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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]

Commerce, as both an environmental and a social factor, is essential to the study of the causes of urban crimes. This paper aims to comprehensively propose research hypotheses based on these two types of commercial factors and optimise statistical tools with which to analyse commerce's impact on the level of theft in Beijing. Combining criminal verdicts, census data, points of interest, and information on nighttime lighting, this paper first applies a hierarchical regression model to verify the effectiveness of using commercial environmental and social factors to explain theft statistics and then constructs a structural equation model to analyse the joint influence of multiple commercial factors on those statistics. This paper finds that Beijing's commerce does not significantly promote theft, verifies the effectiveness of two types of commercial variables and the corresponding Western theories in explaining commerce's impact on theft in Beijing, and provides empirical data for the study of the causes of theft in a non-Western context. [ABSTRACT FROM AUTHOR]

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1 km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1 K (< 2 K), RMSE < 2 K (< 2 K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80 K, 1.09 K, and 0.94 K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values). [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]

As development progresses over time and changes in the environment take place, carbon finance development has become a very interesting research topic for many scholars worldwide. This paper proposes a hybrid MCDM method that combines the distance solutions of the TOPSIS and VIKOR methods. The contribution of this paper combines TOPSIS and VIKOR, incorporating AISM models to obtain the development ranking of Chinese carbon finance pilot cities as related to the Chinese carbon finance development pilot provinces and cities. The conclusion notes that Guangdong and Beijing are the most preferred investment locations for carbon investors. In addition, technological innovation, financial sector development, and an active ETS market can effectively drive the level of local carbon finance. Additionally, the MCDM methodology in this paper provides new ideas in environmental and economic development assessment. [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]

This paper constructs a novel stress measurement system of carbon market from the perspective of trading, emission reduction, and external shocks and simulates the stress indices of national and pilot carbon markets of China with the methods of functional data analysis and criteria importance through intercriteria correlation. It concludes that the overall carbon market stress is in the shape of "W" and still at a high level, with frequent fluctuations and an upward trend. In addition, the stress of Hubei, Beijing, and Shanghai carbon market fluctuates and rises, while the stress of Guangdong carbon market decreases. Moreover, carbon market stress mainly comes from trading and emission reduction. Furthermore, stress fluctuation of Guangdong and Beijing carbon market is more prone to "big waves," indicating that the two markets are sensitive to big events. Finally, the pilot carbon markets are divided into stress-driven and stress-release market and the type of which keeps change in different period. [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 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]

Accurate prediction of short-term passengers' origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time–space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances. [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]

In order to address the problem of air pollution along with the fast development of the economy and an increase of urban population, this paper aims to capture the spatial–temporal correlation in air quality data and improve the prediction accuracy of PM2.5 concentration. Existing models usually neglect the correlation in temporal and spatial dimensions, and the prediction results do not correspond to those in practical applications. We propose a multi-period spatial–temporal graph convolutional network (MST-GCN), in which the time division module fully explores the temporal correlation within the data and builds separate models for different time periods; the spatial–temporal convolution module captures the characteristics of historical data in the time dimension and spatial dimension, in which GCN is used to learn spatial dependencies between stations, and CNN is applied to learn the temporal dependencies of adjacent time points; meanwhile, a spatial–temporal attention mechanism module is introduced to enhance the key information in different dimensions, thereby capturing the spatial–temporal dynamic correlation. Finally, the experiments with real air quality data from Beijing monitoring stations show that the time division method is effective to extract the features in time dimensions; therefore, the model is able to capture the characteristics and dynamic correlation of air quality data in spatial–temporal dimensions. The superiority of the proposed method has been validated over state-of-the-art methods, and the average mean absolute error and root-mean-square error of MST-GCN are 34.37 and 35.55, respectively, which achieves the best performance in the experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region. [ABSTRACT FROM AUTHOR]

In order to deal with severe problems such as environmental pollution and climate change, the Chinese government has proposed the goal of carbon neutrality in 2030 and carbon peak in 2060. Strategic emerging industries have become key areas of high-quality growth of green economy. In order to solve the practical problems of insufficient funds and financing constraints, this paper empirically measures the financing efficiency of strategic emerging industries. Based on the Super Slack-Based Measure model, this paper selects the data analysis of listed companies in Beijing, Tianjin and Hebei from 2011 to 2020. At the same time, this paper systematically combs the index system that affects financing efficiency based on grounded theory. Based on the binary relation and structural level of adjacent matrix and reachable matrix, the explanatory analysis is carried out. On this basis, a systematic GMM model is established to explore the significance of different factors influencing financing efficiency. The research shows that the strategic emerging industry is still in the initial stage, the financing efficiency is not high and the financing output is insufficient. The factors affecting financing efficiency can be divided into 6 dimensions, 20 indicators in total and 5 multipole hierarchical levels. Credit financing, equity financing, financing constraints, technological innovation and government support are the important factors affecting financing efficiency. [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]

In the modern risk society, the general sense of collaborative governance is facing a profound crisis—poor governance efficiency due to the lack of authority. The paper introduces a case from China and finds a new mechanism in COVID-19 prevention and control which is called joint governance. Instead of the relatively equal status among multiple actors, joint governance is characterized by a dominant power forming alliances with other stakeholders. The political advantages that Beijing enjoys make it work. Our study suggests that joint governance is a new perspective of collaborative governance which brings politics back to administrative domains. [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]

The university–industry collaboration is an essential engine of the regional innovation system and can significantly drive regional economic growth. The realization of intellectual collaboration, especially for firms absorbing academic knowledge, is heavily dependent on the transmission of tacit knowledge through face-to-face contact. Though economic geographers have long emphasized the importance of face-to-face contact, its effect is rarely measured directly. This paper contributes to this end by utilizing the mobile signaling data and tries to estimate and explain the marginal effect of face-to-face contact on the co-patenting behavior between firms and universities in Beijing. We conclude that face-to-face contact indeed increases the productivity of university–industry collaboration, specifically in the idea-formation period. In addition, we detect the ambiguity of geographical proximity in explaining university–industry collaboration. For policy consideration, we contend that innovation policy should pay enough attention to reinforcing the university–industry nexus. The removal of interaction barriers between firms and universities is crucial, and it can be achieved either by forming direct contact channels or leveraging the spatial spillover effect of personal interaction. [ABSTRACT FROM AUTHOR]

Focuses on the use of fetal-cell injections for patients with spinal cord injuries and amyotrophic lateral sclerosis in Beijing, China. Historical background of the unconventional treatment for neurological disease; Assessment of the effectiveness of the technique of injecting fetal cells into patients; Claims of Hongyun Huang who developed the treatment concerning the efficacy of the technique in improving the quality of life for people with spinal injuries.

Rainstorm disaster brings serious threat to people's life and property safety. Constructing reasonable rainstorm disaster risk index and drawing rainstorm disaster risk map can help decision-makers to deal with rainstorm disaster effectively and reduce disaster loss. It has important practical significance. This paper, for the first time, proposes a comprehensive risk index for death caused by rainstorm disasters. According to this index, the regional hazard map of Beijing is drawn, so as to directly reflect the damage degree of rainstorm disaster in Beijing. In the process of index construction, the weight is determined by regression coefficient innovatively, and the disadvantage of subjective setting weight is avoided, and the spatial distribution of risk can be more accurately reflected by constructing disaster risk index by using fused grid data. Research shows that the rainstorm death risk index proposed in this paper can well reflect the risk of death caused by rainstorms in various areas of Beijing. Combined with the ArcGIS software, a risk map of death due to rainstorm disasters is drawn. It is found that Fangshan District of Beijing is a major disaster area with the highest risk of death. Finally, the managerial implication included that government administrators should evaluate the risk of rainstorm disasters in a certain area according to the established rainstorm death risk indexes and draw a risk map accordingly. [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]

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing's pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China's carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has generally provided a wide range of application values in various fields. However, interdisciplinary research on blockchain technology and carbon trading companies is currently rare. Based on this, this paper takes the integration of blockchain technology and carbon trading companies as the background and draws on relevant theories such as blockchain technology, carbon trading mechanisms, and major technological changes to study the influencing factors of carbon trading companies applying blockchain technology by combining theoretical deduction with an empirical test. The results show that (1) the number of companies in China's eight carbon trading pilots shows an obvious regional imbalance. Among them, more companies participated in the carbon trading pilot in Shenzhen and Beijing while less in Tianjin and Hubei. Meanwhile, the participating companies in the eight carbon trading pilots are mainly concentrated in eight industries, including electric power, petrochemical, chemical, building materials, iron and steel, nonferrous metals, papermaking, and domestic civil aviation. (2) The willingness of carbon pilot companies to use blockchain technology is stronger; the time for carbon pilot companies to learn and master blockchain technology is shorter, otherwise, the longer. Therefore, priority can be given to applying blockchain technology in carbon pilot industries with strong use willingness. (3) If more companies participate in carbon trading, the easier it is for companies to learn to use blockchain technology in the early stage. Therefore, if the pilot for the application of blockchain technology is carried out in carbon trading companies, the two carbon pilots in Shenzhen and Beijing can be given priority. This paper further enriches the relatively scarce existing interdisciplinary literature on blockchain technology and carbon trading in theory and also provides guidance for the effective application of blockchain technology by carbon trading companies in reality. [ABSTRACT FROM AUTHOR]

In this paper, we empirically study the spatial association network of PM2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing–Tianjin–Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM2.5. The greater the urbanization differences, the more likely the generation of PM2.5 links is, while the opposite is true for differences in geographical distance. [ABSTRACT FROM AUTHOR]

Green finance has become an important reform force to promote the sustainable development of China's economy. Therefore, it has a great significance for in-depth analysis of the advantages and disadvantages of regional green finance development, to further promote it by clarifying and predicting the regional differences and dynamic evolution trends. Based on this, this paper will select the relevant index from 2001 to 2020 to construct China Green Finance Core Network (CGFCN) in different years by using Space-L method at the first, then analyze its network characteristics and spatial evolution pattern in depth, and finally predict the future development trend of CGFCN by link prediction. The research results show that: firstly, the evolution of CGFCN is mainly divided into three stages: rapid development, stable development and optimal development, and the closeness of CGFCN is constantly improving. Besides, two strong relationship networks are gradually forming, that is Beijing-Tianjin region and the Yangtze River Detla region. Secondly, with the development of green finance, the community division has changed. It is mainly divided into four communities, named the Beijing-Tianjin-Hebei leading community, the eastern provincial community, the Yangtze River Delta community and the central and southern joint community. Different communities will have different integration in different periods. Thirdly, the future development direction of green finance network is mainly Beijing-Tianjin-Hebei region and Yangtze River Delta regions, and their outward radiation are mainly shown in the eastern coastal and central regions, which also have strong development potential. In this regard, it is proposed to coordinate development across provinces to speed up the "urban integration" of green finance services; Establish an efficient community development mechanism and promote the interconnection of green finance markets and infrastructure between different regions; Strengthen the resource flow among regions and coordinate the resource competition of green finance. [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]

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]

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]

The COVID-19 lockdown had a positive control effect on urban air quality. However, this effect remains uncertain after the epidemic enters regular management, and furthermore, only limited data are available regarding urban PM2.5 (aerodynamic diameter ≤ 2.5μm) under the impact of the epidemic. We used daily ambient PM2.5 concentration data in Beijing to compare and analyze the changes in urban PM2.5 concentrations before and after the COVID-19 epidemic and to estimate the healthy effects and economic burden associated with PM2.5 before and after the epidemic. The study found that COVID-19 has a significant impact on the urban environmental PM2.5 concentration, which is manifested by the decrease in the PM2.5 concentration in Beijing during the epidemic by 27.8%. Exposure-response models estimated 56.443 (95% CI: 43.084–69.893) thousand people die prematurely in Beijing during the COVID-19 epidemic attributed to long-term PM2.5 exposure, with a 13.3% decrease in the number of premature deaths year-on-year. The total healthy economic losses attributable to PM2.5 in Beijing during the COVID-19 epidemic were 35.76 (95% CI: 28.41–42.44) billion yuan, with a per capita loss of 816.8 yuan. Strict control measures throughout the COVID-19 epidemic had a positive impact on air quality in Beijing, with a decrease in both premature deaths and economic healthy losses attributable to fine particles. This paper helps to enrich and expand the research on the impact of COVID-19 on the urban environment and provides a basis for formulating policies related to air quality improvement in the post-epidemic era. [ABSTRACT FROM AUTHOR]

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Many philosophers have recently been impressed by an argument to the effect that all grounding facts about "derivative entities"—e.g. the facts expressed by the (let us suppose) true sentences 'the fact that Beijing is a concrete entity is grounded in the fact that its parts are concrete' and 'the fact that there are cities is grounded in the fact that p', where 'p' is a suitable sentence couched in the language of particle physics—must themselves be grounded. This argument relies on a principle, Purity, which states that facts about derivative entities are non-fundamental. Purity is questionable. In this paper, I introduce a new argument—the argument from Settledness—for a similar conclusion but which does not rely on Purity. The conclusion of the new argument is that every "thick" grounding fact is grounded, where a grounding fact [F is grounded in G, H, ...] is said to be thick when at least one of F, G, H, ... is a fact—a condition that is automatically satisfied if grounding is factive. After introducing the argument, I compare it with the argument from Purity, and I assess its cogency relative to the relevant accounts of the connections between grounding and fundamentality that are available in the literature. [ABSTRACT FROM AUTHOR]

Long-term exposure to air environments full of suspended particles, especially PM2.5, would damage people's health and life seriously. Nowadays, plenty of air quality monitoring networks have been established in several countries. Timely and accurate prediction of PM2.5 concentration is of great significance. However, current studies on PM2.5 concentration assume that the training set and testing set have the same data distribution, which leads to low accuracy and weak robustness of the model. In this paper, in order to predict accurately and solve the problem that the probability distribution of PM2.5 data may change over time, a feature transfer learning for time series model called adaptive GRU (ADGRU) is proposed. First, the training set data of Beijing PM2.5 concentration is divided into source domain data and target domain data. Then an adaptive bandwidth method is applied in ADGRU to improve its accuracy. Finally, extensive experimental simulations show that ADGRU can learn the time distribution changes of data efficiently, and predict the trend of PM2.5 concentration accurately, which may provide effective reference for people's travel and health. [ABSTRACT FROM AUTHOR]

In order to forecast prices of arbitrary agricultural commodity in different wholesale markets in one city, this paper proposes a mixed model, which combines ARIMA model and PLS regression method based on time and space factors. This mixed model is able to obtain the forecasting results of weekly prices of agricultural commodities in different markets. Meanwhile, this paper sets up variables to measure the price changing trend based on the change of exogenous variables and prices, thus achieves the warning of daily price changes using neural networks. The model is tested with the data of several types of agricultural commodities and error analysis is made. The result shows that the mixed model is more accurate in forecasting agricultural commodity prices than each single model does, and has better accuracy in warning values. The mixed model, to some extent, forecasts the daily price changes of agricultural commodities. [ABSTRACT FROM AUTHOR]

Do environmental risks affect housing prices in the vicinity of industrial plants? Prior studies concentrated on the capitalization effect of infrequent natural disasters instead of industry-related environmental risks caused by production accidents in our everyday lives. In this paper, we first use the 2015 Tianjin explosion in China as an exogenous shock, which led to serious casualties and property damage with nationwide concern, to estimate the effects of environmental risk on housing prices far away from the explosion site. Based on the housing transaction units and chemical plant datasets in Beijing with the difference-in-differences hedonic model, the results indicate that first, housing prices near chemical plants decreased significantly after the explosion and lasted for at least 2 years. Second, the mitigation effects of production and consumption amenities and regional demographic features were examined. The conclusions of this paper provide a policy basis for constantly implementing public sentiment management and safety supervision, balancing industrial and residential land layouts, and improving local amenities and demographics to ensure the stability of the land market. [ABSTRACT FROM AUTHOR]

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]

Beijing, as the capital of China, still has soil pollution problems that cannot be ignored. However, there are few studies on the overall ecological risks of heavy metals in farmland soils in Beijing. This study selected 432 soils and crops heavy metal content data of eight districts in Beijing from academic papers and academic journal papers. In this study, the improved Hakanson method, improved analytic hierarchy process (AHP), and integrated quality impact index (IICQ) comprehensively were used to evaluate the impact of Pb, As, and Cd pollution on the farmland soil environment, and the applicable conditions of these methods were discussed. The results by improved Hakanson method showed that both Pb and As were at the normal ecological risk level, while Cd was the largest contributor to potential ecological risk which accounted for 72.54% of the total risk and is mainly at a moderate ecological risk level. The analysis by improved AHP showed that the average comprehensive index of soil heavy metal pollution in the study area was 0.2317, which was at a light pollution level. The IICQ of soil and agricultural products were between 0 and 1 demonstrating that the soil was clean. In summary, the pollution of heavy metals Pb, As, and Cd in the study area is at a relatively low level, and there is no significant risk to the surrounding environment and human health. IICQ method is suitable for the evaluation of soil heavy metal composition and individual impact and can be more accurately used for the overall ecological evaluation of soil-crop-human health system. [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]

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers. [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]

Ever since China has formally joined the WHO-backed global COVID-19 vaccine initiative known as COVAX, there is a presumed notion that China's vaccine diplomacy will make a significant contribution to the international public good and thus uplift Beijing's role as the rule-maker of international order. To scrutinize this, the paper asks if China succeeded in proliferating its weaponized vaccine policy to obtain maximum diplomatic gains and soft power projection to intensify its international image, geopolitical power, and domestic politico legitimacy. The authors argue that despite its vaccine diplomacy demonstrated the robust governance capacity and responsibility to be a great power. Yet, Beijing's geopolitical influence and international image are significantly overrated and not enough to play a more prominent role in the global power fulcrum/equilibrium. On the contrary, China enjoys a leading position on the domestic political front. Its successful portrayal of China's vaccine provision in the global market and remarkable configuration to leverage a deep-rooted nationalism has fundamentally provided China with a powerful rationale to divert its public's attention from Beijing's earlier inadequate handling of the outbreak. The evaluation of the paper reveals that China's vaccine diplomacy's influence in promoting international image and geopolitics is limited but has successfully stabilized its domestic political environment and enhanced its domestic legitimacy. [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]

Whether logistics agglomeration and pollution reduction can form a perfect combination in space is one of the important breakthroughs for the successful green transformation of the logistics industry in the future. This paper attempts to clarify the nonlinear change law of logistics agglomeration evolution on environmental quality in China and further clarify the interpretation of informatization and legalization factors on macro-regulatory functions. Based on the panel data of 31 provincial-level administrative regions in China from 2010 to 2020, the empirical verification is carried out by means of two-way fixed effects, quantile regression, instrumental variables, and spatial econometrics. The research finds that (1) there is an inverted U-shaped change trend between logistics agglomeration and environmental quality and the low-level logistics agglomeration period has a blocking effect on pollution emission reduction. However, from the starting point of the sample interval, representative regions such as Beijing and Shanghai gradually break through the bottleneck of logistics agglomeration in the form of a pyramid, which has an incentive effect on pollution emission reduction. The above conclusions are supported by robustness tests in six ways, including eliminating extreme data disturbance, eliminating the impact of the COVID-19, controlling the impact of other factors, pollutant sensitivity testing, avoiding the interference of pollution prevention and control, and correcting the endogenous bias of the model. (2) Further analysis of superimposed spatial effects and regulatory effects shows that regional spillover pollution has spatial dependence in both geographical and economic distribution. The effect of logistics agglomeration also produces an inverted U-shaped spatial spillover effect on the environmental quality of adjacent areas. Both informatization and legalization factors help alleviate the pollution increasing effect in the low-level logistics agglomeration stage. Under the regulatory effect of informatization, the arrival of the critical point for logistics agglomeration and emission reduction will be relatively delayed. Under the regulatory effect of legalization, the arrival of the critical point for logistics agglomeration and emission reduction will be relatively accelerated. (3) There is significant regional heterogeneity in the environmental effects of logistics agglomeration. Each region needs to "suit the remedy to the case" in combination with its own situation to break through the threshold of pollution reduction as soon as possible. [ABSTRACT FROM AUTHOR]

The promotion and use of new energy vehicles are important means to reduce carbon emissions. However, its effect is usually affected by the rebound effect. The current research does not consider the impact of the substitution effect of new energy vehicles on traditional fuel vehicles to the carbon emission rebound effect, including its situation in different scenarios. This paper considers the purchase stage, use stage of the new energy vehicles and constructs a comprehensive two-stage almost ideal demand system model. The rebound effect of carbon emissions from 2014 to 2019 and different scenarios from 2020 to 2030 is quantified. The results show that the rebound effect of carbon emissions without car purchase subsidies is 4%–9% less than that with car purchase subsidies in the corresponding year. In the prediction results of the rebound effect under different future scenarios, the values of partial rebound effects are more than 80%, and the values of backfire effects are less than 115%, which indicates that the rebound effect of carbon emissions shows a convergence trend. In the future, the rebound effect of carbon emissions will show a trend of convergence. When the total vehicle sales remain unchanged, the replacement proportion of new energy vehicles reaches 85%, and there is no subsidy policy, the rebound effect of carbon emissions is the smallest, indicating that this scenario is the most favorable for reducing carbon emissions and reducing environmental pressure. [ABSTRACT FROM AUTHOR]

Accurate and effective PM2.5 concentration prediction has important implications for public health and the ecological environment. To provide more accurate early warnings for haze prevention, urban planning, and people's travel planning, this paper proposes a new combined PM2.5 concentration prediction model. Firstly, the original sequence is decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by decomposition and reconstruction of the data by adaptive variational mode decomposition (AVMD) and sample entropy (SE), and then the reconstructed subseries are predicted by long and short-term memory networks (LSTM). The empirical analysis was carried out with three datasets from Beijing, Tianjin, and Baoding, and the following conclusions can be drawn: (1) The validity and robustness of the proposed model were verified, with R2 (0.982), RMSE (2.792), and MAPE (9.088%) being optimal in all comparison experiments. (2) The incorporation of secondary decomposition and pattern reorganization algorithms can effectively handle data with high volatility and non-linearity. (3) Compared with traditional machine learning models, the long and short-term memory network is more suitable for time series prediction. The model provides a novel and effective PM2.5 concentration prediction tool for the government and the public. [ABSTRACT FROM AUTHOR]

In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]