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• To solve the forecasting problems of time-lead and nonlinearity in a system, a novel grey multivariable model is proposed. • To reduce the morbidity of the model, the solution of the proposed model is deduced through a derivation method. • The grey prediction evolution algorithm is employed to find the optimal value of parameters. • The validity of the new model was verified by three examples and applied to house price forecasting. Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

Despite the rapid development of China's digital economy and many studies involving the development of digital economy, there is no unified standard on the measurement of digital economy, and the existing accounting methods all have certain shortcomings. In view of this, this paper constructs a set of accounting system to measure the scale of digital economy industry, from the perspective of digital industrialization and industrial digitization, based on input-output tables. We separate digital economy from traditional industries by splitting and merging IO tables using data such as the Economic Census Yearbook and SAT to calculate the scale of digital economy industry. In this paper, data from Beijing, Tianjin and Hebei provinces in 2017 were used as an example for the calculation. The accounting results show that there is still a large gap in digital economy industry development between Beijing, Tianjin and Hebei, with the total digital economy industry scale, industry digitization scale, digital industrialization scale and the proportion of digital economy value added to GDP in Beijing being much higher than those in Hebei and Tianjin. In addition, due to the characteristics of the respective industrial structures in Beijing, Tianjin and Hebei, the percentages of the four major categories in digital industrialization also show different performance. The share of digital technology application industry in Beijing is much higher than that in Tianjin and Hebei, while the share of digital product manufacturing in Hebei and Tianjin is high. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative study of the tensile performance of plug-pin scaffold joints under different temperatures. The current research objects are mostly for the traditional low carbon steel scaffolding, and lack of research on the tensile performance of scaffolding joints in low temperature conditions. In this paper, based on the temporary seating facilities of the Beijing Winter Olympic Games complex, a new type of sorbite stainless steel was used. The tensile bearing capacity, damage mode and strain distribution patterns of the sorbite stainless steel joints and traditional Q355 joints under different temperature conditions were studied with finite element model and parametric analysis. The results indicated that for traditional Q355 joints, damage primarily occurs in the form of wedge bending deformation, and that decreasing temperature leads to increased tensile bearing and yield capacity. In contrast, for sorbite stainless steel joints, higher tensile bearing capacity was observed at +20 °C, but at −20 °C and − 40 °C conditions, the bend of the C-type card becomes more prone to crack. The accuracy of the ABAQUS finite element model is verified by comparing it with test results. Increasing the thickness of the U-type and C-type cards can slightly increase the tensile bearing capacity of the joint and a minimum wall thickness of 2.7 mm is recommended. Two suggestions are proposed for improving the joint design using sorbite stainless steel. The research provides a premise for further studies on the tensile performance of plug-pin scaffold joints. • A low-temperature loading test device and method for joints are proposed. It offers a test environment of −70 °C ∼ 150 °C. • The material properties of sorbite stainless steel and Q355 at −40 °C, −20 °C and room temperature are studied. • The mechanical properties of plug-pin scaffold joints using sorbite stainless steel and Q355 at low temperatures are studied. • The tensile properties of plug-pin scaffold joints at different low temperatures are investigated. • Theoretical load-displacement modeling of plug-pin scaffold joints is developed. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

This paper about the high-quality development level of Beijing-Tianjin-Hebei region is of vital importance to promote high-quality development of China. An evaluation index system that can comprehensively describe the high-quality development of the Beijing-Tianjin-Hebei region is designed in this paper. There are 27 indexes selected to evaluate the high-quality development of the cities in Beijing, Tianjin and Hebei province from three dimensions: target level, standard level and index level. By applying the SOM (Self-Organizing Map) algorithm to cluster analysis of high-quality development levels. The results of the study showed that there are variations in high-quality development of Beijing-Tianjin-Hebei city cluster. Beijing has the highest quality development level, followed by Tianjin and Hebei, and a significant gap exists between Beijing and Hebei in terms of the level of quality development. In addition, the high-quality development level of cities in Hebei Province are not balanced. Therefore, Beijing-Tianjin-Hebei city cluster should break down administrative barriers, making overall plans and promoting coordinated development among regions. [ABSTRACT FROM AUTHOR]

• We investigate driving forces of different land-use types to BSD. • Diversified machine learnings are proposed for spatial interpolation fusion. • Hotspot detection establishes sampling points for reducing the MAUP effect. • Fusion models improve the interpolation performance: accuracy and stability. • Word clouds identifies predominant facility names in each land-use type. Land use plays a crucial role in promoting the bike-sharing demand. Traditionally, studies on bike-sharing demand (BSD) are mainly focused on its prediction through regression methods, but the influence of MAUP (modifiable areal unit problem) in modeling is ignored. This paper aims to model spatial BSD distribution and prove the driving forces of different land use types to BSD through a machine-learning-based multiple interpolation fusion method. The hotspot detection model is employed to establish sample points covering different land use types in urban areas. In order to capture the differences in adaptations among different urban regions and for different data sizes, six machine learning methods are applied and evaluated to improve BSD estimation by fusing five spatial interpolation algorithms, including Inverse Distance Weight, Spline, Kriging, Natural Neighborhood and Trend. The methodological verification of Beijing City shows that the fusion models improve the estimation performance compared with individual interpolation algorithms, and that GRNN (generalized regression neural network) method is superior to all the others. According to fitting results of all POIs based on the GRNN fusion model, we identify which types of facilities correspond to customers that will have a stronger preference for bike-sharing and demonstrate which facility names are more prominent in each land use type. The conclusions presented here enrich our understanding relationships between land-use and BSD, which provide a valuable foundation for the bike-sharing development. Compared with implementing regression in an analysis zone or a square grid, troubles caused by the MAUP are effectively solved through this method. [ABSTRACT FROM AUTHOR]

• An integrated optimization approach of the flow control and train scheduling considering skip-stop patterns is established. • Joint strategy that can reasonably allocate arriving passengers and control train occupancy is obtained. • The optimization strategy can increase the average boarding rate from 88.09% to 94.69%. • The average travel time of passengers can be reduced by 7.22% by the proposed approach. • The proposed strategies provide more explicit and accurate guidance for metro managers. In many special situations (epidemic, emergencies, large-scale events, etc.) of the metro, it is a considerably real issue on how to control passenger flow density and improve operational efficiency synchronously under the control requirements of specific the load factor. This paper aims to propose an integrated optimization approach for jointly researching passenger flow control and train scheduling with skip-stop patterns. Besides, the passenger dynamic demand and train occupancy rate are considered. A mixed integer programming model is formulated to minimize the unused capacity of trains and the total number of waiting passengers at platforms and outside stations. Through the comprehensive analysis of the relationship between passengers and trains, the numerical experiments are implemented in Beijing Metro Line 1 to illustrate the validity of the proposed method. Finally, discussion and comparison of the performance of integration optimization strategies with diverse scenarios are given. The results show that, under the optimized combination strategy, the average boarding rate of all stations can increase from 88.09% to 94.69% by the proposed approach, the number of waiting behaviors of passengers can be declined by 92.65%, and the average travel time of passengers can be reduced by 7.22%. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

[Display omitted] • 2010-2018: Ecological-economic coordination was highest in Hebei and lowest in Tianjin. • The ecological-economic coordination degree of various regions has obvious spatial spillover effects. • Improved functional and development tactics and policy reforms can strengthen ecological-economic development relations. The Beijing-Tianjin-Hebei region is an integral part of the ecology and economy of northern China. Unique geographical factors closely interlink the regions; however, limited attention has been paid to urban clusters' ecological and economic spatial linkages. Therefore, this paper introduces Ecosystem service value and Ecological-economic coordination degree, considering extensive spatial spillover effects within the region under study. This paper adopts spatial autocorrelation and the spatial Durbin model to detect the coordination relationship. Moreover, examine the influence of population growth, sales of consumer goods, health facilities, green areas, and Tertiary Industry on Ecological-economic coordination. The relationship is tested economic growth, using the internal and external Thus, it determines that the change of coordination degree shows a "V"-shaped development in time and space and gradually realizes the overall coordinated development. It affirmed the judgment that the Ecological-economic coordination degree of each region has an obvious spatial spillover effect. Policy reforms, improved functions, and development mechanisms can enable the balanced and healthy development of the region's ecological and economic systems. [ABSTRACT FROM AUTHOR]

• This study identified four types of urban industrial lands. • A large number of urban industrial land needs to be decentralized. • Some urban industrial land is suitable to be converted into urban green space. Decentralization is essential for the sustainable socio-economic development of megacities. In the context of the decentralization of noncapital functions in Beijing, the renewal of inefficient urban industrial land (UIL) into urban green space (UGS) is an important way to improve the quality of the city. This paper analyzes the urban industrial land use efficiency (UILUE) and its urban green space suitability (UGSS) of 865 study units in Daxing District, Beijing, and combines bivariate spatial autocorrelation with cluster analysis. The results show that (1) the distribution of UIL in Daxing District is "dense in the north and sparse in the south", and UILUE shows a spatial pattern of "high in the west and low in the east", with a high-efficiency area of 985.85 ha, accounting for 12.06 % of the study area, and a low-efficiency area of 507.77 ha, accounting for 6.21 % of the study area. (2) Among the existing UIL, the high suitable area is 1106.46 ha, accounting for 13.57 % of the study area, and the low suitable area is 439 ha, accounting for 5.38 % of the study area. (3) Through bivariate spatial autocorrelation analysis, four types of UIL were identified, among which the low–high cluster area had the greatest potential for land conversion, with 175 study units and an area of 1335.22 ha, accounting for 16.28 % of the study area. Finally, this paper proposes recommendations for the renewal and utilization of UIL in Daxing District to provide guidance and suggestions for the decentralization of other megacities in the world. [ABSTRACT FROM AUTHOR]

Pumping wells (PWs) lie at the core of agricultural irrigation infrastructure, and their spatial configuration is of great significance for the scientific management of regional agriculture and rational use of water resources. To solve the problems of unreasonable and unscientific spatial allocation of regional agricultural irrigation PWs, this paper proposes a new strategy for updating agricultural irrigation PWs based on a spatial decision model, using Songzhuang Town, Tongzhou District, Beijing as a case study. The model utilizes constraints based on local factors such as actual land use, and proposes a novel feasible solution strategy that combines random local search and abrupt jumps to increase the probability that feasible solutions will be found, addressing the performance issues of simulated annealing algorithms, influence of initial values, and parameter sensitivity. Finally, we used the proposed method to perform updated decision analysis on the spatial configuration of PWs in the study area. The results showed that most of the crops in the study area lack irrigation, and the currently available PWs cannot meet the needs for irrigation. The new PWs added based on this decision method can be more evenly distributed within the predefined optimization unit. The proposed number of PWs to be added is 166, the density of PWs is 0.04/ha, and the crop area covered by the PWs is 3350.43 ha. In contrast to the previous PW layout, the optimized PW configuration can meet the irrigation needs of residents, indicating that the new method proposed in this paper has good potential for extension and application. [Display omitted] • A new decision-making method for renewal of agricultural irrigation PWs was proposed. • The maximum unit crop area can be covered when the buffer zone radius was set to 166 m. • The optimal spacing of agricultural irrigation wells was 332 m. • The scheme alleviated the problem of insufficient water for agricultural irrigation. • The method has popularization potential for regional agricultural water management. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

Pollution havens create environmental inequality issues. China's recent policy of directing high-pollution firms to migrate out of its capital, Beijing, offers a case of a pollution haven that was mandated, rather than resulting from firms' responses to environmental regulation. More importantly, it leads to a question that has been less discussed in previous literature: does a pollution haven strike back? After the highly polluting enterprises moved out of Beijing to neighboring Hebei province, Hebei has been blamed for the continuing poor air quality in Beijing, based on a perception that the pollutants emitted in Hebei are being blown back to Beijing. This paper investigates the existence of a pollution haven in Hebei and quantifies the contribution of Hebei's emissions to Beijing's air quality. We find that the proportion of output values and the relative concentration of high-pollution industries in Hebei increased when the large-scale out-migration began, while the indicators in Beijing decreased. We also find higher emissions of enterprises in Hebei than their counterparts in Beijing. Together, these findings indicate the existence of a "manmade" pollution haven. Further, based on high-frequency spatial data, we combine an Autoregressive Distributed Lag model and a Spatial Autoregressive Model and decompose the factors that affect Beijing's air quality into local and regional transport of air-borne pollutants. We find a significant effect of the neighboring cities' air pollution on Beijing's air quality. This indicates that the pollution haven indeed struck back. These findings imply the importance of regional policies to control air pollution. [ABSTRACT FROM AUTHOR]

A research into congestion propagation mechanism of urban rail transit passenger flow in train delay scenario is of great significance to the formulation of coordinated limitation measures of passenger flow among the operation lines in urban rail transit networks. This paper presents a methodology for modelling cascade dynamics of passenger flow congestion. In this method, firstly, a computing method of network passenger flow states based on load entropy is proposed to describe the overall load conditions of passenger flow in the network. Secondly, a rail transit network is developed and the computation formulas of load and capacity of nodes have been established combining with a comprehensive consideration of nodes' topological passenger flow attributes. Thirdly, Trip Betweenness Centrality of nodes is proposed and calculated. Considering the function of rail transit line capacity and load distribution strategy, the passenger flow congestion in train delay scenario is established to calculate cascade dynamics of congestion and the state transition of urban rail transit network. A real case study on Beijing Metro Line 10 is used to demonstrate the proposed methodology. The results show that the load values of nodes and the distances from the initial failure nodes can be determined for the spreading scope of passenger flow congestion in disrupted metro line and adjacent metro lines. [ABSTRACT FROM AUTHOR]

The Olympic Games are striking a balance between economic and ecological benefits. Taking the 5 ice event venues of the 2018 PyeongChang Winter Olympic Games as the research object, the CO 2 emissions under the 2018 PyeongChang Low-carbon scenario and 2022 Beijing Low-carbon scenario are compared. The empirical results show that, in terms of venue operation, the Beijing strategies can reduce CO 2 emissions by 715.7 t compared with the PyeongChang strategies. In terms of the refrigerant escape, Beijing's adoption of CO 2 refrigerant can reduce the emission of CO 2 by 1202.2 t compared with the R404 refrigerant used in PyeongChang. This paper confirms the progress of the Green Winter Olympic Games, and may provide implications for the future host countries as well as the cities which in the green transformation. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei metropolitan cluster has inevitably brought many problems along with its vigorous economic development, one of which is environmental pollution. Based on our previous research, we clarified the mechanisms and characteristics of environmental pollution transfer in the Beijing-Tianjin-Hebei and surrounding regions, and this paper refered to the further correct delineation of the responsibility for environmental pollution control in Beijing-Tianjin-Hebei and the surrounding provinces. This study measureed the full cost of environmental pollution control and economic benefits in Beijing, Tianjin, Hebei and surrounding provinces in terms of the economic costs required for governance, and constructed an inequity index (CG index) to characterise the inequity in the share of costs borne and benefits by the aforementioned provinces. The results of the study showed that there is significant environmental inequity and inequality of responsibility in the process of regional trade among the provinces in and around Beijing, Tianjin, Hebei and the surrounding region. The developed provinces (Beijing and Tianjin) benefited more and bore less responsibility for pollution control due to their optimised industrial structure, producing more capital-intensive and high value-added industries, while provinces with heavy chemical, high pollution and labour-intensive industries, such as Hebei and Shanxi, developed their economies more in the primary and secondary sectors due to their irrational industrial structure, and therefore benefited less and bore more when trading goods with the developed provinces (Beijing and Tianjin) benefited less and took more responsibility for pollution control. Based on the above findings, policy recommendations are then put forward to provide suggestions and references for the collaborative management of environmental pollution in Beijing, Tianjin and Hebei. [ABSTRACT FROM AUTHOR]

Urban resilience reflects the ability of cities to resist, absorb, adapt and recover from danger in a timely and efficient manner, which is critical to the normal operations of cities and the daily lives of citizens. An increasing number of cities have included resilience in their city planning. Thus, conducting the resilience evaluation is necessary for urban resilience improvement and enhancement. This paper proposed a MACBETH-based method for urban resilience evaluation, in which a multi-dimensional evaluation system including four dimensions and sixteen criteria is established, and the main procedure was presented to determine the overall and dimensional resilience indices. The proposed method was applied to evaluate Beijing's urban resilience. The results shown that Beijing's resilience improved from 2016 to 2020, despite the impact of the COVID-19 pandemic in 2020. [ABSTRACT FROM AUTHOR]

This study investigates the carbon compensation mechanism as a tool to promote coordinated economic and environmental development in China's Jing-Jin-Ji region (Beijing, Tianjin, and Hebei). A novel approach for calculating carbon compensation cost that integrate emission responsibility as well as carbon abatement cost through a bi-level programming model is proposed. Shared producer and consumer responsibility is established for carbon emissions under the multi-regional input-output model. Marginal abatement costs of Beijing, Tianjin, and Hebei are further calculated through the input-output and linear programming method. Results highlight that the emission responsibility allocation method significantly affects the inter-regional carbon compensation amount. The production-based approach leads to a 77% reduction of carbon compensation amount from Beijing to Hebei, and a 170% overestimation from Tianjin to Hebei compared with the shared responsibility approach in 2060. The optimal carbon compensation unit cost ranges from 1539 to 6438 yuan/ton when the carbon reduction ratio ranges from 10% to 20%. Beijing would compensate Hebei 3.56 and 29.74 billion yuan for achieving 10% and 20% emission reduction, respectively. Similarly, Tianjin's compensation for Hebei would be 0.23 and 1.87 billion yuan in the two scenarios. Results further show that implementing the proposed carbon compensation mechanism can reduce total abatement cost by 43% and 84% in Jing-Jin-Ji under the two scenarios. The methods and results of this paper could help inform future carbon compensation implementation in China and other countries. • Develop a bi-level programming model to determine optimal carbon compensation cost. • Consider emission responsibility and carbon abatement cost in Jing-Jin-Ji. • Resulting mechanism reduces abatement cost by 43% for 10% emission reduction. • Production-based allocation underestimates carbon compensation amount by 62%. • Optimal carbon compensation unit cost changes with marginal abatement cost. [ABSTRACT FROM AUTHOR]

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line. • A novel multi-objective optimization model based on the reinforcement learning is proposed to search for the optimal control sequence. The embedding matrix is used to formulate the control sequence. • An intelligent train operation algorithm based on the gated recurrent unit network is proposed to compute the optimal speed trajectory without the precise train dynamics or the prior knowledge from the experienced drivers. • The energy consumption is reduced by 3.17%, 0.71% and 6.77% respectively comparing with fuzzy predictive control, genetic algorithm and field test data. [ABSTRACT FROM AUTHOR]

While it is widely known that the greening landscape spatial quality plays a vital role in promoting the level of highway landscape design, there is still no consensus on how to measure it quantitatively on a large scale. Recent advancements in computer vision tasks under deep learning, along with esmerging datasets such as street view pictures, have shown the potential to overcome previous limitations, leading to a paradigm shift in research. This paper proposes a novel digital measurement technique that automatically identifies elements and greening landscape spatial features from a partial panoramic and spherical view field, considering the driver and passengers' environmental perspectives. To achieve this, we have developed a spatial quality parameter index system consisting of three dimensions and nine indicators, with two regions dedicated to plains and mountainous regions. We have also created a visualization atlas that illustrates the landscape of highway greening in both vertical and horizontal dimensions. We applied this method to the Beijing section of the Beijing-Tibet highway to guide targeted improvement strategies from different perspectives. Our findings provide strategies for enhancing scientific performance and tools for quantitative analysis in digitizing, engineering, and refining highway landscape design. • Propose a high-precision training model Panoptic-DeepLab. • Propose a digital measurement indicator system of highway greening landscape spatial quality. • Gives an atlas representation method of highway greening landscape spatial quality. [ABSTRACT FROM AUTHOR]

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

Currently, ozone pollution is one of the key environmental concerns, and to provide more effective strategies for ozone control, this paper proposes the use of the Geostationary Environmental Monitoring Spectrometer (GEMS) in conjunction with MAX-DOAS for observations and ozone sensitivity analysis. Compared to traditional polar-orbiting satellites, GEMS, launched by South Korea in 2019, offers superior spatiotemporal resolution. However, there is a lack of ground validation for GEMS data related to ozone and its precursors. In this study, we conducted validation in the Jing-Jin-Ji (Beijing-Tianjin-Hebei) region using two typical MAX-DOAS sites and the China National Environmental Monitoring Center (CNEMC) sites, confirming the accuracy of GEMS data. Analysis of the seasonal horizontal distribution results from GEMS and vertical distribution results from MAX-DOAS in 2022 reveals that the concentrations of NO 2 , HCHO, O 3 , and ozone sensitivity not only display seasonality and regional variations but also exhibit distinct characteristics at different altitudes and times, influenced by local climatic conditions, human activities, and topography. When formulating ozone control policies, it becomes imperative to consider factors such as time, regional characteristics, and seasonality. Comparisons between daily results from TROPOMI and GEMS indicate that polar-orbiting satellites may overlook high-pollution periods within a single day, and their measurements may not provide a comprehensive representation of an entire day's data. Consequently, relying solely on polar-orbiting satellite data for shaping ozone prevention and control policies may inadvertently encourage ozone formation during specific time periods, rendering these policies ineffective. Therefore, leveraging GEMS is essential for the development of comprehensive, around-the-clock ozone control policies. Finally, we used monitoring data from Beijing on October 6, 2022, to illustrate how the combination of GEMS and MAX-DOAS enables three-dimensional monitoring of pollutant transport processes on an hourly scale. [Display omitted] • Validation of the accuracy of ozone and its precursor data from GEMS. • Analysis of hourly ozone sensitivity level distribution using GEMS. • Limitations of polar orbit satellites in O 3 sensitivity analysis compared to GEMS. • Observation of the transport of pollutants combining GEMS and MAX-DOAS. [ABSTRACT FROM AUTHOR]

The solar furnace is key equipment for the thermochemical reaction research which uses focused solar energy to drive the reactor, and the solar flux distribution on the reactor surface plays a decisive role in the regulation of the thermochemical reaction process and formation of target products. In this paper, a reflective solar furnace with a thermal power of 30 kWth in Yanqing, Beijing, is taken as the research objective. Firstly, the Monte Carlo Ray Tracing (MCRT) method is used to establish the mathematical model for the optical calculation, and the distribution characteristics of solar flux are deeply analyzed. The accuracy of the model is verified through the comparison with the SolTrace software and experimental measurements respectively. Secondly, an improved four-quadrant and adjustable focusing solar furnace experimental equipment is developed, and a new solar flux distribution regulation strategy is proposed by using the inward crossover and outward expansion offset of the four-quadrant focusing points coupled with the heat-absorbing platform movement. The results show that the non-uniform factor and the peak value of the solar flux distribution in the set target area can be optimized by selecting an appropriate inward crossover offset and movement of the heat-absorbing platform, respectively. Moreover, when the DNI varies between 480 W/m2 and 800 W/m2, through the optimized control strategy, a non-uniformity factor with the value of less than 0.10 can be achieved in the target area of a square reactor surface with a side length of 10 cm, and the peak flux can also be controlled in the range from 3000 kW/m2 to 3300 kW/m2 with a fluctuation range of less than 10.0% as set by the thermochemical reaction. The optimized control strategy can effectively meet the uniformity and stability requirements of solar flux distribution in solar thermochemical reaction and finally achieve the goal of regulating the target products. • An improved four-quadrant adjustable focusing solar furnace is developed. • An innovative solar flux distribution regulation strategy is proposed. • A non-uniformity factor with the value of less than 0.10 can be achieved. • The peak flux can be controlled in the range from 3000 kW/m2 to 3300 kW/m2. [ABSTRACT FROM AUTHOR]

At present, the urbanization in Beijing is developing fast, and its accompanying rapid accumulation pattern of urban infrastructure stock is undoubtedly of great significance to the realization of Beijing's infrastructure construction and urbanization coordinated development. This paper analyzes the time series of changes in 8 types of infrastructure stock and 10 types of infrastructure materials in Beijing through the stock accounting model. The decoupling relationship between the level of urbanization in Beijing and the infrastructure stock is analyzed through the decoupling model. Its purpose is to achieve social and economic development while achieving the optimal scale of infrastructure stock construction, thereby promoting the progress of Beijing's new urban development. The results show that: (1) Beijing's infrastructure stock was on an upward trend from 1949 to 2018. In 2018, the total infrastructure stock expanded to 1,384 times that of 1949, reaching 3,342.036 million tons. It has experienced a process of slow growth from 1949 to 1975, rapid growth from 1976 to 2008, and steady growth from 2009 to 2018. (2) From 1949 to 2018, in terms of building types, Beijing has the largest housing stocks, followed by the road stocks, the pipeline stocks and the track stocks. In terms of material types, gravel, cement, bricks, and steel account for about 99% of the infrastructure material stock in Beijing. (3) The state has reached weak decoupling between the level of urbanization in Beijing and the stock of roads and track, and is moving from "weak decoupling" to finally achieving "strong decoupling", while the state between the level of urbanization and housing stocks and pipeline stocks has not yet reached decoupling. The state between the level of urbanization and the stock of various infrastructure materials has not yet reached a state of decoupling, and most of them are extended decoupling, which puts great pressure on Beijing's resources and environment. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei region is an area where the conflict on resource environment and development most acute. Based on the panel data of listed companies in 2010-2019, this paper constructs a panel model on strategic identification degree, which reveals the development strategy of enterprises and the degree of strategic transformation. The results show that environmental and credit policies in this region have been focused on protecting and supporting business-oriented enterprises. The listed company converts monetary funds and some business assets into financial assets through the items available on the balance sheet, which hinders the development strategy of promoting independent innovation in Beijing, Tianjin and Hebei. We hope that the government should pay more attention to the problem of illegal use of funds in enterprises, and take the necessary regulatory measures to correct the tendency of misallocation of assets. At the same time, the environmental protection department and commercial banks will coordinate and work together to promote the independent innovation and business development of enterprises. [ABSTRACT FROM AUTHOR]

With the development of society, the traditional residential building form has gradually failed to meet the needs of modern people's life, which urgently needs to be updated and developed. At the same time, there are also many problems in the transformation. For example, the transformed buildings cannot be integrated into the regional architectural environment, and the traditional construction skills are missing, and so on. Therefore, traditional residential transformation strategy needs more rigorous generation process. The basic idea of extenics is to use formal methods to deal with contradictions in various fields, which is very suitable as a tool for scientific analysis of contradictions and feasible strategies in the practice of residential transformation. This paper takes Baoshui village as an example, establishes the problem model based on extenics, analyzes the objectives and conditions, solves the contradiction through the extension transformation method, generates the strategy, and finally use excellent standard evaluating method evaluates to obtain the optimal strategy. The generation process of extension strategy for rural residential transformation is explored. On the one hand, it can provide a reference for the formalization of the practice process of rural residential transformation in the future, on the other hand, it can make the culture of traditional rural residential continue and regenerate. [ABSTRACT FROM AUTHOR]

Chinese economy has changed from high-speed growth stage to high-quality development stage. Beijing-Tianjin-Hebei region is an essential part of economic development in China, where high-quality development and realize innovation-driven development need to be achieved urgently. In this paper, an index system is constructed to measure the high-quality development of Beijing-Tianjin-Hebei region, and the entropy weight method is used to measure the high-quality development of Beijing-Tianjin-Hebei region. The results indicate that from the perspective of vertical development, the high-quality development of Beijing-Tianjin-Hebei region from 2009 to 2018 is on the rise year by year. Besides, Hebei has presented a rebound development recent years due to the weak development foundation in the early stage. Tianjin develops evenly with an annual growth rate of 10%, and Beijing has also been growing steadily. From the perspective of horizontal development, the high-quality development of Beijing-Tianjin-Hebei region is significantly unbalanced, and the comprehensive index of high-quality development in Beijing is significantly higher than that in Tianjin and Hebei, demonstrating a large gap in high-quality development among Beijing and the other two regions. The innovation contribution of the target layer to the criterion layer is the largest. [ABSTRACT FROM AUTHOR]

In the early morning on December 17, 2020 Beijing time, China's chang'E-5 probe successfully returned to the Earth with 1731 g of lunar samples after completing drilling, shoveling, packaging of lunar soil and scientific exploration on lunar surface. It is the successful completion of the third phase of China's lunar exploration project, namely "circling, landing and returning to the moon". The scientific objectives of CE-5 mission are to carry out in situ investigation and analysis of the lunar landing region, laboratory research and analysis of lunar return samples. This paper analyzes scientific exploration tasks of CE-5 mission conducted on the lunar surface, and carries out the scientific payload system architecture design and individual scientific payload design with the scientific exploration task requirements as the target, and proposes the working mode and main technical index requirements of the scientific payloads. Based on the preliminary geological background study of the Mons Ruemker region which is the landing region of CE-5, the lunar scientific exploration and the laboratory physicochemical characterization of the return samples are of great scientific significance for our in-depth understanding of the formation and evolution of the Earth-Moon system and the chemical evolution history of the lunar surface. [ABSTRACT FROM AUTHOR]

Pairs trading is one of the mainstream statistical arbitrage strategies in current financial market practice that can seize possible arbitrage opportunities caused by mispricing between similar assets. Based on the theory of statistical arbitrage and the similar attributes of carbon emission allowances traded in different carbon markets, this paper studies the applicability of the pairs trading strategy among different pilot markets in China. The novelty and possible contribution of this paper are as follows. Firstly, this paper extends the statistical arbitrage-based pairs trading framework to the analysis of carbon market transactions in China, which broadens the understanding of the correlation between China's carbon markets. Secondly, this study explores the arbitrage opportunities among China's regional carbon markets that have not been revealed by previous studies. Thirdly, based on the pairs trading analytical model adopted in this study, this paper further investigates the operation characteristics of China's carbon markets and the interaction between markets, as the sensitivity of strategy performance to the key parameters is also discussed. The results show that arbitrage based on pairs trading is profitable. In the baseline case, the annualized arbitrage returns between the Shanghai emission allowance and Beijing emission allowance is about 9.11%, the highest among all pairs. However, there is almost no pairing relationship between the Tianjin carbon market and other markets. By discussing the time distribution of arbitrage signals generated by the pairs trading strategy, it is found that most signals are sent out during the inactive trading period in a compliance cycle. The conclusions provide valuable insights for formulating relevant carbon market regulatory policies and investors' participation in arbitrage activities in China's carbon trading markets. • The application of the pairs trading strategy is extended to the Chinese carbon markets. • Based on relative value arbitrage theory, arbitrage opportunities between markets are examined. • Pairs trading strategy can also yield sustainable and distinctive returns in the Chinese carbon markets. • Arbitrage trading between the Shanghai and Beijing markets may yield promising annualized returns with about 9.11%. [ABSTRACT FROM AUTHOR]

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction. • Evaluate the impact of China's CETS on the power industry's green innovation in each pilot. • The CETS effects among pilots are heterogeneous, only significant in Beijing and Guangdong. • Rational CETS mechanism design could improve the power industry's green innovation. • Enterprise scale and R&D investment positively moderate and mediate the CETS effect. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Sewer pipes health and failure consequences evaluation are vital to ensure the safety of urban sewer networks. Sewer pipes with low health and severe failure consequences should be the primary focus of daily maintenance and management for drainage authorities. Conversely, sewer pipes with high health and low failure consequences are temporarily not a primary concern, in order to reduce resource wastage within the constraints of limited economic and time resources. Thus, the scientific analysis of their relationships is deemed essential. This paper applies supply-demand balance theory to analyze the relationship. On the supply side, a health Fuzzy Comprehensive Evaluation model (HFCE) is proposed with weighted hierarchies established. On the demand side, a failure consequences FCE model (FFCE) is established. Each sewer pipe's supply-demand balance score is analyzed using matrix multiplication, highlighting two types of imbalanced pipes and sub-districts. Taking the Liangshui River Basin in Beijing as an example, the results show that the health evaluation can identify the spatial distribution of the health of sewer pipes, with low health levels V and IV accounting for 10.23 % and 4.43 %, respectively. The failure consequences evaluation can evaluate the spatial distribution of the severity of sewer pipe failure consequences, with high failure consequences levels V and IV respectively accounting for 10.97 % and 16.77 %. The supply-demand balance analysis identifies the proportion of the first and second types of imbalanced pipes, respectively 14.24 % and 6.80 %. This method, by analyzing the relationship of health and failure consequences, provides a decision-making basis for drainage authorities to prioritize sewer pipe operation management, repair, and maintenance. • Supply-demand balance to identify the relationship of health and failure consequences. • DEMATEL optimized AHP calculated weights by considering inter-indicator relationships. • Improved health evaluation via hydraulic metrics in drainage models, enhancing accuracy. • Identifying low(high) health/high(low) failure consequences areas for drainage authorities. [ABSTRACT FROM AUTHOR]

This paper studies the location–allocation problem of distribution centers (DCs) for fresh products, in which it requires to determine the number and locations of DCs and design the allocation scheme for fresh products. The main challenge in optimizing this problem is to handle the uncertain demands with only partially known probability distribution information. To address this challenge, this paper first characterizes the ambiguous probability distributions of fresh products demands by an ambiguity set. Then, from different perspectives, this paper proposes two distributionally robust (DR) location–allocation models to find the optimal decisions that perform best in view of the worst-case distribution within the ambiguity set. The first model is with ambiguous expectation, which is transformed into a mixed-integer linear programming (MILP) model via employing the linear decision rule technique. The second model is with ambiguous chance constraints. This model is a semi-infinite chance constraint programming problem, which is transformed into a mixed-integer second-order cone programming (MISOCP) model by deriving safe approximations of the ambiguous chance constraints. To verify the feasibility and effectiveness of the proposed models, a case study about Hema fresh chain stores in Beijing is addressed. The experimental results demonstrate that the optimal decision provided by the proposed optimization methods can immunize against ambiguous probability distributions of demands with a small cost price. • Two DR location–allocation models of DCs for fresh products are raised. • The distribution information of uncertain demands is only partially known. • The DR model with ambiguous expectation is transformed into an MILP model. • The DR model with ambiguous chance constraints is transformed into an MISOCP model. • A case study shows the feasibility and effectiveness of the proposed DR models. [ABSTRACT FROM AUTHOR]

• The paper integrated the flexible wheelset model calculated by the finite element method into the classical vehicle-track coupling dynamic model, and then imported the bearing substructure model into the vehicle-track coupling dynamic model including flexible wheelset to complete the bearing-vehicle-track coupled dynamic simulation model. • In order to solve the contact relationship between raceway and roller, the slice method is used to describe the contact state of bearing substructure, and the contact between inner ring rib and roller is considered. • To verify the correctness of the established bearing-vehicle-track model, field tests were conducted on the high-speed railway line from Beijing to Tianjin. The field tests verify the effectiveness of the simulation model as well as the correctness of the vehicle model under polygonal wheel wear excitation. • The effects of the different orders and amplitudes of wheel-polygonal wear on dynamic performance of the axle-box system are analyzed and discussed in detail. With the increasing speed of high-speed trains and the deteriorating operation environment of axle-box system, the dynamic performance of axle-box bearing directly affects the stability and safety of operation. In this paper, a bearing-vehicle-track coupling dynamic model is established, and its effectiveness is verified by field tests. The simulation results of the coupling dynamic model including wheel-polygonal wear show that the effect of high order polygonal wheel wear (17th ∼ 21st order) on the axle-box system is greater than that of low order wheel wear (1st ∼ 5th order). The frequency domain of axle-box vertical vibration acceleration excited by high order wheel-polygonal wear is mainly distributed in 400 ∼ 600 Hz. The low order polygonal wheel wear amplitude has little effect on the bearing roller-outer raceway contact load. When the wear amplitude of 20th order polygonal wheel is 0.06 mm, the roller-outer raceway contact load is 27.22% higher than that when the wear amplitude is 0.04 mm. In order to avoid bearing failure caused by excessive bearing roller-raceway contact force, the amplitude of 20th order polygonal wheel wear should reduce to less than 0.04 mm. [ABSTRACT FROM AUTHOR]

• Behavior of waterscape park visitors can indicate their perceived water quality. • Mobile phone data allows such quantitative behavioral indicators such as Park visiting hours (PVH) and sub-daily Attendance Probabilities (PVA). • PVH increases with improved water quality, indicating a prolonged enjoyed ecosystem service. • Integration of multisource big data is prospective approach to assist application of behavioral metrics in ecological and environmental issues. The rational evaluation of pollution control measures is crucial for advancing water pollution management, ensuring water quality, and creating high-standard urban environments. Currently the evaluation heavily relies on water quality indicators with little regard to the sense of gain of the people. In this paper, we proposed an objective indicator PVH (Park Visiting Hours) to represent the behavioral response, as a materialized sense of humans, to water quality. We demonstrated the application of PVH in four typical waterscape parks in Beijing with multi-source data, and the results showed that: (1) The Mann-Kendall Test and an independent correlation test utilizing Sina Weibo data (Chinese Twitter) indicated that PVH was significantly negatively correlated with the Satellite image based Sensory Pollution Index (SPI), as corroborated the competence of PVH to appraise the sense of gain from water quality improvement. (2) Multivariate regressions showed that an increase in SPI of 0.1 corresponds to an average 8.8 min reduction in PVH. (3) The relationship between PVH and SPI was consistent across various visitor demographics, and whether visitors were in groups or alone. This study introduced PVH as a novel but concise indicator to represent water quality-related behavior of park visitors and would inspire the use of big multisource data for measuring human perceptions of ecosystem services. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

To better make use of the waste heat in distributed data centers, a new design of integrated waste heat recovery system that can meet different needs including heating for users and data center cooling is introduced. The system can switch among different operation modes including waste heat recovery mode, natural cooling mode, composite cooling mode and refrigeration mode based on the ambient temperature. The integrated waste heat recovery system is experimented in the enthalpy-difference laboratory, and the experimental results show well consistency compared with the modeling results. The operation strategies to switch among different modes are determined based on the experimental and modeling results. The paper introduced PUE (Power Usage Effectiveness) and ERE (Energy Reuse effectiveness) to comprehensively evaluate the cooling and energy reutilization effectiveness of the system. According to the evaluation result, the integrated waste heat recovery system has well performed energy effectiveness, environmental and economic effect. The ERE values of using the integrated waste heat recovery system in Chinese typical climate cities including Harbin, Beijing, Xi'an, Shanghai and Guangzhou are 0.87, 1.02, 1.04, 1.17 and 1.29 respectively. Using this system at 96 kW distributed data center instead of CRAC (computer room air conditioning) system can save 152.7 MWh electricity, 56.4 tons of standard coal, and 244.7 tons of CO 2 emission in Beijing annually. The payback periods of this new system in different climate regions in China are all within 3 years. • An integrated data center cooling and waste heat recovery system is proposed. • Operation performance of the system is verified by experiment and modeling. • Energy Reuse Factor in Beijing is 1.02, and payback period is within 3 years. [ABSTRACT FROM AUTHOR]

[Display omitted] • A methodological framework to delineate the provision area for the wind erosion prevention service based on the carrier dynamics of ecosystem service flows; • A computational framework to determine ecosystem service transmission pathways; • Methods to calculate the realized ecosystem service value distribution within the service provision area; • Implications on urban ecological hinterland delineation and precise ecological compensation program design. Many ecosystem service (ES) items, such as the wind erosion prevention service (WEPS), have spatially separated service provision and benefit areas (SPA & SBA), which are connected by directional ecosystem services flows (ESF). Clarifying the specific spatial relationships between the SPA and SBA is of potential scientific and policy importance for such directional flow-conveyed ES. Much of the existing research has focused on the identification of SBA based on a given SPA. However, due to the asymmetry of supply-based and demand-based ESFs and geospatial heterogeneity, this relationship cannot be directly inferred backward to obtain the SPA for a given SBA. In this paper, we propose a methodological framework along with computational methods to delineate the SPA of a given SBA. Our methods feature ESF carrier dynamics-based algorithms to determine the specific path of the ESF which is enabled by an adapted Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, as well as quantification approaches for evaluating the actual realization of ecosystem services value (RESV) with the support of the Revised Wind Erosion Equation (RWEQ) model. We demonstrate the applicability of our methods with the example of three mega-cities in northern China that are severely affected by the wind erosion problem. The results show that the spatial distribution of wind erosion source areas in the three cities are different, and the SPA based on the trajectory weights shows a decreasing trend from the city center to periphery, with those in the cities of Beijing and Taiyuan mainly concentrated in the northwestern direction, while the high RESV zones in the SPA are mainly concentrated in the desert areas. In the time series, wind erosion affecting these three cities mainly occurred in late April and May. The study provides a methodological and empirical basis for applied ecological analysis including the delineation of the ecological hinterlands of cities, or the formulation of precise ecological compensation programs. [ABSTRACT FROM AUTHOR]

Accurate prediction on influent wastewater quality is of great importance to energy saving and chemical dosage reduction of wastewater treatment plants (WWTPs). However, the existing methods ignore the data noise caused by water sensors working in harsh conditions and the intrinsic variable dynamics inherent in the time series of wastewater quality. To tackle this problem, we propose a novel approach called wt-ResLSTM (w avelet t ransform and Res idual L ong S hort- T erm M emory) to predict the influent wastewater quality. Specifically, we adopt wavelet transform and semi-soft thresholding to remove the noise from influent wastewater quality data adaptively. Then, we use autoencoder to learn the latent representation of the recent fluctuation of wastewater quality to capture its transient uncertainty. Further, the residual LSTM is adopted to learn both the long-term and short-term sequential dependencies of influent wastewater quality from the historical wastewater quality and the latent representation of its recent fluctuation. Experiments on the dataset from a large-scale urban WWTP in Beijing demonstrate that the proposed wt-ResLSTM approach outperforms state-of-the-art techniques in predicting influent wastewater quality in terms of level accuracy and directional accuracy. • This paper proposes a novel approach called wt-ResLSTM for the prediction on influent wastewater quality. • The wt-ResLSTM approach considers both noise removal and transient uncertainty in recent fluctuations of wastewater quality. • The wt-ResLSTM approach can be used to accomplish precise control of WWTPs for energy saving and chemical dosage reduction. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

A reasonable allocation of carbon emission quotas is essential to improving the national unified carbon trading market. A bankruptcy game model is developed for allocating carbon emission quotas to Chinese provinces based on bankruptcy theory and the Nash bargaining game. The model takes into account the disparities that exist among these provinces. The allocation scheme under the carbon peak target can be obtained by using the projection pursuit model and the subject satisfaction function. The findings reveal that certain eastern provinces bear a greater responsibility in terms of historical carbon emissions. Furthermore, the rampant transfer of carbon emissions from energy-rich to energy-demanding regions has exacerbated the imbalance in regional development. Interestingly, the carbon emission demands are fulfilled in some provinces situated in the coastal regions of the southwest and southeast, such as Beijing, Tianjin, and Fujian. However, certain provinces in the northeast express the lowest levels of satisfaction with their carbon quota allocation, necessitating a greater burden of carbon emission reduction in the future. The research offers significant implications for regional development and carbon reduction efforts. • A bankruptcy game model is developed for allocating carbon emission quotas. • The model takes into account disparities between the provinces. • The bankruptcy theory and Nash bargaining game are used in this paper. [ABSTRACT FROM AUTHOR]

The location of waste transfer station is a problem that has been studied in the previous literature, while no studies devote to considering the adjustment of the existing transfer station in the site selection process of waste transfer station to the best of our knowledge. However, in reality, a large number of existing transfer stations have a huge impact on the planning of new transfer stations. Therefore, this paper firstly adopts a location model based on mixed integer linear programming as the first attempt to consider the adjustment of existing transfer stations in the process of waste transfer station location. Next, a robust model is established by considering the uncertainty of waste production. And the budget of uncertainty is introduced to control the conservativeness. A case study in Beijing, China is given to illustrate the applicability of this model. The study finds that compared with the model without considering the adjustment of existing transfer stations, the investment cost of the deterministic model in this paper is significantly reduced, and the total cost is also reduced to a certain extent. Moreover, the solution of the robust model reveals the significant impact of waste output uncertainty on the location strategy of waste transfer station. • Adjustments of existing transfer stations are considered in the model. • Robust optimization is applied to deal with uncertain waste production. • The adjustment of uncertain budget makes the planning scheme more flexible. • A case study based in Beijing was conducted to prove the validity of the model. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper focused on stable urban heat island which is lack of attention in current studies. • This paper used morphological spatial pattern analysis to identify the key zones. • This paper obtained the causes and characteristics of the stable urban heat island. People residing near stable urban heat islands (UHIs) experience a high long-term heat exposure, and the health consequences of such islands may be more severe than those of an expanding UHI; however, these areas are hidden in the overall heat island and not focused on. We developed a novel approach to identify the locations and ranges of stable UHIs by considering multiple frequency urban heat islands (MFUHIs). The MFUHIs were identified through multi-temporal remote sensing data and morphological spatial pattern analysis (MSPA), and the approach was applied in the case area of Beijing, the capital city of China. Specifically, the MFUHIs in Beijing were classified according to the landscape characteristics, based on the field survey and remote sensing data. The landscape indicators included land-use types, biophysical parameters (normalised difference vegetation index (NDVI), proportion of water areas, and impervious surface area (ISA)) and 3-D metrics (building density and building heights), which were considered causative factors. The relationship between the land surface temperature and landscape indicators was examined using ordinary least-squares (OLS) regressions and the Area Weighted Contribution Index (AWCI). The results showed that the MFUHIs in Beijing have four key zones, in which the surface temperature is more than 9 ℃ higher than the average temperature of the urban area, and the air temperature is 3.3–8.5 ℃ higher than the built-up temperature in the non-UHI area. The OLS regression and spatial statistics of the landscape indicators and surface temperature indicated that the regions in which area where MFUHIs occur could be characterised by a relatively high degree of impervious surface coverage, lack of green space and water area, and presence of low-rise and high-density buildings. The average number of building-floors in MFUHIs was between 1.93 and 2.64, and the number of floors in the entire study area was less than 3.6. Moreover, the building-densities in the MFUHIs were 16.25% to 27.62% higher than that in the whole study area. According to the result of the AWCI and field survey, the MFUHIs were classified into transportation, historical block, commercial, and city village high-temperature centre. The main findings indicated that certain regions between the city centre and rural areas, in which sound planning is not implemented, are the hotspots of UHI mitigation and risk elimination regions. The proposed approach can help policymakers develop targeted mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

The waterlogging disaster has become a major threat to the resilient development of cities, which needs a unified and systematic accounting framework to estimate potential waterlogging effects in terms of forming disaster prevention strategies. In this paper, we developed an emergy-based waterlogging footprint accounting framework applying depth-damage functions, input–output (IO) analysis and ecological footprint, considering both economic and environmental factors. Taking the old town Beijing as a case study, we simulated the urban waterlogging distribution with a hydrodynamic model (InfoWorks ICM) over different return periods (1, 2, 3, 5, 10, 20, and 50 years) at high resolution. This study first considered the economic impact caused by urban waterlogging to evaluate the direct economic loss and indirect economic loss by using the depth-damage function and IO model, respectively. The environmental loss was assessed with an ecological footprint and was unified by emergy with economic loss. Then, four emergy-based indicators were established to quantify the specific impacts of waterlogging on urban resilience. The results show that the waterlogging footprints grew constantly by 284.57% from 1 year to 50 years. The waterlogging footprints driven by environmental loss, which were overlooked, accounted for 13.02% of the total. The waterlogging footprint density and waterlogging resilience index showed high spatial heterogeneity, indicating that the sensitivity of subdistricts to waterlogging varies greatly. Our study provides a useful energy-based tool for assessing urban waterlogging losses and supports decision-making for waterlogging disaster mitigation and risk zoning management. • An emergy assessment of hydrological-based urban waterlogging loss was first conducted. • Losses of natural capital due to urban waterlogging disasters were quantified. • Economic and environmental losses were incorporated. • Urban waterlogging resilience was explained in context of metabolism. [ABSTRACT FROM AUTHOR]