further examined and compared the unique impacts of the multiple dimensions of urbanization on PM2.5 concentrations. The results showed that spatial dependence of PM2.5 generally took place over the range of 200 km in the study area. Economic urbanization had a much stronger influence on air quality than land urbanization or population urbanization; it was determined to be the best indicator to represent the impact of urbanization, especially the spillover effect of urbanization on PM2.5 concentrations in adjacent regions. Over the period 2000–2010, the impacts from both local and neighboring economic urbanization on PM2.5 concentrations increased significantly. In contrast, land urbanization mainly exerted influence on air quality in focal regions. Population urbanization showed no significant direct or spillover effects on PM2.5 concentrations in the whole urban agglomeration. This study provides evidence that the influence of urbanization within and across cities must be considered when conducting joint air pollution control in an urban agglomeration. PM2.5 taxes based on the diffusion and transfer of environment pressures are one possible policy mechanism that could help to account for the spillover effect of economic urbanization on air quality. System-based approaches and flow-centered governance are two suggested ways to coordinate the management of both urbanization and air quality.

Air pollution has caused wide concern in China, and many governance policies and plans have been implemented in recent years. Based on counterfactual quasi-natural experiments, we analyzed the implementation effects of autumn and winter air pollution control policies in the Jing-Jin-Ji region and surrounding areas using a difference-in-differences (DID) model. The control group was selected based on geographical and meteorological factors, and we analyzed the impact of the policies on six pollutants. The results show that the policies reduced air pollution overall, but not every pollutant. Due to the policy contribution, the concentrations of PM

Objective: To compare the curative effect of balanced acupuncture combined with TongduZhengji manipulation vs acupuncture in the treatment of acute lumbar sprain. Methods: Clinical data of 71 patients with acute lumbar sprains in our hospital from January 2020 to December 2020 were retrospectively analyzed. Patients were divided into single group (n=35) and combined group (n=36) based on treatment methods. The single group received only acupuncture treatment, while the combined group received balanced acupuncture combined with TongduZhengji manipulation. The treatment efficacy, pain level, lumbar function and motion of the lumbar spine were compared between the two groups. Results: The Visual Analogue Scale (VAS) scores of the combined group were lower than those of the single group after 3, 4, and 5 days of treatment (P0.05). The Roland-Morris Disability Questionnaire (RMDQ) score of the combined group showed no significant difference compared with that of the single group after 1 and 2 days of treatment (P>0.05), and were lower than those of the single group after 3, 4, and 5 days of treatment (P0.05), and was higher than that of the single group after 4 and 5 days of treatment (P0.05), and was lower than that of the single group after 3, 4, and 5 days of treatment (P

Food security is closely related to individual livelihood and social stability, and assessing the state of food security is essential for making agricultural policies. Nonetheless multi‐scale assessment of national food security state with explicit consideration of availability and access remains to be resolved. In this study, a spatially explicit crop model was used to simulate the production of major crops in China (i.e., wheat, maize, rice, and soybeans). Food production (as simulated by the crop model) and GDP per capita relative to the global average were then used to develop food security indicators to evaluate China's food security levels on grid, provincial, and economic zone scales. The Getis–Ord method was further applied to analyze the spatial aggregation of food security and food insecurity provinces. Results show that food security situation improved in mainland China during 1990–2015, attributed largely to the enhanced purchasing power. On the grid scale, changes in food security state went through two stages: Before 2000, the regions in the eastern part of the Hu Line were food security, while the regions in the western part were food insecurity; after 2000, the state of food security greatly improved from insecurity to security in Northwest China, and the areas of the highest security level considerably increased in eastern China. On the provincial scale, food security state in most provinces improved or almost unchanged over time. Exception was found in coastal provinces, with food security state was greatly affected by urbanization. Food security hot spots were primarily detected in central China, while cold spots were detected in Xinjiang, Qinghai, and Tibet. On the economic zone scale, food security areas in the eastern part decreased during 1990–2010 but increased thereafter, while food security areas in the other three economic zones increased from 1990 to 2015. To ensure food security, China should maintain the arable land areas, improve the quality of farmlands, mobilize farming enthusiasm, and advance agricultural marketization.

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by COVID-19 pandemic. To better measure real-time decline of human mobility and changes of socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and places. Bi-directional causality between SI and the new COVID-19 cases was detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. Underdeveloped countries are more affected by the COVID-19 pandemic.

Ecosystem services (ESs) are increasingly affected by human interventions, and the ES balance between supply and demand plays a vital role in guaranteeing the expected efficacy of ecosystem restoration projects. However, the spatial disparities of ES balance, along with its determinants and spillover effects, remain unclear, constraining effective ES management and landscape planning. The aim of this study was to fill these gaps by quantifying the ES balance in the restoring Loess Plateau using an expert-based ES matrix approach and by examining the spatial associations between ES balance and driving factors via an integrated regression approach. The results showed that the county-scale ES balance was closely related to the land-use composition and population density. Geographic locations of counties with ES surplus were mainly concentrated in mountainous areas with high proportions of woodland and grassland, while urbanized land and a high population density resulted in an ES deficit. Forest and grass regeneration, due to revegetation practices, alleviated ES deficits, while rapid urbanization and population growth aggravated ES imbalance. The integrated regression approach demonstrated that the ES balance and its dependencies (i.e., landscape metrics and population density) had remarkable spatial heterogeneity and spillover effects, which should be practically considered in localized ES management and landscape optimization. Excessive agricultural reclamation and urban expansion improved grain productivity and economic profits but deteriorated landscape fragmentation, further aggravating the ES deficit. In contrast, excessive revegetation practices promoted ecosystem restoration and improved ES surplus but threatened food security. Therefore, an appropriate balanced state should be maintained for sustainable ecosystem restoration through timely and efficient policy interventions and landscape optimization.

Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

Monitoring human activities in border areas is challenging due to the complex geographical environment and diverse people. China has the longest terrestrial boundary and the highest number of neighboring countries in the world. In this study, a human activity intensity index (HAI) was proposed based on land cover, population density, and satellite-based nighttime light for a long-term macroscopic study. The HAI was calculated at 1 km resolution within the 50 km buffer zone of China’s land boundary on each side in 1992, 2000, 2010, and 2020, respectively. Results show that human activity is low in about 90% of the study area. Overall, the HAI on the Chinese side is higher than that on the neighboring side, and the intensity of land use on the Chinese side has increased significantly from 1992 to 2020. Among China’s neighbors, India has the highest HAI with the fastest growth. With the changes in the HAI between China and its neighboring countries, four regional evolution patterns are found in the study area: Sino-Russian HAI decline; Sino-Kazakhstan HAI unilateral growth; Indian HAI continuous growth; China and Indochina HAI synchronized growth. Hotspot analysis reveals three spatial evolution patterns, which are unilateral expansion, bilateral expansion, and cross-border fusion. Both the “border effect” and “agglomeration effect” exist in border areas. The HAI changes in border areas not only impact the eco-environment but also affect geopolitics and geoeconomics. The HAI can be used as an instrument for decision-making and cooperation between China and neighboring countries in such areas as ecological protection, border security, and border trade.

Oncolytic adenovirus-mediated gene therapy shows promise for cancer treatment; however, the systemic delivery of oncolytic adenovirus to tumors remains challenging. Recently, mesenchymal stem cells (MSCs) have emerged as potential vehicles for improving delivery. Yet, because the oncolytic adenovirus replicates in MSCs, balancing MSC viability with viral load is key to achieving optimal therapeutic effect. We thus developed an all-in-one Tet-on system that can regulate replication of oncolytic adenovirus. Then, we loaded the novel oncolytic adenovirus carrying interleukin (IL)-24 and/or Endostatin in human umbilical cord blood-mesenchymal stem cells (hUCB-MSCs) for glioma therapy. In vitro assays demonstrated that this novel oncolytic adenovirus could efficiently replicate and kill glioma cells while sparing normal cells. Moreover, doxycycline effectively regulated oncolytic adenovirus replication in the hUCB-MSCs. The doxycycline induction group with dual expression of IL-24 and Endostatin exhibited significantly greater antitumor effects than other groups in a xenograft model of glioma. Thus, this strategy for systemic delivery of oncolytic adenovirus with its oncolytic activity controlled by a Tet-on system is a promising method for achieving antitumor efficacy in glioma, especially for metastatic tumors.

Based on socioeconomic statistical data, transport data, and network big data, the urban connection index (UCI) was constructed in terms of industry, transportation, information, and innovation, and the high-quality development index (HDI) was established from five aspects: innovation, coordination, green development, openness, and sharing. Taking Lanzhou-Xining urban agglomeration as a case, the urban connection intensity and high-quality development level were measured to analyze the relationship between them. From 2012 to 2018, the UCI and HDI of each city showed different degrees of growth. Note that there exist significant regional differences, with Lanzhou and Xining having the largest difference. The biggest gap among cities lies in the innovative connection intensity. Moreover, urban external connections are closely related to high-quality development, especially innovation and green development. For every 1% increase in industrial and transport connection, the HDI will increase by 0.317% and 0.159%, respectively. This study provides a methodological reference for measuring urban connectivity and provides decision support for high-quality development in China and other countries.

The scientific evaluation of carrying capacity and the formulation of adaptive regulation policies are powerful ways to achieve sustainable development goals. In order to quantitatively and accurately diagnose the sustainable development state of urban agglomeration, this paper responds to the "Future Earth" framework; takes the carrying capacity as the breakthrough point; embeds the conjugate thought; considers the elements of the resources, the environment, the ecology, and the development; and establishes the conjugate evaluation model and the evaluation index system of sustainable carrying capacity. In order to solve the actual bottleneck problem of urban agglomeration, this work identifies the key obstacle factors, constructs the multi-scenario dynamic coupling (MSDC) model, recognizes the sensitivity policies, and proposes the adaptive regulation polices. Taking the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) in arid areas as a case study, it was found that from 2007 to 2017, the carrying capacity gradually increased, and the sustainable development showed a benign transformation trend in the UANSTM. It will also be in a transitional stage of growth from now to 2035, and the steady development of the economy and the society and the good maintenance of the resources, the environment and the ecology are equally important. The policies on population intervention and on the management of water resources play a vital role, and the speed of economic development should be controlled throughout the whole process. The study confirms that the effective regulation is necessary for the sustainable development of the urban agglomeration in the future, and the key lies in improving technological progress and governance policy support. It is expected that this new and complete research framework for the conjugate evaluation of the sustainable carrying capacity of urban agglomeration and multi-scenario policy regulation can be applied to other regions or urban agglomerations in developing countries and can make a possible breakthrough in promoting the theoretical exploration and practice of sustainable development.

By promoting financial agglomerations to support green development in a region is a keyway for China to resolve the sharp contradiction between economic growth and environmental protection. However, existing research only considered the promotion effect of financial agglomerations on green development, but the spatio-temporal non-stationarity of that effect has been overlooked. Using a panel data of 285 prefecture-level cities in China and based on the evaluation of green development by a Driving-Pressure-State-Impact-Response (DPSIR) model, this paper analyzes the spatial correlation of financial agglomeration on green development. The paper also investigates the differences in the spatio-temporal influence of financial agglomeration on green development from both global and local perspectives by employing a Bivariate Local Indicators of Spatial Association (BLISA) model and a Geographically and Temporally Weighted Regression (GTWR) model. The results indicate that: (1) There exists significant spatial dependency between financial agglomeration and green development from 2003 to 2015, with Low-Low (L-L) and Low-High (L-H) spatial clusters as the main cluster types. (2) From the local perspective, the promoting effect of financial agglomerations on green development has showed significant spatial heterogeneity with a gradually decreasing trend from the southeast coast to the northwest inland of China. This work can help to develop policies for supporting green development by formulating differential strategies for financial agglomerations.

The outbreak of COVID-19 exposed the shortage of medical resources and the imbalance between cities in China. To better reveal this inequality, this study uses cartograms and half-violin plots to visualize the hospital beds per 10,000 people, medical personnel per 10,000 people and number of 3A hospitals in China. We find that China has made remarkable progress in the primary healthcare system; however, there is still a significant imbalance in the distribution of medical resources, especially the high-quality part. There is a two- to three-fold gap in primary medical care resources between diverse cities, and high-quality medical resources are highly concentrated in cities with high administrative rank and influence.

Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA’s economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis; (2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems; (3) network resilience presented an obvious core–edge structure, and the spatial correlation and spillover effect between blocks were substantial; and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries.

With the advent of Information and Communication technology (ICT) in modern age, the statement of “death of distance” has received numerous discussions. This article contributes a new empirical study to the debate of “death of distance” by considering the effect of spatial autocorrelation in the estimation of distance decay effect with the incorporation of network autocorrelation in spatial econometric modeling. This work is based on a city-level dataset from China's largest social networking site called Weibo. The findings are shown as following. First, the coefficient value of network autocorrelation term (0.007, significant at 0.01 level) suggests that the city-level online social links are spatially dependent. In other words, these social connections are not randomly distributed across space but tend to form spatial clusters where neighboring links are more similar. Second, controlling spatial autocorrelation in the data, a distance decay effect on the formation of online social links is unveiled with a much smaller scaling exponent of the distances (i.e., 0.276) as compared to those (e.g., 2.0, 1.8, 1.45, 1.06, 1.03, 0.4, and 0.5) in existing studies. This research provides a useful modeling framework to analyze the real-world driving forces that characterize the patterns of social interactions in virtual space and thus advance our understanding in the connection of virtual and real spaces.

This paper uses data for the period 1950–2050 compiled by the United Nations Population Division together with methods including spatial autocorrelation analysis, hierarchical cluster analysis and the standard deviational ellipse, to analyze the spatio-temporal evolution of population and urbanization in the 75 countries located along the routes of the Silk Road Economic Belt and the 21st-century Maritime Silk Road, to identify future population growth and urbanization hotspots. The results reveal the following: First, in 2015, the majority of Belt and Road countries in Europe, South Asia and Southeast Asia had high population densities, whereas most countries in Central Asia, North Africa and West Asia, as well as Russia and Mongolia, had low population densities; the majority of countries in South Asia, Southeast Asia, Central Asia, West Asia and North Africa had rapid population growth, whereas many countries in Europe had negative population growth; and five Belt and Road countries are in the initial stage of urbanization, 44 countries are in the acceleration stage of urbanization, and 26 are in the terminal stage of urbanization. Second, in the century from 1950 to 2050, the mean center of the study area’s population is consistently located in the border region between India and China. Prior to 2000, the trajectory of the mean center was from northwest to southeast, but from 2000 it is on a southward trajectory, as the population of the study area becomes more concentrated. Future population growth hotspots are predicted to be in South Asia, West Asia and Southeast Asia, and hotspot countries for the period 2015–2030 include India, China, Pakistan and Indonesia, though China will move into negative population growth after 2030. Third, the overall urban population of Belt and Road countries increased from 22% in 1950 to 49% in 2015, and it is expected to gradually catch up with the world average, reaching 64% in 2050. The different levels of urbanization in different countries display significant spatial dependency, and in the hundred-year period under consideration, this dependency increases before eventually weakening. Fourth, between 2015 and 2030, urban population hotspots will include Thailand, China, Laos and Albania, while Kuwait, Cyprus, Qatar and Estonia will be urban “coldspots.” Fifth, there were 293 cities with populations over 1 million located along the Belt and Road in 2015, but that number is expected to increase to 377 by 2030. Of those, 43 will be in China, with many of the others located in India, Indonesia and the eastern Mediterranean.

Although China’s urbanization process requires corresponding infrastructure development, it has reached a consensus that wastewater transportation and wastewater treatment capacity are not satisfactory in China. We use an input-oriented non-radical Data Envelopment Analysis (DEA) model to investigate the construction efficiencies of urban sewers and wastewater treatment capacity in China’s 70 megacities during 2006–2012. The results show that in 2012, the average construction efficiency of urban sewers is 0.76, more than the average construction efficiency of wastewater treatment capacity that is 0.68. During the sample period, Hefei, Liuzhou, Shenzhen, and Wuxi present better construction efficiencies than other cities, while Jilin, Wenzhou, and Xianyang have relatively lower construction efficiencies. 26 cities should develop infrastructure for both urban sewers and wastewater treatment capacity. The growth rates of both urban sewers and wastewater treatment capacity are lower than those of urban population in Xiamen. More attentions should be paid by relevant government agencies.

Objective: Axillary lymph node dissection (ALND) may be unnecessary in 20%–60% of breast cancer patients with sentinel lymph node (NSLN) metastasis. The aim of the present study was to review the medical records of Chinese patients with early-stage breast cancer and positive NSLN metastasis to identify clinicopathological characteristics as risk factors for non-NSLN metastasis.Methods: The medical records of 2008 early-stage breast cancer patients who received intraoperative sentinel lymph node biopsy (SLNB) between 2006 and 2016 were retrospectively reviewed. These patients were clinically and radiologically lymph node-negative and had no prior history of receiving neoadjuvant chemotherapy or endocrinotherapy. The clinicopathological characteristics of patients with positive NSLN metastasis who underwent ALND were investigated.Results: In the present study, 296 patients with positive NSLN metastases underwent ALND. Positive non-NSLN metastases were confirmed in 95 patients (32.1%). On univariate analysis, ≥ 3 positive NSLN metastases (P P = 0.023), and lymphovascular invasion (P = 0.04) were associated with non-NSLN metastasis (P Conclusions: The number of positive NSLNs, NSLN macrometastases, and lymphovascular invasion were correlated with non-SLN metastasis. The number of positive SLNs was an independent predictor for non-NSLN metastasis. When 2 or 3 risk factors were present in one patient, the probability of non-NSLN was higher than that in the American College of Surgeons Oncology Group Z0011 trial (27.3%); thus, avoiding ALND should be considered carefully.

The haze pollution accompanies rapid urbanization and industrial development is the central environmental problem for academia, the government, and the public in China today. Recent studies have investigated the different aspects of haze, but no holistic research has yet been conducted that includes both natural and anthropogenic factors and spatial effects. This study used the Air Quality Index (AQI) as the measure of haze pollution and 14 natural and anthropogenic factors as explanatory variables. We applied exploratory spatial data analysis and the spatial Durbin model (SDM) to analyze the spatial distribution and variation pattern of the AQI and to quantitatively estimate the contributions and spatial spillovers of different natural and anthropogenic factors on the air quality of 289 prefecture-level cities in 2014. The results show that approximately 1.255 billion people in 280 Chinese cities were exposed to an unhealthy atmospheric environment. A significant positive spatial autocorrelation of AQI values was identified, with the influence of urban air pollution extending, on average, between 600 and 800 km. The AQI of a city increased by more than 0.45% for every 1% increase in the average AQI of neighboring cities. The most heavily polluted regions are mainly located in urban agglomeration areas–the areas with the highest population densities. Urbanization, urban population aggregation and industrialization had a significant positive impact on the AQI. The spillover effect of car density is also significant. Except for temperature, all the natural factors that we studied have a negative effect on the AQI, with vegetation cover having a significant spatial spillover effect around cities. Only the ratio of green space to urban built-up areas has a significant local effect, while wind speed has a more significant effect locally than on neighboring areas. The amount of urban land, per capita gross domestic product, elevation, and relative humidity have no significant effect. The final remarks of this paper suggest three strategies to prevent haze and to develop more sustainable cities.

City size hierarchy and distribution are always at the heart of urban studies, as they have a special ability to reveal the rules of city development and urban system spatial layouts. There is however a data deficiency with regard to city size hierarchy and distribution; in particular, an absence of complete statistics and spatial differences from the global perspective. To fill this research gap, this paper investigates global city size hierarchy based on 2014 data of more than 190 countries and regions by using classic models of “rank-size” rule, the fractal theory and the law of the primate city. We analyzed the spatial patterns, regional features, and implications for China from multi-scale and multi-dimension perspectives. The results show that: (1) There is an obvious pyramid structure of global city size distribution, but differences exist among countries and regions with different economic development types, suggesting a feature of “various types with pyramid dominated”; (2) The primate feature of global city size distribution is not very obvious. However, the primacy ratios of developed countries are much higher than others, and significant differences exist among different regions; (3) The global Zipf exponent and Hausdorff dimension are 0.66 and 1.29, respectively. Cities with middle ranks are in the majority, and the monopoly power of large and super cities is effective to a certain extent, indicating a decreasing concentration tendency in the city size distribution and a convergence trend in terms of relative population size, especially with regard to the medium and small cities; (4) The medium and small cities develop swiftly with limited agglomeration effect of large cities, and Chinese cities would significantly influence global urban progress and spatial patterns. Therefore, developing 780–800 cities will be reasonable for China's urbanization efforts by 2030.

The comprehensive regionalization of Chinese human geography is based on the rules governing regional differentiation of Chinese physical geographic factors. Based on regional differences and similarities in human factors, this study divides the whole country into two levels of relatively independent, complete and organically linked human geographic units. As a fundamental, comprehensive, cutting-edge, practical and important task, the comprehensive regionalization of human geography highlights the characteristics, regional and sub-regional features, complexity and variety of spatial differences between factors of Chinese human geography. It is capable of promoting the development of human geography based on local conditions, providing basic scientific support to national and local development strategies, such as the Belt and Road Strategy, new urbanization and environmental awareness, and creating a sound geopolitical environment in key areas. Using results from existing physical and human geographic zoning studies, and in accordance with the principles of synthesis, dominant factors, the relative consistency of the natural environment, the relative consistency of social and economic development, the consistency of the regional cultural landscape, the continuity of spatial distribution and the integrity of county-level administrative divisions, and taking as its basis the division of human geography into 10 major factors (nature, economy, population, culture, ethnicity, agriculture, transportation, urbanization, the settlement landscape and administrative divisions), this paper constructs an index system for the comprehensive regionalization of Chinese human geography through a combination of top-down and bottom-up zoning and spatial clustering analysis. In this study, Chinese human geography is divided into eight regions and 66 sub-regions. The eight human geography regions are (I) Northeast China, (II) North China, (III) East China, (IV) Central China, (V) South China, (VI) Northwest China, (VII) Southwest China, and (VIII) Qinghai and Tibet. This zoning proposal fills gaps in studies involving the non-comprehensive regionalization of Chinese human geography. Each human geography region and sub-region has different topographical, climatic, ecological, population, urbanization, economic development, settlement landscape, regional cultural and ethno-religious attributes. This proposal on the comprehensive regionalization of Chinese human geography dovetails closely with previous studies on comprehensive regionalization in Chinese physical geography, Chinese economic zoning, and Chinese agricultural zoning. It shows that, under the dual roles of nature and humans, there are certain rules of regional differentiation that govern the comprehensive regionalization of Chinese human geography.

The rapid spread of information and communications technologies is accelerating the development of our economy and society. While information and communications technologies have become an important development tool that reshapes many aspects of the world’s activities, digital inequality emerges on a worldwide scale due to difference in access to and use of information and communications technologies. Digital inequality, which is a new phenomenon in the current information era, will widen the gap between developed and developing countries/regions. China has witnessed rapid informationization along with its booming economic growth over the past decade. However, digital inequality, as an important aspect of social inequalities in China, has received only scant attention currently. Based on the technique of cartogram, Figures display provincial information and communications technologies development index (IDI) values in 2010 and 2015 in China, exhibiting high inequality. Regions of the highest information and communications technologies levels are distributed along China's eastern coast. On the contrary, the provinces in the west present very low information and communications technologies levels. The coefficient of variation of IDI values dropped from 0.30 to 0.18, indicating declining digital inequality in China. A causal bidirectional relation probably exists between the economic development and information and communications technologies levels.

Background Breast cancer is the most common cancer among women worldwide. Here, we report the prevalence of BRCA1/2 mutations in patients with high‐risk breast cancer from Inner Mongolia and Jilin, China, which was a part of a nationwide project on the detection of BRCA1/2 mutations in Chinese patients with hereditary breast cancer. Methods According to the criteria, index patients from a total of 245 independent families were initially recruited. All 49 exons of BRCA1 and BRCA2 and adjacent noncoding regions were screened for mutations based on next‐generation sequencing from collected saliva. Results We detected 17 BRCA1/2 variants in 18 of 216 (8.3%) index patients with high‐risk breast cancer. Among these, seven mutations were novel, including four BRCA1 mutations (c.123_124delCAinsAT, c.5093_5096delCTAA, c.5396‐2A>G, and c.2054delinsGAAGAGTAACAAGTAAGAAGAGTAACAAGAAG), and three BRCA2 mutations (c.304A>T, c.7552_7553insT, and c.9548_9549insA). The BRCA1/2 variants were identified in 14% (8/57) of the patients with triple‐negative breast cancer and in 6.3% (10/159) of the patients with non‐triple‐negative breast cancer. There was no significant difference between the two groups (p = 0.07). A higher frequency for BRCA1 mutations was observed in patients with triple‐negative breast cancer than in those with non‐triple‐negative breast cancer (12.3% vs. 2.5%, p = 0.004). The frequencies of the BRCA2 mutations were not significantly different between patients with triple‐negative breast cancer and those with non‐triple‐negative breast cancer (1.8% vs. 3.8%, p = 0.46). Conclusion We found that patients with triple‐negative breast cancer had a higher frequency of BRCA1 mutations than those with non‐triple‐negative breast cancer. In this study, no significant associations between the BRCA1/2 mutation status and age, family history of breast cancer, ovarian cancer, pancreatic cancer and prostate cancer, number of primary lesions, tumor size, or lymph node metastasis were observed.

Rapid urbanization and large-scale industrialization in China have produced problems that affect resources, the environment, and local ecology, which are obstacles to urban ecosystem resilience. Ecosystem resilience is critical for recovery from adverse natural events and the security of urban ecosystems. In this paper, we constructed the evaluation indexes of urban ecological resilience systems (UERs) using DPSIR framework to quantify urban ecological resilience index (UERI) for 35 major cities in China, and we evaluated the sustainability of UER networks using ecological network analysis. The results show that: (1) UERI generally increases annually, but it gradually decreases geographically from developed coastal areas to developing inland areas; (2) factors that affect the DPSIR components Drivers, State and Responses significantly increase UERI, and factors that affect Pressures and Impact decrease UERI; all paths of DPSIR passed the causal hypotheses test; (3) the ideal value for resilience of an urban ecosystem network was 0.550; we found that resilience in 2005 was 0.659, and in 2010 was 0.512 but in 2015 was 0.462 (

The 2030 Agenda for Sustainable Development provided brand new goals and action targets for human well-being and development, but the COVID-19 pandemic has cast a shadow on the implementation of the Sustainable Development Goals (SDGs). It is therefore essential to provide a reference for making policy adjustments and transformations to promote the realization of SDGs in the post-pandemic era. Based on a literature review of the progress and policies of SDGs across countries worldwide, we find that research on sustainable policies has rapidly increased since the SDGs issued in 2015 with particular focuses on eco-environment, sustainable policies, green economy, sanitation and health, and water sanitation. Most countries are in the process of nationalization, institutionalization, and universalization of the SDGs through incorporating the SDGs into national development frameworks, enabling extensive participation and negotiation mechanisms, and promoting the SDGs’ national publicity. Countries of different economic and institutional backgrounds demonstrate divergent development pathways, priorities, measures, and progress in the implementation of SDGs. Despite significant global progress during the last five years, the North–South divide emerges in the policy action and achievement of SDGs. The least developed countries in sub-Sahara Africa and South Asia appear to be difficult or even unable to implement the SDGs and monitor the progress. In the post-pandemic era, particular attention shall be given to integrating SDGs and achieve synergy among goals, concretizing short-and medium-term priorities toward the SDGs targets for all countries, strengthening multilateralism and global cooperation among countries and continents, providing reliable data and approaches for real-time impact assessment and process monitoring, and promoting an inclusive engagement and integrative implementation with multiple stakeholders and consortiums.

Global urbanization is exerting severe stress and having far-reaching impacts on the eco-environment, and yet there exists a complex non-linear coupling relationship between the two. Research on the interactive coupling effect between urbanization and the eco-environment will be a popular area of study and frontier in international earth system science and sustainability science in the next 10 years, while also being a high-priority research topic of particular interest to international organizations. This paper systematically collates and summarizes the international progress made in research on interactive coupling theory, coupling relationships, coupling mechanisms, coupling laws, coupling thresholds, coupling models and coupling optimization decision support systems. The research shows that urbanization and eco-environment interactive coupling theories include the Kuznets curve theory, telecoupling theory, planetary boundaries theory, footprint family theory and urban metabolism theory; most research on interactive coupling relationships is concerned with single- element coupling relationships, such as those between urbanization and water, land, atmosphere, climate change, ecosystems and biodiversity; the majority of research on interactive coupling mechanisms and laws focuses on five research paradigms, including coupled human and nature systems, complex social-ecological systems, urban ecosystems, social-economic-natural complex ecosystems, and urbanization development and eco-environment constraint ring; the majority of interactive coupling simulations use STIRPAT models, coupling degree models, multi-agent system models and big data urban computer models; and research has been carried out on urbanization and eco-environment coupling thresholds, coercing risk and optimal decision support systems. An objective evaluation of progress in international research on interactive coupling between urbanization and the eco-environment suggests that there are six main research focal points and six areas lacking research: a lot of research exists on macroscopic coupling effects, with little research on urban agglomeration and scale coupling effects; considerable research exists on single-dimension coupling effects, with little on multiple-dimension coupling effects; a great deal exists on “one-to-one” dual- element coupling effects, with little on “many-to-many” multiple-element coupling effects; a lot exists on positive feedback coupling effects, and little on negative feedback coupling effects; a great deal exists on empirical coupling effects, and little on theoretical coupling effects; a great deal exists on the use of simple quantitative methods, and little on using integrated simulation methods. Future studies should focus on coupling effects between urbanization in urban agglomerations and the eco-environment, spatial scale coupling effects, multi-dimensional coupling effects, telecoupling effects, “one-to-many” and “many-to-many” element coupling effects, and positive and negative feedback coupling effects. There is also a need to strengthen the development and application of dynamic models for multi-element, -scale, -scenario, -module and -agent integrated spatiotemporal coupling systems and further improve theoretical innovations in coupling effect research and integrate and form complete and diverse coupling theoretical systems.

The Tibetan Plateau (TP) is an important area that affects global sustainable development. Quantifying spatiotemporal patterns of urbanization is crucial for maintaining the sustainability on the TP. This study took Xining City, the largest city on the TP, as an example to understand the urban expansion in this region in the past 50 years. We combined the high-resolution spy satellite data and China’s long-term urban land dataset (CULD) to quantify the urban expansion of Xining City. The object-oriented random forest classification was performed to extract urban land from spy satellite data in 1969, and the inter-annual correction was used to combine urban land information from 1969 to 2017. We found that the proposed approach can accurately quantify the urban expansion of Xining City over the past half century with an overall accuracy of 91% and a kappa coefficient of 0.86. Such high accuracy benefits from the fine resolution of spy satellite data and the consistency of CULD. We also found that Xining City experienced accelerated and fragmented urban sprawl to higher altitude areas, as a result of socioeconomic development and topographical limitations. The acceleration of urban expansion was more obvious, and the urban landscape fragmentation was more serious at high altitude areas. Such urban expansion encroached on cropland and grassland, and caused increased risks of landslides and other geological disasters. Therefore, Xining City urgently needs to promote the development of compact cities to control urban sprawl at higher altitude areas and provide a reference for improving urban sustainability across the TP. In this study, we analyzed the urban expansion of Xining city from 1969 to 2017, and provided a reliable way to understand the long-term spatiotemporal urbanization based on remote sensing, which has the potential for wide applications. In addition, the extracted urban information can help to improve the urban sustainability of Xining City and the entire TP.

With the deployment and real-time monitoring of a large number of micro air quality monitoring stations, new application scenarios have been provided for the research of air quality prediction methods based on artificial intelligence. Integrating deep learning with multi-task learning, this paper proposes a hybrid model for air quality prediction to leverage data from intensive air quality monitoring stations. The proposed model consists of a shared layer, a task-specific layer, and a multi-loss joint optimization module. It is tested on three monitoring stations located in three different districts of Lanzhou City, China, for PM2.5 concentration prediction. The results show that: (1) When the number of convolutional layers of convolutional neural network in the shared layer and the number of gated recurrent unit layers in the task-specific layer exist in two layers, model performs the best, and its predictability of the optimization algorithm with early-stopping will be significantly improved. (2) Using the proposed model to predict PM2.5 concentration on horizon t + 1 , the mean absolute error and root mean square error are 4.54 and 7.96, respectively, indicating better performance in intensive air quality prediction than previous models based on simple hybridization. (3) The predictive performance on different stations is different, and the proposed model performs better than other models when there are large fluctuations and sudden changes in the data. Overall, the proposed model has good temporal stability and generalization ability and provides a new method for air quality prediction in intensive air quality monitoring scenarios.

Topsoil erosion is a widespread and major threat to terrestrial ecosystems that leads to environmental deterioration. Monitoring soil erosion dynamics and understanding the driving mechanisms are of great significance for optimizing ecological restoration programs and combating land degradation. We apply a widely used soil erosion assessment model to quantify the changes in soil erosion on the Loess Plateau (LP) in response to afforestation-induced vegetation cover and land use/cover changes. Furthermore, we examine the relationship between landscape metrics and soil erosion using spatial econometric approach. The results indicate that soil erosion in the LP substantially decreased through afforestation efforts, while the observed sediment load declined significantly as a result of soil and water conservation programs. A number of largescale ecological programs altered the spatial configuration and structural composition of the landscape from different aspects, which disturbed the formation and transportation of soil erosion, eventually resulting in a reduction in soil loss. The spatial association between landscape metrics and soil erosion indicated that properly fragmented landscapes can effectively decrease soil erosion. Due to the spatial spillover, soil erosion in one county can be affected by soil erosion in adjacent counties. Therefore, the spillover effect between counties should not be ignored, and the responsibility for formulating and implementing solutions to combat land degradation should not fall solely on individual local governments. Preferably, the cooperation between different regions should be strengthened as a critical way to improve the efficiency of programs combating land degradation.

This paper evaluated the spatial patterns and hierarchical clustering of final energy consumption in China from 2014 to 2016. For this purpose, exploratory spatial data analysis, kernel density estimation, and spatial hierarchical clustering were applied. The results presented various spatial characteristics of final energy consumption with different fuel types and consumption sectors. i) Coal and heat consumption showed significant positive spatial autocorrelations, whereas petroleum products exhibited significant negative spatial autocorrelations, and natural gas and electricity without any significant spatial autocorrelations. ii) The industry sector exhibited clusters of hot spots in eastern China and cold spots in southwestern China, which was obviously different from the north-south difference of coal and heat in the sector of residential energy and of transport, storage and post. iii) Residential coal consumption in rural areas exhibited the most significant disparity. In addition, northeastern, northern and eastern parts of China were identified as three spatial clusters of final energy consumption. Further, it indicated to improve the residential coal utilization level in key coal-producing regions such as Shanxi and Guizhou Provinces.

Significant differences in urban greening have occurred in Chinese cities, accompanied by China's rapid urbanization. However, there are relatively few studies on the spatial differentiation of urban greening in China at the city level. In addition, there is no unanimous conclusion on the main factors influencing the spatial differentiation of urban greening. Based on 2014 emission inventory data from 289 cities, the spatial differentiation pattern and spatial correlation characteristics of the urban green space ratio, urban green coverage rate, and public green area per capita were calculated and analyzed using global and local Moran's I. We then used ordinary least squares, spatial error model, spatial autoregression, and geographically weighted regression to quantify the impact and spatial variations of China's economy on urban greening. The results showed (1) a significant spatial dependence and heterogeneity existed in urban greening values, and the patterns showed influences of both the stage of economic development and spatial agglomeration; (2) regression models revealed per capita GDP had a positive effect on the urban green space ratio and public green area per capita while the urbanization rate, secondary industry, urban land, and population density had opposite effects on these two greening indexes; and (3) geographically weighted regression revealed per capita GDP had a greater influence on urban greening in the northwestern region than in the southeastern region. The study could constitute a valuable reference for mid-to-long-term green space planning policy in diverse parts of China and could further assist in coordinating the development of urban greening and economic growth.

Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP) and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

Financial agglomeration is a key approach to the green transition of China’s economy and society. However, few studies have examined the spatial threshold effect on the regional boundary of financial agglomeration. In this paper, the mechanism of the spatial threshold effect is analyzed with spatial panel Durbin model and panel threshold regression (SPDM_PTR) model over 272 prefectural-level cities in China from 2003 to 2014. The results show: (1) Financial agglomeration plays an important role in promoting local green development. (2) The spatial spillover effect progresses in stages. When the agglomeration level exceeds 1.949, the spatial spillover effect of high-level financial agglomeration is significantly stronger as opposed to medium- and low-levels. (3) The regional boundary of the spillover effect changes with the financial agglomeration level, with 1480 Km for high-level, 860 Km for medium-level and 700 Km for low-level financial agglomeration. These findings suggest concrete evidence for developing policies for further encouraging green development of regional financial agglomerations.

Tourism is an important sustainable industry in the economy that optimizes the industrial structure. Thus, as a core part of this market, tourism enterprises perform a key role in the effective operation of this industry. This paper applies data envelopment analysis (DEA) and Malmquist index (MI) models to calculate the efficiency of Chinese tourism enterprises between 2005 and 2014. Results showed that: (1) The efficiency and the total factor productivity change index (TFPC) of tourism enterprises remained low, and both have decreased. (2) The efficiency of regional tourism enterprises across China cloud be characterized as high in the east region, low in the central region, and high in both northeast and western regions. (3) The efficiency levels of the cities of Beijing and Shanghai were ahead of the country over the period of this study, while Chongqing, Tibet, Qinghai, and Ningxia all possess a number of obvious advantages in the western region. (4) Centers of overall tourism enterprise efficiency mainly moved in a southeast-to-northwest direction over the period of this research. (5) The spatial autocorrelation of tourism enterprise efficiencies is also assessed in this study, and the results show that the comprehensive efficiency (CE) of tourism enterprises in southeastern coastal regions of China tended to a certain spatial agglomeration effect, while the correlation between the central region and northern China was not significant. (6) The Geodetector model is applied to analyze the key factors driving the spatial differentiation of tourism enterprise efficiencies, and the results show that the degree of opening to the outside world, potential human capital, and traffic conditions were the most important factors driving spatial differentiation in the efficiency of tourism enterprises.