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This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With its rapid economic development, China has had to confront the serious issues of high energy consumption and air pollution. Natural ventilation is regarded as an effective method to reduce building energy consumption, but it is largely influenced by indoor and outdoor air pollution. However, most of the previous studies estimating natural ventilation potential (NVP) in China do not consider air pollution. This research estimated the NVP for residential buildings in major cities from four climate regions in China (Guangzhou, Chengdu, Shanghai, Beijing, and Shenyang) while considering the combined effect of indoor and outdoor air pollution. We compared the yearly NVP in three different scenarios, namely without considering air pollution, only considering outdoor air pollution, and considering both outdoor and indoor air pollution. The results show that Guangzhou had the highest yearly NVP, followed by Shanghai, Beijing, Shenyang, and Chengdu. The impact of air pollution could reduce the annual NVP in China by 78–95%. In addition, the main factors causing a low NVP differed between the four cities. The key factors for Chengdu and Guangzhou were natural ventilation flow rate and indoor air pollution, respectively. Beijing and Shenyang were mostly influenced by outdoor air pollution. Shanghai had two main factors with similar influence degrees, namely outdoor air pollution and indoor air pollution. The findings of this study will guide architects and policymakers in better forming natural ventilation strategies. [ABSTRACT FROM AUTHOR]

Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions. Deep learning, as a powerful tool for fitting data, offers an alternative approach. However, most deep learning-based ozone-prediction models only take into account temporality and have limited capacity. Existing spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU). STAGRU uses a double attention mechanism, which includes temporal and spatial attention layers. It takes historical sequences from a target monitoring station and its neighboring stations as input to capture temporal and spatial information, respectively. This approach enables the achievement of more accurate results. The novel model was evaluated by comparing it to ozone observations in five major cities, Nanjing, Chengdu, Beijing, Guangzhou and Wuhan. All of these cities experience severe ozone pollution. The comparison involved Seq2Seq models, Seq2Seq+Attention models and our models. The experimental results show that our algorithm performs 14% better than Seq2Seq models and 4% better than Seq2Seq+Attention models. We also discuss the interpretability of our method, which reveals that temporality involves short-term dependency and long-term periodicity, while spatiality is mainly reflected in the transportation of ozone with the wind. This study emphasizes the significant impact of transportation on the implementation of ozone-pollution-control measures by the Chinese government. [ABSTRACT FROM AUTHOR]

Recent years have witnessed amount of studies on characteristics of public transport network and passengers' flows, however, most of them are discussed independently. An important criterion for evaluating public transport network is whether it could satisfy travel demand well. Therefore, in this paper, we study the interaction between public transport network and passengers' flow. Firstly, a layered network model is constructed for depicting public transport network, passengers' Origin–Destination (OD) flows and transfer flows. Based on that, we propose layer operations to analyze interaction between them and to evaluate suitability between traffic supply (physical infrastructure) and travel demand (passengers' OD trips and transfers). The proposed model is validated against empirical data from Chengdu and Qingdao, two metropolis cities in China. Results reveal the two cities have significant difference in suitability between travel demand and traffic supply. Although in contrast with Chengdu, Qingdao with advantages on transporting passengers by more straight lines and non-walking transfers, the average transfer flows and OD flows on stations in Qingdao are much heavier. The study demonstrates the importance of layer operations on exploring the interaction study of public transport network and traffic dynamics. • Public transport network, people's OD and transfer are studied by layered network. • Layer operations are proposed to explore the interaction between layers. • GPS and IC card data from Chengdu and Qingdao are used to validate our methods. • Layer operation is useful to study suitability between traffic supply and demand. [ABSTRACT FROM AUTHOR]

The purpose of this study is to reveal the characteristics and driving factors of cropland change in Sichuan Province. This study summarizes the current status of coordination among croplands, ecosystem protection, and urban expansion in the region and provides suggestions for the rational use and conservation of arable land resources. The research methods include ArcGis spatial analysis and the PLUS model. The results show that: (1) The cropland area in Sichuan Province has been continuously decreasing over the past 21 years, but arable land protection policies have effectively mitigated this situation. (2) With the exception of Chengdu, the reduction of arable land in each city in Sichuan province has gradually balanced out, safeguarding the development of major and lagging cities. (3) "Dual suitability" refers to the conflict between urban development and planting. Moreover, due to the Matthew effect, urban development further promoted the expansion of construction lands, leaving croplands in a weak position in the competition with construction lands. (4) Sichuan Province showed the characteristics of alternating leadership between urbanization of land and population urbanization during the process of urban development occupying cropland. In conclusion, the decreasing trend of croplands in Sichuan Province has gradually improved, and the coordination between cropland, ecosystem, and urban space has been achieved. [ABSTRACT FROM AUTHOR]

Urban traffic flow prediction has received much attention in the past few years, especially after the availability of huge traffic data. In addition, the efficacy of some existing traffic flow techniques heavily depends on some influential external factors (like weather, geographical information, point-of-interests (POIs), or road information). However, exploitable data containing such auxiliary information are extremely limited. One primary reason is that some rising cities lack the resources necessary for collecting such data. Therefore, it is difficult to pick an effective method that can accurately predict the traffic state when the data is limited. Due to that, this paper proposes a framework for achieving accurate predictions based on four data characteristics and the temporal correlation of neighbors without using additional influential factors. Specifically, we first propose a novel Multi-Region Correlation (MRC) method to make regions cooperate and share their traffic flow and patterns. Then, a deep recurrent network is used to capture the neighbors' temporal correlation by integrating their traffic history that flow among regions and their neighbors. When analyzing the traffic data history, we model four data characteristics (seasonality, trend, residual, and cyclic). And lastly, we employ a Multiple Linear Regression Unit (MLRU) to predict the future traffic directly from the neighbors' traffic. We conducted extensive experiments on two real-world datasets collected from two major cities in China, Chengdu and Xi'an. The results demonstrate that the proposed model can achieve superior performance compared to many existing models in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The concept of transit deserts stems from the concept of food deserts. There is substantial research on transit deserts in developed countries. However, there is no known research that has studied this subject in Chinese cities. Using open-source data, this paper identified transit desert areas in four major Chinese cities (Beijing, Shanghai, Wuhan, Chengdu). The results show that: (1) In these four cities, the transit desert areas are mainly concentrated in city centers and hardly occur in any suburban areas, which is very different from the cases in the US. (2) Shanghai has the largest transit-dependent population living in transit deserts, followed by Beijing, Chengdu, and Wuhan. Chengdu has the smallest transit desert areas, followed by Shanghai, Wuhan, and Beijing. (3) An oversized transit-dependent population and incomplete transit systems in these cities might contribute to the transit deserts' occurrences. (4) Different distribution of population density, traveling preference, and transportation investment policy in Chinese and American cities might contribute to the different findings. By examining transit desert problems in major Chinese cities, this study brought people's attention to the gap between transit demand and supply in China. [ABSTRACT FROM AUTHOR]

Particulate matter (PM 2.5) concentrations in China have decreased significantly in recent years, but surface ozone (O 3) concentrations showed upward trends at more than 71 % of air quality monitoring stations from 2015 to 2021. To reveal the causes of O 3 increases, O 3 production sensitivity is accurately diagnosed by deriving regional threshold values of the satellite tropospheric formaldehyde-to-NO 2 ratio (HCHO/NO2), and O 3 responses to precursor changes are evaluated by tracking volatile organic compounds (VOCs) and NO x with satellite HCHO and NO 2. Results showed that the HCHO/NO2 ranges of transition from VOC-limited to NO x -limited regimes apparently vary among Chinese regions. VOC-limited regimes are found widely over megacity clusters (North China Plain, Yangtze River Delta and Pearl River Delta) and concentrated in developed cities (such as Chengdu, Chongqing, Xi'an and Wuhan). NO x -limited regimes dominate most of the remaining areas. From 2013 to 2021, satellite NO 2 and HCHO columns showed an annual decrease of 3.0 % and 0.3 %, respectively, indicating an effective reduction in NO x emissions but a failure to reduce VOC emissions. This finding further shows that O 3 increases in major cities occur because the Clean Air Action Plan only reduces NO x emissions without effective VOC control. Based on the O 3 –NO x –VOC relationship by satellite NO 2 and HCHO in Beijing, Chengdu and Guangzhou, the ozone concentration can be substantially reduced if the reduction ratio of VOCs/NOx is between 2:1 and 4:1. [ABSTRACT FROM AUTHOR]

This study investigates the impact and transmission mechanism of foreign direct investment (FDI) inflows on the economic growth of city clusters. Via theoretical analysis, a spatial Durbin model (SDM) and mediating effect model are applied based on panel data of the three major city clusters in the Yangtze River Economic Belt (YREB) from 1997 to 2018. The results reveal the following: (1) FDI inflows promote the economic growth of city clusters in the YREB, but the degrees of impacts on the three city clusters are different; (2) the spatial effects of FDI inflows differ across city clusters; the effects are negative in city clusters of the Yangtze River Delta and the Yangtze River Middle Reaches, and positive in the Chengdu-Chongqing city cluster; (3) urbanization and industrial structure upgrading are two important mechanisms by which FDI inflows promote the economic growth of city clusters. However, there are differences among the three major city clusters; the intermediary effect of urbanization is more significant in city clusters of Chengdu-Chongqing and the Yangtze River Middle Reaches, while the intermediary effect of industrial structure upgrading is more significant in the Yangtze River Delta and Chengdu-Chongqing city clusters. Policy implications are proposed accordingly. [ABSTRACT FROM AUTHOR]

• Multifractal method is introduced to determine the interaction between PM 2.5 and O 3. • Multi-temporal scaling of the interaction between PM 2.5 and O 3 is studied. • A mulitfractal index is set up to evaluate PM 2.5 -O 3 coordinated control capability. • Time dependence of the mulitfractal index in different cities verifies its validity. • The mulitfractal index can provide guidance for two-pollutant control strategies. Under combined air pollution conditions, the coordinated control of PM 2.5 and O 3 has significant implications for environmental management. To better understand the coordinated control capability of different metropolises, a prime consideration is how to develop a scientific and objective index for quantifying it. In this paper, based on the multifractal detrended cross-correlation analysis (MF-DCCA) method, a novel dynamical index η is developed to quantitatively determine the capability of metropolis to control PM 2.5 and O 3 pollution cooperatively. Furthermore, combing with the sliding window method, the high temporal scale resolution of η values of four metropolises (Beijing, Shanghai, Guangzhou, and Chengdu) in China is conducted in order to determine the temporal evolution of η. Then, the monthly variation of η values in the four metropolises is investigated. The U-shaped curves in the monthly mean η values indicate that PM 2.5 -O 3 coordinated control capability of the four metropolises is best in summer. The further discussions about the relations between index η and meteorological conditions have been made. Next, long-term trends of η series of four metropolises are studied by Mann–Kendall statistical test. Result shows that the trends of the η series of four metropolises are similar from 2015 to 2017. After 2017, the η series of Beijing, Shanghai and Chengdu show an increasing trend, while that of Guangzhou show a decreasing trend. This suggests that PM 2.5 -O 3 coordinated control capability of Guangzhou is gradually strengthened after 2017. This may be caused by the combined effect of targeted strategies and special climatic conditions in Guangzhou. The η index could directly quantify the two-pollutant coordinated control capability of different metropolises based on field observation, rather than rely on any subjective factor. It can provide guidance for policy-making of pollution emission reduction, so as to control the combined air pollution. [ABSTRACT FROM AUTHOR]

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions. [ABSTRACT FROM AUTHOR]