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The fossils of Hamipterus tianshanensis (Wang et al. in Curr Biol 24:1323–1330, 2014) and their eggs have important scientific significance because they can provide unique information about the reproduction, development, and evolution of pterosaurs. The fossils and the rock surrounding them have, however, been weathered, which including powdering and flaking, since they were relocated from Xinjiang to Beijing. The high content of soluble salts is a significant factor in fossil deterioration because the dissolution–recrystallization process can generate tremendous pressure and lead to decreased mechanical strength. This study evaluated the electrokinetic desalination performance for the fossils, and two types of poultices employed including paper pulp from Bioline® and CKS121 (cellulose: kaolin: sand = 1:2:1, w/w). Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), ion chromatography (IC), and other methods were applied to evaluate the desalination effect. The surface salt content reduction by applied direct current (DC) was about 70%, and the inner salt content reduction was about 80%. The experimental results suggest that the electrokinetic method is a promising way to desalinate fossils. Nonetheless, cracks appeared in the surrounding rock crack after electrokinetic desalination, which can be explained by the montmorillonite swelling-induced stresses. Pre-consolidation, especially for electro-chemical method may solve the cracking problem for the clay-rich sandstone desalination. [ABSTRACT FROM AUTHOR]

Nations worldwide are mobilizing to harness the power of Artificial Intelligence (AI) given its massive potential to shape global competitiveness over the coming decades. Using a dataset of 2.2 million AI papers, we study inter-city citations, collaborations, and talent migrations to uncover dependencies between Eastern and Western cities worldwide. Beijing emerges as a clear outlier, as it has been the most impactful city since 2007, the most productive since 2002, and the one housing the largest number of AI scientists since 1995. Our analysis also reveals that Western cities cite each other far more frequently than expected by chance, East–East collaborations are far more common than East–West or West–West collaborations, and migration of AI scientists mostly takes place from one Eastern city to another. We then propose a measure that quantifies each city's role in bridging East and West. Beijing's role surpasses that of all other cities combined, making it the central gateway through which knowledge and talent flow from one side to the other. We also track the center of mass of AI research by weighing each city's geographic location by its impact, productivity, and AI workforce. The center of mass has moved thousands of kilometers eastward over the past three decades, with Beijing's pull increasing each year. These findings highlight the eastward shift in the tides of global AI research, and the growing role of the Chinese capital as a hub connecting researchers across the globe. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Consumer demand and retailing models nowadays are being upgraded more frequently. More and more retailers are switching to the Omni-channel retailing model. Choosing a reasonable location for a front distribution center (FDC) helps control an enterprise's cost and improves its service level. This is especially true in the existence of fierce competition. In this paper, two important and contradictory objectives are proposed for the first time in the FDC location problem: minimizing the distribution costs from the facility and minimizing the fixed cost of the facility's location. For these objectives, a bi-objective programming model is established by considering the factors of a facility's capacity, demand and rent fluctuation. Meanwhile, the FDC location problem has been solved by compromising programming and elite set multi-objective simulated annealing algorithm respectively. Taking the FDC locations set of an e-commerce enterprise in a region of Beijing as an empirical sample, this paper uses the above algorithms to re-plan the FDC locations of the enterprise. This algorithm provides support for retail enterprises by helping find the best FDC location. Based on the empirical results, some comments and future research directions are also proposed. [ABSTRACT FROM AUTHOR]

The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents' commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers' commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples' jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents' travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments. [ABSTRACT FROM AUTHOR]

Free-floating bike sharing (FFBS) attracts increasing research focusing on usage patterns, determining factors, and integrated transportation. However, existing researchers tend to overlook the variation in usage characteristics over various time ranges, particularly the usage pattern at night. This paper is conducted to fill the gap through a series of analysis approaches on FFSB in Beijing. The characteristics of the usage pattern, including time-varying usage and traveling distance distributions, are initially illustrated. Subsequently, the spatial patterns of FFBS are visualized and thoroughly analyzed in different time ranges and origin-destination (O-D) flows. A statistical model evaluating the environmental effects of FFBS trips revealed the source of FFBS usage. In addition to focusing on the nighttime, the usage patterns varying day and night are compared through the analysis. The findings explain the usage pattern variation and the unique pattern at night, providing valuable insight for improving the management of the FFBS system. [ABSTRACT FROM AUTHOR]

Urban energy issues have attracted global attentions due to the key role in controlling climate change. As one of the world's largest metropolises, Beijing has been focusing on improving urban energy performances to achieve green transition and sustainable development during the past decades, for which purpose the measurement and decomposition of energy performance is of vital importance. In this paper, exergy is introduced as one of the indicators under the frame of index decomposition analysis. An hierarchical framework is constructed to reveal the contributions from different level of urban energy system to overall energy performance. Beijing's energy and exergy consumption and intensity are then analyzed and compared from the city and sector level during 2008–2017. Results suggest that Beijing's city and sector-level energy performance has been improving during the last 10 years and technical improvements contribute significantly from both energy and exergy perspectives. Meanwhile, changes from Beijing's industrial structure and energy supply structure display neutral contributions, which probably indicate the underestimation of structural contribution is caused under the IDA framework. [ABSTRACT FROM AUTHOR]

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability. [ABSTRACT FROM AUTHOR]

The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people's lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area's inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH. [ABSTRACT FROM AUTHOR]

With the maturity of big data technology, analyzing residents' travel habits and tracks has become an important research direction in the field of intelligent transportation study. In this paper, based on the subway and bus ride data, a subway-bus double-layer network model was established using complex network theory, taking the optimal traffic efficiency as the goal, the structure of intelligent bus network optimization method is proposed, and an empirical study is conducted on the Beijing bus network. In the empirical study, by adding or deleting bus station in the network, obtain an efficient network structure, the goal of optimal operation efficiency of the bus network was realized, and the theoretical and practical research on solving the problem of transportation line network planning with big data of traffic travel was enriched. [ABSTRACT FROM AUTHOR]

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. [ABSTRACT FROM AUTHOR]

In the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of "how the urban innovation ecosystem evolves", this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects. [ABSTRACT FROM AUTHOR]

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs). [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the effects of interventions to reduce air pollution during two international events on air quality in Beijing and its neighbor cities. Air quality data were gathered from China's Ministry of Environmental Protection, meteorological data from the China Meteorological Administration and economic data from the China Statistical Yearbook. The paper uses fixed-effect panel data models to empirically evaluate air quality improvement in Beijing and other affected cities before, during, and after the 2008 Olympic Games and the 2014 Asia-Pacific Economic Cooperation summit. Results show substantial improvement in air quality in Beijing and neighboring cities during the two events. However, some of the air quality improvement achieved reverted within a year after the games and within a week after the summit. Furthermore, the improvement achieved during the summit completely reverted and air quality deteriorated severely five days after the summit. It is also found that air quality in China, at least in the cities included in this study, gradually improved over the past 15 years or so. The findings suggest that sustainable interventions and incentive-based programs to reduce emissions from industry production and traffic are the key to maintaining the air pollution reduction achieved during the events. [ABSTRACT FROM AUTHOR]

In China, with the rapid development of economy and the advancement of urbanization, the deterioration of urban ecological environment is obvious. In order to obtain the quantitative state of urban ecological security, this paper contributes an integrated and coupled emergy ecological footprint framework and Beijing city was selected to verify methodology effect in this paper. The results demonstrate that: (1) from 2010 to 2019, the change range of EEFB is from 6.37 to 9.29%; meanwhile, EEFI is from 7.95 to 17.72%; EEFE is from 36.36 to 63.71%; EEFP is from 18.45% to 41.65%. Comparing the proportions of four subparts, the energy resource products (EEFE) and emissions (EEFP) are the main factors, far more significant than biological resources (EEFB) and industrial products (EEFI). (2) Compared to all ECCS data, there is a definite growing trend in Beijing city, from 2.68E + 4 hm2/cap in 2010 to 4.72E+4 hm2/cap in 2019, approximately 42.22% growth range. (3) Four sustainable indicators analysis: The changes of EEFT, EBI, and EEF are from 3.1, 2.46, and 1.02 in 2010 to 0.688, 9.29 and 1.14 in 2019, respectively. For EDI, total proportions of fossil land and built-up land are 85.25% to 90.43% of the entire EEF in Beijing city. These results reflect that Beijing city is suffering from a substantial ecological challenge due to remarkable ecological deficit, awfully high emergy ecological footprint intensity, and low cooperation level between ecological system and economic system. To identify key improvement factors, Pearson correlation analysis was conducted in this paper and revealed the most positive and negative elements, which are the unbalanced industrial structure and a large proportion heavy and polluting industries in Beijing city. Finally, based on the pivotal influencing factors, corresponding strategies and measures are proposed to improve and optimize the ecological security in Beijing city. [ABSTRACT FROM AUTHOR]

Since the 18th National Congress of Chinese Communist Party, ecological civilization construction has been incorporated into Five-sphere Integrated Plan, that is, to promote coordinated progress in the economic, political, cultural, social, and eco-environmental fields. The Chinese government has proposed the goal of "carbon peak" and "carbon neutrality". Low-carbon transportation becomes the mainstream. To achieve carbon peak and carbon neutrality in public transport, this paper takes Beijing's public transport sector as a case study, using system dynamics model with relevant policies and the current situation. It calculates and predicts carbon emissions under electric vehicle substitution scenario, clean energy generation, and carbon capture, utilization and storage scenario for the period of 2020–2060. The following main conclusions are obtained: Direct carbon emissions have peaked and started to decline based on the current measures, while indirect carbon emissions have peaked and started to decline under the suggested intervention. The direct carbon emissions peaked at 2.58005 × 106 tons in 2014. The indirect carbon emissions peaked at 1.59027 × 106 in 2025. They all achieved carbon neutrality under the suggested intervention. Electric vehicle substitution and carbon capture, clean energy generation, utilization and storage are the main factors of carbon emission reduction. The outcomes of this study can provide essential information for policy-makers to advance Beijing's future low-carbon development in public transport. The electric vehicle substitution, clean energy generation and carbon capture, utilization and storage provide a new perspective to research carbon emissions in the transport sector. [ABSTRACT FROM AUTHOR]

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. [ABSTRACT FROM AUTHOR]

The undergoing rapid urbanization is bringing dramatic economic development and social improvement for cities, but also a series of problems associated with the urban energy metabolism, which hinders the transition toward a carbon–neutral society. Optimizing the energy metabolic processes has been one of the key solutions to cure the metropolis disease that are caused by the enormous energy throughputs in urban areas. This paper highlights the heterogeneity of components in urban energy metabolic system and evaluates the related impacts on urban energy metabolism based on gravity model. Using the data from Beijing's 17 sectors during 2005 ~ 2018, in combination with the "de-coalification" and "non-capital function dispersal" policies in Beijing, this paper testifies that each sector has different impacts on Beijing's energy metabolism and conducted the scenario analysis based on Beijing's recent supply and demand side control policies. It is found that the supply control policies are losing the effectiveness on Beijing's energy metabolism, and may even cause energy shortage up to 0.09 mtce; the demand-control policies can reduce the total energy consumptions up to 1.44 mtce, and improve the energy metabolic processes in the manufacture and education sectors, but will increase the energy consumption in the transportation and wholesale trade sectors. The method proposed in this paper expands the analytical framework for the optimization of urban energy metabolic system, and the results provide suggestions for the policy making of urban carbon–neutral transition. [ABSTRACT FROM AUTHOR]

Policy decision of curling refers to providing strategy suggestions for curling competition with the help of computers. Existing curling agents have achieved good results in the digital scenarios, but cannot make correct decisions when applied to actual competition and training scenes. In this paper, a strategies decision agent in the real scene has been proposed. The competition situation was acquired by a Situation-Aware Network and mapped by a Digital Extraction module. We designed Curling MCTS to explore the best strategy in continuous space. The effectiveness of our framework has been verified by experiments and evaluated by China's wheelchair curling team at China Disabled Sports Management Center. With the help of our system, China's wheelchair curling team trained effectively and won the championship in the XIII Paralympic Winter Games (2022, Beijing). In addition, a new curling target detection dataset was provided. [ABSTRACT FROM AUTHOR]

Beijing is a typical energy-imported city, and the study of energy consumption factors in Beijing is important for the sustainable development of Beijing. There are many influencing factors of energy consumption in Beijing, and the influence degree of each factor differs in different development periods. By constructing the IPAT model of energy consumption change in Beijing, this paper breaks down the factors that cause the change in energy consumption in Beijing into population, affluence, and technological progress. The results show that Beijing is a highly energy-consuming city and the change in energy consumption in Beijing is the superposition of multiple effects. The energy consumption change caused by population growth in Beijing continues to increase through data from 2006 to 2020, but begins to show a downward trend, with a maximum change in energy consumption of 19.8 million tons of standard coal. The largest change in energy consumption is caused by per capita GDP growth, which reached 50.3 million tons of standard coal in 2018–2019, and the effect caused by affluence is greater than the impact of population changes. Technological advances always reduce energy consumption and are increasingly effective. To reduce energy consumption and improve environmental quality, the following two things need to be done: first, to continuously control the population of Beijing, and second, to enhance the development and introduction of innovative technologies for energy conservation. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A methodology is proposed to introduce knowledge graphs into the study of the Chinese cultural field for use in a newly designed, complete application. At present, the combination of culture and information technology has become a trend. Among various technologies, knowledge graphs are a very promising option. The contributions of this paper are as follows: it supplies for the first time a knowledge graph in the cultural field of the ancient capital of Beijing, establishes a domain knowledge base, and develops a platform for visual analysis and interactive question and answer. In this process, a framework for applying knowledge graphs to research in the cultural field is summarized, providing ideas for research in the cultural field. [ABSTRACT FROM AUTHOR]

The 2022 Winter Olympics were held in the three competition zones of Beijing, Yanqing and Zhangjiakou, China. The venues of this Winter Olympics were scattered and the terrain was complex. Moreover, the medical resources of Hebei and Beijing were relatively unbalanced. In the medical security of major events, the connection between first aid and in-hospital processes is of the utmost importance to rescue quality. 5th generation mobile network (5G) applications in medical scenarios are on the rise. It would be of great relevance to fully use 5G's low-latency and high-speed features to share the process information of patients, ambulance personnel, and the destination hospital's rescue team at emergency scenes and in transportation, improving rescue efficiency. This paper proposes a system scheme of cross-institutional emergency health information sharing based on 5G and augmented reality wearable devices. It also integrates the construction method of monitoring and other sign data sharing, in addition to testing the proposed scheme's service quality in 5G environments. In the deployment area of the 5G emergency medical rescue information sharing scheme for the Beijing Winter Olympic Games, we selected two designated medical support institutions for testing. The test adopted a combination of fixed-point and driving tests to experiment on the service data, voice service, and streaming media indicators. The 5G signal's coverage rate was close to 100%, the standalone connection's success rate was 100%, and the drop rate was 0. The average downlink rate of multiple scenarios was 620mbps, and the average uplink rate of 5G was over 71.8mbps, which is higher than the average 5G level in China. The downlink rate was more than 20 times larger than the 4th generation mobile network (4G) rate. This study's proposed scheme demonstrates the importance of 5G applications in emergency response and support, in addition to providing a suitable scheme for the integration of 5G networks in the medical scene. [ABSTRACT FROM AUTHOR]

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a 'preprocessing-encoder-decoder' hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness. [ABSTRACT FROM AUTHOR]

Craquelure is the most common defect on ancient polychrome paintings, which may deteriorate further to paint loss. Previous image processing methods, which can accurately recognize paint loss, have limited precision and efficiency in segmenting craquelure. This paper proposes a semantic segmentation method, Res-UNet, for the recognition of craquelure and paint loss in the Palace Museum, Beijing. The residual structure of ResNet-50 enables the avoidance of network degradation, and image features can be fully extracted. Using the unique skip connection module of U-Net, features of different levels are fused to improve segmentation accuracy and provide smoother craquelure edges. Three loss functions are combined to accelerate stable convergence. The model was tested on a newly built dataset based on 600 images. Experimental results supported by statistical tests show that Res-UNet is a capable method of craquelure recognition, with an accuracy rate of 98.19%, and F1-score of 93.42%. Hence, the proposed hybrid approach is a promising tool to support the preservation and restoration of valuable traditional Chinese polychrome architectural paintings. [ABSTRACT FROM AUTHOR]

The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. [ABSTRACT FROM AUTHOR]

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. [ABSTRACT FROM AUTHOR]

Based on the encouragement and increasingly recycling demands of the Chinese governments, online recycling platforms based on B2C, such as loving recycling app, waste recycling alliance app, have emerged as the times require. As an indispensable part of online recycling, recycling app evaluation plays a vital role in user acceptance of the innovative recycling way. As we all know, app evaluation is a typical multiple criteria decision making (MCDM) problem involving many complicated criteria. This paper aims to propose an integrated MCDM method to solve this issue under a probabilistic linguistic context. Firstly, a special evaluation criteria system is constructed for measuring apps' performance, which includes five main criteria namely technical feature, safety, interface design, basic requirement, service quality, as well as 12 sub-criteria. Then, we integrated the best-worst method (BWM) and entropy method with the probabilistic linguistic term to determine the subjective and objective weights. And then the comprehensive weights are calculated by multiplicative integration method. Afterwards, the MULTIMOORA method with the Borda rule, is used to rank alternatives and identify the optimal recycling app. Finally, the assessment of the four recycling apps' performance in Beijing is presented to illustrate the validity and rationality of the proposed approach in practical applications. [ABSTRACT FROM AUTHOR]

Finding students at high risk of poor academic performance as early as possible plays an important role in improving education quality. To do so, most existing studies have used the traditional machine learning algorithms to predict students' achievement based on their behavior data, from which behavior features are extracted manually thanks to expert experience and knowledge. However, owing to an increase in the varieties and overall volume of behavioral data, it has become more and more challenging to identify high-quality handcrafted features. In this paper, we propose an end-to-end deep learning model that automatically extracts features from students' multi-source heterogeneous behavior data to predict academic performance. The key innovation of this model is that it uses long short-term memory networks to capture inherent time-series features for each type of behavior, and it takes two-dimensional convolutional networks to extract correlation features among different behaviors. We conducted experiments with four types of daily behavior data from students of the university in Beijing. The experimental results demonstrate that the proposed deep model method outperforms several machine learning algorithms. [ABSTRACT FROM AUTHOR]

Microvibration induced by natural disturbance and human activities has an adverse effect on the operation of the large-scale and ultraprecise facilities in the world. Under such circumstances, a passive vibration control method is generally deployed for such vibration-sensitive facilities, taking the High Energy Photo Source (HEPS) in Beijing as an example, a 3 m-thick mass concrete layer forming a ring foundation was cast at the facility, where a 1 m-thick reinforced concrete slab (RC slab) lies. Since microvibration control plays a crucial role in the operation of such large-scale scientific and ultraprecise facilities and few studies have been reported for large-scale concrete layer as antimicrovibration devices, this paper presents four field tests in Beijing, China, to evaluate the vibration control capacity of a mass concrete layer. Based on a large number of field tests, the effect of applying the concrete layer is discussed, and a reference is provided for the construction of similar facilities. The vibration signals, generated by shock excitation and ambient excitation, are measured through a highly sensitive and high-accuracy vibration acquisition system. It is concluded that the existence of the 1 m-thick RC slab has little influence on the microvibration signal frequency distribution in the vertical direction and that the signals from the concrete layer and subsoil differ by approximately 10 Hz in the vertical direction while differing by approximately 5 Hz in the horizontal direction. The microvibration control ability of the concrete layer is favorable in a higher frequency band over 20 ~ 30 Hz and more than 50% attenuation can be gained through the concrete layer; however, the microvibration control ability is not significant below 20 ~ 30 Hz. The vibration levels across different heights of the concrete layer section are the same. To prevent adverse vibration disturbance below 20 ~ 30 Hz, it is suggested that the traffic and road surface conditions should be taken into consideration when choosing the construction location. In addition, a long-term monitoring shows that 75% vibration energy at the site is firmly related to the construction activities which are approximately 1.4 km from the site. [ABSTRACT FROM AUTHOR]

Increasing traffic congestion is a major obstacle to the development of cities. The prediction of traffic flow is very important to city planning and dredging. A good model of flow is able to accurately predict future flow by learning historical flow data. Traffic flow is usually affected by macro and micro factors. At the macro level, the whole city can be divided into different subregions according to the similarity in the traffic flow patterns. At the micro-level, there is a temporal and spatial correlation between the traffic flow of different road sections at di fferent times. In this paper, we propose a multi-mode traffic flow prediction method with Clustering based Attention Convolution LSTM (CACLSTM) to model spatial-temporal data of traffic flow. The framework includes three modules: a convolution LSTM encoding-decoding layer which is used to predict the traffic flow of the next time slice by encoding the historical traffic information, a clustering based attention layer which is able to extract different temporal features by clustering based attention, and an additional factors layer which can integrate weather, wind speed, holidays and other factors to improve the prediction accuracy. The experimental results on Beijing taxis data show that the CACLSTM method performs more effective than the six well-known compared methods. [ABSTRACT FROM AUTHOR]

Land subsidence induced by groundwater level decline has spatiotemporal variations. Taking the Interferometric Synthetic Aperture Radar (InSAR) results and the groundwater subsidence data acquired by the monitoring stations as the source material, this paper aims to reveal the spatiotemporal heterogeneity of groundwater-land subsidence in Beijing plain by using the Wind Rose Map (WRM) method and the Change Point Analysis (CPA) method. The WRM results show that the amount and variation in subsidence differs in different directions. This method detected the formation of new subsidence centers and the slowdown of land subsidence in 2008. The CPA results show that obvious changes are detected in subsidence development at the Wangsiying (WSY), Tianzhu (TZ) and Wangjing (WJ) stations. However, there is a relatively stable trend of groundwater decline and land subsidence at the Tianzhu (TZ) station. The stages of land subsidence development show a significant response to groundwater. Moreover, changes in land subsidence also show delayed response behind the changes in groundwater level. The time-lag could be affected by the variation in amplitude of the groundwater level. [ABSTRACT FROM AUTHOR]

The essence of megacity ecosystem problems is the unbalanced relationship between humans and their living environment. The most notable features of this imbalance are the environmental quality decline and resource shortages, which cause human survival crises. This paper combines the drivers, pressures, state, impact, and response (DPSIR) framework to explore a new system dynamics (SD) model to achieve megacity ecosystems' sustainability. First, indicator selection is conducted using the DPSIR framework model. Second, causal loop diagrams and stock-flow charts of systems are generated with the SD model to simulate the change tendency inside the megacity ecosystem. Finally, to validate the applicability of the proposed method, we select the urban ecosystem of Beijing as the case study. This study utilizes the proposed research framework to analyze the effect of the main driving factors among five development strategies and current trends. The results show that the combination of population, environmental protection policies and technical means had the greatest sustainability potential. This study reveals a dynamic relationship between the main influencing factors within megacity ecosystems and offers suggestions to the sustainability of the environment, resources and social economy of megacities. [ABSTRACT FROM AUTHOR]

With the development of industrial civilisation, urban environmental problems are increasingly severe. However, Chinese urban residents' actual adoption level of pro-environment behaviours (PEBs) is still relatively low. In order to improve the residents' PEBs adoption, this paper combined the Theory of Planned Behaviour, the Norm Activation Model and the Attitude-Behaviour-Context theory into a holistic framework to explore the determinants of private-sphere PEBs. In this model, we assess the determinants of private-sphere PEBs from perspectives of intrapersonal and contextual factors and treat private-sphere PEBs as three different types of behaviours, namely environmental purchasing behaviours, environmental transport behaviours and conservation behaviours, rather than aggregated and undifferentiated construct or a single item behaviour. The model was tested empirically by primary data collected from 1,054 citizens in Beijing and Shanghai in China. The results indicate that intrapersonal factors, such as pro-environmental intentions and personal norms, are significant positive factors of private-sphere PEBs adoption; contextual constraints have negative direct effects on the three types of private-sphere PEBs adoption; contextual constraints act as a moderator between pro-environmental intentions and private-sphere PEBs, while the moderating effects are sensitive to the types of private-sphere PEBs. The findings contribute to a deeper understanding of residents' adoption of private-sphere PEBs and making more targeted policies accordingly. [ABSTRACT FROM AUTHOR]

Improving carbon emission efficiency (CEE) would promote the development of the green and low-carbon economy in the Beijing-Tianjin-Hebei (BTH) region, China. This paper uses the EBM model of unexpected output to measure the city-level CEE of the BTH region from 2007 to 2016. The spatial distribution characteristics and evolution law of CEE are analyzed with respect to overall and local aspects, and the spatial quantile regression model is used to verify the influencing factors of CEE. The main findings are as follows: (1) The carbon emission in the BTH region is considered to be of medium efficiency, and there are eight cities within the region at middle- and high-efficiency levels. The overall efficiency values show a downward trend. Beijing, Cangzhou, Baoding have high-CEE values, whereas Handan, Tangshan, and Zhangjiakou have low-CEE values. (2) The CEE values for BTH show significant spatial agglomeration characteristics at both the global and local levels. The "H–H" agglomeration areas are primarily distributed in the central region, and the "L-L" agglomeration areas are chiefly distributed in the southern and northern regions. The spatial pattern change is generally stable. (3) The selected factors, URB, PGDP, DS, ISG, FDI, and TEL, have different regression coefficients on CEE at different quantiles. [ABSTRACT FROM AUTHOR]

Using the calculation method of the United Nations World Tourism Organization (UNWTO), this paper measured the carbon emissions of tourism traffic and their evolution process in Beijing from 2005 to 2017. The Tapio model and Logarithm Mean Divisia Index (LMDI) approach were used to discuss the decoupling relationship between economic development and the change of tourism traffic carbon emissions and the influencing factors. There are six major indicators in our analysis, including (1) tourists scale, (2) per capita tourism consumption level, (3) contribution rate of tourism industry to Gross Domestic Product (GDP), (4) passenger traffic volume per unit GDP, (5) energy consumption per unit passenger traffic volume, and (6) energy structure. The results showed that: the main positive drivers are tourists scale, per capita tourism consumption level and energy consumption per unit of passenger traffic volume. In addition, per unit GDP passenger traffic volume is an effective factor to restrain the growth of carbon emissions. And the economic development of Beijing is developing in step with the change of carbon emissions in Beijing's tourism traffic. The research results have important theoretical and practical significance for Beijing to formulate emission reduction policies and develop low-carbon economy. [ABSTRACT FROM AUTHOR]

Ecological welfare performance is a measure of the relationship between input ecological resources and output welfare, and it reflects the sustainable development status of a region. In this paper, the environmental resource consumption and human development index of four areas (covering thirty provinces, autonomous regions, and municipalities) in China from 2019 to 2020 were predicted using the GM (1,1) model. The status and trend of sustainable development were evaluated using the Malmquist index and its decomposition index (catch-up effect and frontier e impact of movement). The corresponding regions' environmental resource consumption data and human development index from 2011–2018 were used for the analyses. The following results were obtained: (1) In terms of the catch-up effect. However, Beijing shows a greater technical efficiency than other regions with values less than 1, which is a drag on sustainable development. (2) Regarding the frontier movement effect, all regions' technical level has improved, and the frontier exhibits an upward trend. (3) According to the Malmquist total factor productivity index, the sustainability status has been continuously deteriorating in all regions, except for improvements in a few areas. (4) In terms of planning periods, the frontier movement effect positively contributed to sustainable development in all regions during the 12th and 13th Five-Year Plan periods; in other words, technological innovation promotes sustainable development. Except in Beijing, the catch-up effect had negative contributions. By combining the gray prediction model with the Malmquist index, the authors provide more in-depth insight into the sustainable development of four regions in China from ecological welfare performance. [ABSTRACT FROM AUTHOR]

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than 2.5 μ m ( P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method "hybrid CNN-LSTM multivariate" enables more accurate predictions than all the listed traditional models and performs better in predictive performance. [ABSTRACT FROM AUTHOR]

The generalized regression neural network (GRNN) is one of the most effective neural models in the field of information prediction. In this paper, we take advantage of the hesitant fuzzy set (HFS) and develop the GRNN under the hesitant fuzzy environment. However, the performance of the GRNN only depends on the smooth factor after determining the input and output variables. To determine the most appropriate smooth factor of network, we propose an improved fruit fly optimization algorithm with fast decreasing step (FDS-FOA) based on a dynamic step size function. A specific implementation process of the optimized GRNN based on FDS-FOA is also presented. Moreover, we apply the proposed model to the prediction of air quality index (AQI) in Beijing. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized GRNN based on FDS-FOA. [ABSTRACT FROM AUTHOR]

Short-term prediction of origin–destination (OD) flow is a primary but complex assignment to urban rail companies, which is the basis of intelligent and real-time urban rail transit (URT) operation and management. The short-term prediction of URT OD flow has three special characteristics: data lag, data dimensionality, and data malconformation, distinguishing it from other short-term prediction tasks. It is essential to propose a novel prediction algorithm that considers the special characteristics of the URT OD flow. For this purpose, based on deep learning methods and multi-source big data, a modified spatial–temporal long short-term memory (ST-LSTM) model is established. The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow/outflow; (4) a fusion module combines historical spatial–temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial–temporal correlations and exceed the rest benchmarking methods. [ABSTRACT FROM AUTHOR]

Digital museums play a crucial role in facilitating users' access to and exploration of digital cultural heritage resources. However, exploring the factors influencing user engagement with these digital museums from a user experience perspective remains essential. This study evaluates the factors driving user continuous behavioral intention towards the digital museum of Beijing's central axis, integrating the new task-technology fit (TTF) and the new unified theory of acceptance and use of technology (UTAUT) models, and introducing perceived enjoyment, design aesthetics, and perceived cultural value as additional variables. Analyzing survey data (n = 377) utilizing structural equation modeling (SEM), the study identifies the following key findings: (1) the task and technology characteristics of digital museums significantly impact the TTF; (2) performance expectancy, effort expectancy, design aesthetics, perceived enjoyment, and perceived cultural value all positively impact user continuous behavioral intention; (3) the technological characteristics of digital museums were observed to positively impact users' effort expectancy; but (4) the TTF and social influence did not have no significant impact the user continuous behavioral intention. These findings offer valuable insights into the factors driving users' continuous behavioral intention to use digital museums of cultural heritage, offering practical guidance for future development and optimization of these digital museums, and highlighting specific implications and suggestions for enhancing the user experience. [ABSTRACT FROM AUTHOR]

This research tries to establish the emissions diffusion model of high-speed motor vehicles on the Capital Ring Expressway (CRE) around Beijing, China, applying the MOVES and the CALPUFF models. Data required for this study have been collected from different sources. The results show that small buses and large trucks have the highest pollutant emissions on the CRE. The pollutants like CO and HC mainly comes from gasoline vehicles, while NOx, PM10 and PM2.5 comes from diesel vehicles. The average concentrations of the diffusion of CO, NOx, CH4, PM10, and PM2.5 along with the CRE are 6.5 mg/m3, 2.3 mg/m3, 0.27 mg/m3, 0.055 mg/m3 and 0.056 mg/m3 respectively. The results of this study have practical significance and influence on the emission reduction and concentration control strategies under different circumstances, and put forward suggestions on changing transportation modes, strengthening supervision and so on. It has produced a number of recommendations for the policy makers including (1) active promotion of new energy vehicles on expressways, (2) strategic change of the mode of passenger and freight transportation on expressways, (3) strengthen traffic management and supervision of motor vehicles on expressways, and (4) strengthen vegetation protection in rural and ecologically sound areas along the route. [ABSTRACT FROM AUTHOR]

The use of Internet data to carry out geographic study has become an academic hotspot in recent years. We referred to the "Zhongke Beauty Index" and crawled 530 thousand POIs (POI: Point of interest) to carry out regional construction evaluation in Beijing-Tianjin-Hebei region. The results showed that: (1) Geographic big data provided detailed information for fine-grained regional research, and the POI evolution tree model revealed the shortcomings of regional development. (2) The Beijing-Tianjin-Hebei comprehensive beauty index in 2019 was 0.21, and Beijing and Tianjin are far ahead of Hebei in terms of ecology and culture. Specifically, the POI types of eco-environment construction of research area were single; the industrial development construction presented a trend of multi-center gathering; the social harmony construction has formed a gathering circle in Beijing; the eastern and central regions were the dominant regions in terms of institutional improvement construction; and the cultural inheritance construction was the shortest board in research area. (3) The evaluation ranking was sensitive to the population and the size of the area in Beijing-Tianjin-Hebei region. When evaluating, the uncertainty caused by the characteristics of indicators and POIs should be fully considered. This article indicated the shortcomings in five dimensions of Beijing-Tianjin-Hebei region and can provide reference for the evaluation of regional construction. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing the provincial CO2 emission efficiency of China using data on energy consumption and carbon emissions from 2013 to 2019. Specifically, the game cross-equilibrium efficient frontier data envelopment analysis (DEA) approach, consisting of a game cross-efficiency model and a DEA fixed-sum output model, was proposed. In this method, CO2 is considered a fixed-sum undesirable output, and the decision-making units (DMUs) affect each other to achieve the efficiency result. The results show that: (1) CO2 emissions efficiency varied significantly between provinces during the study period, with Beijing performing the best and Ningxia the worst. (2) The overall national average CO2 emission efficiency showed a slow increasing trend over time. The eastern region performed best, followed by the northeastern, central, and western regions. (3) By comparing the existing DEA fixed-sum output methods, the CO2 efficiency obtained by our method, considering the DMU game relationship, is more acceptable. Based on these results, several policies and improvements were recommended to the provinces, such as reducing energy utilization in high-carbon emission industries. [ABSTRACT FROM AUTHOR]

Environmental degradation and climate change are two important issues that threaten human survival and sustainable social development. Based on the characteristics of the same source of sulfur dioxide and greenhouse gases, we analyze the collaborative governance mechanism of carbon emission trading on sulfur dioxide. The results show that carbon emission reduction trading may not necessarily deliver sulfur dioxide reduction co-benefits. Then, we conduct an empirical using data of 30 regions in China from 1998 to 2017. The results show that the co-benefits of carbon emission trading in terms of the reduction of sulfur dioxide are regionally heterogeneous. The carbon emission trading policies implemented in Beijing, Chongqing, Hubei, and Guangdong have realized collaborative governance on sulfur dioxide. However, the carbon emission trading policies implemented in Tianjin and Shanghai have not realized collaborative governance on sulfur dioxide. Moreover, Chongqing has the highest average annual sulfur dioxide reduction co-benefits, followed by Guangdong. The gap of sulfur dioxide reduction co-benefits between Beijing and Hubei's synergistic emission reduction effect is small. Moreover, the sulfur dioxide reduction co-benefits of carbon emission trading are mainly achieved by reducing fossil energy consumption. Meanwhile, the carbon emission trading implemented in Beijing also delivers sulfur dioxide reduction co-benefits by improving pollution reduction technology. [ABSTRACT FROM AUTHOR]