Search Results (27)

New quality productive forces serve as a catalyst for high-quality development and act as a critical driver of Chinese-style modernization. This study evaluated the degree of new quality productive force in China’s five major urban agglomerations between 2013 and 2022 using the entropy approach. Additionally, it utilized kernel density estimation, the Dagum Gini coefficient, and Markov chain analysis to explore the spatial and temporal dynamics of these forces and their evolutionary trends. The findings revealed the following: (1) Overall, the new quality productive forces in China’s five major urban agglomerations have exhibited a steady upward trend, although the overall level remains relatively low. Among these regions, the Pearl River Delta ranks the highest, followed by the Yangtze River Delta, Beijing–Tianjin–Hebei, Chengdu–Chongqing, and the Urban Cluster in the Middle Reaches of the Yangtze River. Nevertheless, significant potential for improvement persists. (2) The traditional Markov probability transfer matrix suggests that the new quality productive forces in these urban agglomerations are relatively stable, with evidence of “club convergence”. Meanwhile, the spatial Markov transfer probability matrix indicates that transfer probabilities are influenced by neighborhood contexts. (3) Over time, the new quality productive forces in Chinese urban agglomerations show a tendency to concentrate at higher levels, reflecting gradual improvement. The developmental state and evolutionary patterns of new quality productive forces in Chinese urban agglomerations are thoroughly evaluated in this paper, along with advice for accelerating their growth to promote Chinese-style modernization. Full article

Urbanization induces rapid plant environmental modifications, leading to alterations in plant diversity distribution patterns and plant homogenization. However, how plant diversity is distributed along urbanization gradients in regional urban agglomerations and its relationship with environmental factors are not well defined. In three nearby Henan Province Chinese cities—Zhengzhou, Kaifeng, and Zhongmu—along an urbanization gradient, the distribution pattern of plant diversity was quantified. Both native and non-native plants found in urban green spaces were taken into consideration. A total of 176 plant quadrats were selected and separated into three urbanization gradient types using space-constrained hierarchical clustering: urban core, urban suburb, and urban outskirt. Polynomial fitting was used to characterize the spatial distribution patterns of plants along the urbanization gradient, and Pearson correlation and the Mantel test were employed to examine the effects of environmental factors, including longitude, latitude, altitude, distance from the urban center, temperature, and illumination, on plant diversity. A total of 313 vascular plant species, comprising 137 woody species and 176 herbaceous species, were examined. Along the three urbanization gradients, remarkable variations in plant diversity for woody and herbaceous species were observed. The spatial patterns of plant diversity were consistent across cities, whereas woody plants and herbaceous plants displayed the opposite behavior. Distance to the city center and temperature were the most substantial environmental effect factors for the diversity of woody plants, whereas light factors had a major impact on herbaceous plants. These findings show different life-type plants are affected differently by urbanization, and they offer managers and planners a recommendation for increasing urban plant diversity by executing various interventions throughout the urban gradient. Full article

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control. Full article

Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China’s technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises’ websites in 2022. It explores the network’s inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure. Full article

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. Full article

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM_2.5 pollution. This study examines the spatiotemporal distribution of PM_2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM_2.5 concentrations. Results reveal significant regional differences in both average PM_2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM_2.5 concentrations. According to the MLP model’s feature importance identification, temperature is the most significant factor affecting PM_2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM_2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM_2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies. Full article

This study addresses the knowledge gap regarding the spatiotemporal evolution of Chinese urban agglomerations using long time series of luminescence remote sensing data. The evolution of urban agglomerations is of great significance for the future development and planning of cities. Nighttime light data provide a window for observing urban agglomerations’ characteristics on a large spatial scale, but they are affected by temporal discontinuity. To solve this problem, this study proposes a ridge-sampling regression-based Hadamard matrix correction method and constructs consistent long-term nighttime light sequences for China’s four major urban agglomerations from 1992 to 2018. Using the Getis-Ord Gi* hot-cold spot, standard deviation ellipse method, and Baidu search index, we comprehensively analyze the directional evolution of urban agglomerations and the correlations between cities. The results show that, after correction, the correlation coefficient between nighttime light intensity and gross domestic product increased from 0.30 to 0.43. Furthermore, this study identifies unique features of each urban agglomeration. The Yangtze River Delta urban agglomeration achieved balanced development by shifting from coastal to inland areas. The Guangdong-Hong Kong-Macao urban agglomeration developed earlier and grew more slowly in the north due to topographical barriers. The Beijing-Tianjin-Hebei urban agglomeration in the north has Beijing and Tianjin as its core, and the southeastern region has developed rapidly, showing an obvious imbalance in development. The Chengdu-Chongqing urban agglomeration in the inland area has Chengdu and Chongqing as its dual core, and its development has been significantly slower than that of the other three agglomerations due to the influence of topography, but it has great potential. Overall, this study provides a research framework for urban agglomerations based on four major urban agglomerations to explore their spatiotemporal characteristics and offers insights for government urban planning. Full article

China’s urban agglomerations (UAs) are striving to build a new development pattern oriented towards the new era and new stage, and the population distribution is facing new problems of synergy with the layout of labor factor productivity and regional coordinated development. Therefore, this study couples UAs with population distribution, using data from three population censuses and nighttime light data in 2000, 2010, and 2020, to measure the population agglomeration patterns of Chinese UAs using population agglomeration indicators and to explore the influencing factors and spatial stratification heterogeneity characteristics by constructing an econometric model. The results show that: (1) the population agglomeration patterns of Chinese UAs can be classified into four major categories: weakly polycentric, weakly monocentric, strongly monocentric, and strongly polycentric UAs, and China’s UAs are in a low-level stage dominated by weakly polycentric UAs at present. (2) In terms of influencing factors, 15 indicators, such as economic development and social conditions, are important factors affecting the population agglomeration patterns of the four UAs, but their effects vary greatly due to specific patterns. (3) For specific agglomeration models, the total passenger volume has always been the strongest positive influencing factor for weakly polycentric UAs; the industry location entropy index, scale of fiscal expenditure, and total passenger volume in municipal districts are relatively strong positive effects to weakly monocentric UAs, the per capita GDP and urbanization rate are relatively strong positive effects to strongly monocentric UAs, and the urbanization rate is always the strongest positive effect to strongly polycentric UAs. The refined analysis of population migration in Chinese UAs in this study enriches the theoretical results related to population migration in Chinese UAs to a certain extent and provides a feasible basis for the development of new development patterns in Chinese UAs and the formulation of regional population policies in the new stage. Meanwhile, this study divided the polycentric attributes of different UAs, which provide a reference for the theoretical development of polycentric spatial structure of UAs. Full article

The urban–rural relationship has been a critical issue in studies on urban and rural geography. Urban–rural integration development (URI), as an integral part of the urban–rural relationship, needs to be understood under an integrated theoretical framework. Based on the conceptual analysis from productivism to post-productivism, this study constructs a multidimensional framework to understand urban–rural integration, restructuring from five layers that integrate population, space, economic, social, and environmental concerns, and the revised dynamic coordination coupling degree (CCD) model is used to measure the level of URI. Many studies have focused on the connection between URI and factor allocation. However, it is yet to be determined how both fiscal decentralization and factor allocation are linked with URI. This study focuses on this unexplored topic, and the impact mechanism among URI, factor allocation, and Chinese-style fiscal decentralization is investigated by adopting spatial econometric models, for achieving the high-quality development of China’s urban–rural relations. Empirical analysis of China’s three major urban agglomerations reveals that there are promising signs in China’s urban–rural integration development, with an orderly and coordinated structure shaping over the period 2003–2017. The rationality of factor allocation depends heavily on the power comparison between the helping hand and the grabbing hand of local governments under Chinese-style fiscal decentralization. Moderate fiscal decentralization, with a perfect market and social security system, leads to the free flow of factors and promotes urban–rural integration. By contrast, excessive fiscal decentralization causes resource misallocation and hinders urban–rural integration development. In light of our empirical evidence, the coordinated development of small- and medium-sized cities and subcities in urban agglomerations is suggested, it is highly necessary to establish a perfect social and employment security system. In addition, a reasonable space planning system for land use needs to be constructed by China’s governments at all levels. Chinese local governments should pay more attention to rural development in their jurisdiction by stimulating their information advantages under Chinese-style fiscal decentralization. Full article

In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China’s “dual-carbon” goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin’s land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. “Low–Low” was largely constant and concentrated in the upper reaches, whereas “High–High” was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components’ driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance. Full article

In the 21st century, carbon dioxide emissions have led to adverse climate changes; meanwhile, the impact of climate change has imposed challenges worldwide, particularly in developing countries, and China is one of the most affected countries. Assessing the impact of climate change requires handling a large amount of data in the literature comprehensively. In this study, a text-based classification method and literature mapping were used to process the massive literature and map it according to its location. A total of 39,339 Chinese academic studies and 36,584 Chinese master’s and doctoral theses, from 2000 to 2022, with evidence of the impact of climate change were extracted from the China National Knowledge Infrastructure database. Our results show that the literature on climate change impacts has exploded during the last decades. This indicates that increasing attention to the intensified impact of climate change in China has been paid. More importantly, by mapping the geolocation of the literature into spatial grid data, our results show that over 36.09% of the land area shows clear evidence of climate change. Those areas contribute to 89.29% of the gross domestic product (GDP) and comprise 85.06% of the population in China. Furthermore, the studies we collected on the climate change impacts showed a huge spatial heterogeneity. The hotspot areas of research were generally located in developed regions, such as the BTH urban agglomeration and Yangtze River Economic Zone, major agricultural production areas such as Shandong and Henan, and ecologically fragile regions including Yunnan, Xinjiang, and Inner Mongolia. Considering the imbalance spatially of the evidence of climate change can help in a better understanding of the challenges in China imposed by climate change. Appraising the evidence of climate change is of great significance for adapting to climate change, which is closely related to the natural ecosystem services and human health. This study will provide policy implications for coping with climatic events and guide future research. Full article

To investigate the spatiotemporal patterns of fine particulate matter (PM_2.5) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM_2.5 concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM_2.5 concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m³/year, respectively. PM_2.5 concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m³ in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM_2.5 concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM_2.5 concentrations. RH has a positive correlation with PM_2.5 concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM_2.5 changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM_2.5 changes. Among the six urban agglomerations, PM_2.5 concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM_2.5 concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures. Full article

Tourism development consumes ecological resources to varying extents while bringing economic benefits; tourism eco-efficiency (TEE) assessment has thus become an area of major focus in destination sustainability research. This paper intends to examine the spatiotemporal characteristics and driving factors of eco-efficiency changes in 36 tourist cities on the Chinese mainland from 2010 to 2019, using a super-slacks-based measure (SBM) model, the data envelopment analysis (DEA)–Malmquist index, spatial correlation, and regression analysis. In contrast to the previous work, this work explores TEE among major tourist cities in China by considering the undesirable outputs of carbon emissions and sewage. The results show that (1) the TEE of most cities during the study period was low but increasing; there were significant spatial differences among different cities, and the eco-efficiency of the same city fluctuated over time. (2) The TEE was globally uncorrelated, but low-eco-efficiency areas were adjacent to each other and formed agglomerates, enhancing the negative spillover effect. (3) Despite fluctuations, the Malmquist indices exhibited positive trends, which resulted from the technical progress index rather than the technical efficiency index. (4) Socioeconomic development significantly promoted TEE. This research reveals the evolutionary law of TEE on the urban scale and explores the impact of social and economic development on TEE, which can provide a reference for policymaking and enrich research on destination sustainability. Full article

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west. Full article

The spatial pattern and geographical diffusion of Chinese traditional food culture are important manifestations of population migration and cultural chain remodeling. Taking the national roast duck stores and Beijing Quanjude and Bianyifang brand chain roast duck stores as the research objects, the spatial distribution characteristics and geographic diffusion patterns of roast duck stores, and the spatial association characteristics of the chain stores are analyzed by using spatial analysis methods and mathematical statistics. The results of the study showed that: (1) The roast duck stores in the mainland show an overall northeast-southwest direction, and the spatial distribution is extremely uneven. The eastern coast of China shows a high-value continuous distribution, from the Bohai Bay Economic Circle and the Yangtze River Delta Economic Circle, gradually radiating westward to the middle and showing the clustering characteristics of “point + surface”. (2) Using the point cluster analysis method, the diffusion pattern of roast duck stores in the three major economic zones of China is explored, and roast duck stores in the western region show the characteristics of contact diffusion combined with hierarchical diffusion. Contact diffusion is the main diffusion mode of roast duck stores in the east. The central region shows the diffusion characteristics of contact diffusion combined with hierarchical diffusion. Overall, the roast duck stores in mainland China show a composite diffusion pattern. (3) Quanjude and Bianyifang stores have spatial agglomeration characteristics, Quanjude chain stores have a slightly stronger central pointing, while Bianyifang roast duck chain stores have slightly wider spatial diffusion. Both brands significantly show spatial orientation close to transportation facilities and high consumption markets. The street population has a slightly weaker influence on the spatial distribution of the two brands. (4) Through the multivariate spatial analysis method, it is found that the spatial correlation of mutual attraction between Quanjude and Bianyifang roast duck chain stores is presented, but there are differences in the formation mechanism and weak asymmetry in the attraction intensity, which is related to the consumer population and corporate positioning of Quanjude and Bianyifang. With the advent of the big data era, it is possible to obtain and use big data analysis methods to reshape the deep information under the surface logic. Attention should be paid to the location choice of traditional restaurant chains in the new era, to explore the possibilities of enterprise development, and to improve the efficiency of urban space. Full article