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Economic policies affect companies' production decisions. And the energy consumption volume is an intuitive reflection of the enterprise's production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study. • The linkage between coal and carbon prices are positive only in Shenzhen and Beijing. • The correlations between coal prices and EPU are negative most of the time. • The relationship between carbon prices and EPU vary from region to region. • Asymmetric effects of EPU and coal prices on carbon prices are revealed. [ABSTRACT FROM AUTHOR]

This study combined the real-time monitoring and investigation of traffic flows to comprehensively analyze the road traffic flow and vehicle structure in downtown Beijing. A dynamic emission inventory of motor vehicle air pollutants in downtown Beijing in 2021 was established, and the impact of these emissions on air quality was simulated and quantified, and different emission reduction control scenarios were proposed to evaluate their environmental improvement effects and explore measures to mitigate the impact of pollution emissions. The results show that the high traffic flow and the structure of the motor vehicle emissions in downtown Beijing are the main causes of severe motor vehicle pollution. Monitoring data shows that traffic flow in central Beijing is dominated by small passenger vehicles, while the vehicle mix is better than in other regions, with 72.0% of vehicles meeting "National V" or higher emission standards. However, to achieve higher air quality goals, further reducing vehicle emissions is necessary. Based on dynamic traffic flow, the average daily emissions of nitrogen oxides (NO X), particulate matters(PM 2.5) and volatile organic compounds(VOC) from motor vehicles in central Beijing are 17.7 tons, 0.6 tons and 14.0 tons, respectively, accounting for 23.0% of the city's average daily motor vehicle emissions. If a zero-emission zone for motor vehicles were implemented in central Beijing, the annual average emission level of pollutants will be reduced by 10.4% to 21.0%. The designation of ultra-low emission zones for motor vehicles could be effective in improving the air quality in the center of Beijing. [Display omitted] [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) pollution in Beijing was investigated based on field observation and air quality modeling. Measurement results showed that when using elemental carbon (EC) as the reference component, concurrent increases were observed in the relative abundances of sulfate, nitrate, organic carbon (OC) and water-soluble organic carbon (WSOC) when RH exceeded ∼65% during winter. The observed increases could not be explained by variations of primary biomass burning emissions, instead they likely pointed to heterogeneous chemistry and presumably indicated that formation of secondary inorganic and organic aerosols might be related during winter haze events in Beijing. Large gaps were found in winter when comparing the observational and modeling results. In summer, RH exhibited little influence on the observed sulfate/EC, OC/EC or WSOC/EC, and the observed and modeled results were in general comparable for the concentrations of sulfate, EC and OC. This study suggests that distinct yet poorly-understood atmospheric chemistry may be at play in China's winter haze events, and it could be a substantial challenge to properly incorporate the related mechanisms into air quality models. • Increases in sulfate/EC and WSOC/EC were observed under humid winter conditions. • Formation of secondary organic and inorganic aerosols may be related. • Large gaps existed between the observational and modeling results in winter. • RH exhibited little influence on sulfate/EC and WSOC/EC in summer. • The observed and modeled results were generally comparable in summer. [ABSTRACT FROM AUTHOR]

Urban public transit has been rapidly developed in recent years. However, given increases in travel volume, the level of service still needs to be improved to meet the satisfaction of passengers. Transit service providers and researchers have focused on improving transit devices, but the service level of public transit has not yet been effectively improved, so more and more research is interested in analyzing the travel patterns of passengers. Compared with traditional survey methods, smart card collection systems—which can collect spatial-temporal information about passengers' trips—are convenient for the study of bus and subway passengers' travel patterns. However, the data provided by smart cards have not yet been fully explored. Therefore, this paper proposed a multistep methodology to gather information on the travel patterns of bus and subway passengers in Beijing, China. We conducted statistical analyses and used an unsupervised clustering method to study and classify passengers based on travel patterns. Four groups have been identified: standard commuters, flexible commuters, and two types of low-frequency passengers. Then, a comprehensive analysis was conducted. We also discussed the changes of passengers' travel time consumption before and after the implementation of customized bus for high-frequency passengers. The analyses indicated that passengers' travel patterns can provide useful information for transit service providers and can help improve the level of service of urban public transit by promoting the promulgation of local public transport policies and the implementation of customized services. [ABSTRACT FROM AUTHOR]

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic. [ABSTRACT FROM AUTHOR]