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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]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

• CO 2 air conditioning systems driven by compressed air engines are designed innovatively. • CO 2 air conditioning system driven by compressed air engine can significantly reduce running costs. • Combination of air conditioning system and compressed air storage is promising. Traditional building air conditioning uses electricity or thermal energy to drive the refrigeration cycle. The energy released by the expansion of compressed air can also be used for driving the refrigeration cycle. Moreover, the expanded low-temperature air can be used in the air conditioning system too. In this paper, three forms of CO 2 air conditioning systems driven by novel reciprocating piston compressed air engines were designed, namely EAGC (Exhaust Air for Gas Cooler), EAS (Exhaust Air for Sub-cooler), and EASC (Exhaust Air for Space Cooling), to analyze the performance and economics of CO 2 air conditioning systems combined with compressed air energy storage technology. Twenty-seven typical cities were selected in different climate regions in China to evaluate the systems' performance and running cost under outdoor temperature changes and different electricity prices. The results show that compared with the electricity-driven CO 2 air conditioning system, the three systems can greatly improve the system COP (Coefficient of Performance) but still cannot reach the performance level of the conventional freon refrigeration systems. However, in cities like Beijing and Shanghai with the large peak-to-valley electricity price difference, EASC system compared to the electricity-driven R410A air conditioning system running costs are lower, the average running cost per hour can be saved 11.96% and 16.35%, respectively. This means that the CO 2 air conditioning system with compressed air energy storage technology has a certain economic application value and can be popularized on reasonable occasions. This innovative system design can help solve the contradiction between supply and demand of building energy, realize leisure energy consumption, and reduce the economic cost of energy consumption on the demand side. [ABSTRACT FROM AUTHOR]

• This paper examines the integrated optimization of train stopping plan and seat allocation. • The integrated optimization problem is formulated as a mixed-integer mathematical model. • The integrated optimization approach improves efficiency against separate optimization of stopping plan and seat allocation. • Numerical studies were conducted on Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors. This paper examines the integrated optimization of train stopping plan and seat allocation scheme in railway systems, where equilibrium passenger travel choice under elastic demand is considered. The integrated optimization problem is formulated as a non-concave and non-linear mixed-integer mathematical model, where the objective is to maximize the system net benefit considering ticket revenue, consumer surplus, and cost associated with train stoppings. The integrated optimization model can be reformulated into a mixed-integer linear programming (MILP) model based on a series of linearization, relaxation, and outer-approximation techniques, which can then be solved by commercial MILP solvers (e.g., GUROBI). We also compare the integrated optimization approach with that when the train stopping plan and seat allocation are optimized separately and identify the potential benefits. Numerical studies have been conducted on a small-scale example, the Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors to illustrate the proposed model and solution approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • A solar energy system with hydrogen energy storage and alkaline fuel cell is proposed. • An optimization model base minimum life cycle cost is developed by Getopt. • The performance of solar energy system without and with electrical storage device are compared. In this paper, a novel solar energy system with hydrogen energy storage and alkaline fuel cell is developed in TRNSYS. The solar energy system without electrical energy storage and solar energy system with battery energy storage are established as the reference systems. The life cycle cost is chosen as the optimal objective, and different operating strategies are developed for the proposed system and the reference systems. The optimal variables and dynamic analysis can be obtained after optimization. Various evaluation criteria, including electricity coverage ratio, annual carbon dioxide emission and levelized cost of energy, are used to comprehensively assess the overall performance of the proposed system and the reference systems. Beijing and Shanghai, which belong to cold region and hot-summer and cold-winter region respectively, are chosen as the study sites for case study. The results show that the proposed system has the lowest life cycle cost, which is 186,484.36 USD and 261,467.43 USD in Shanghai and Beijing, respectively. The proposed system has the highest electricity coverage ratio (which is more than 0.5 for most time of the year), the lowest annual carbon dioxide emission (which is 19,099.34 m3 in Shanghai and 31,525.96 m3 in Beijing) and the lowest levelized cost of energy (which is 0.12 USD/kWh in Shanghai and 0.08 USD/kWh in Beijing). [ABSTRACT FROM AUTHOR]

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of urban development. The post-classification comparison (PCC) is a widely-used change detection method by classification and temporal comparison. For classification, image-level labeling is an efficient alternative to pixel-level one for pixel-wise weakly supervised segmentation, which frequently applies pixel-level pseudo labels generated from class activation map (CAM) to train semantic segmentation networks. CAM can be obtained from classification networks trained with image-level labels and can indicate the spatial location of objects. The existing studies are subject to the following issues: 1) They only rely on the single-scale and low-resolution CAM, but ignore the multi-scale property of BAs; 2) Pixel-level pseudo labels usually contain noises (e.g., omissions and false alarms); 3) The temporal correlation between multi-temporal images is less considered in PCC. To address these limitations, this paper proposed a multi-scale weakly supervised learning method, which utilized a large number of single-temporal high-resolution images and image-level labels to detect BA changes. This method consisted of three modules: 1) multi-scale CAM for BA pseudo label generation; 2) adaptive online noise correction for BA detection; and 3) generation of reliable pseudo labels for BA change detection. Based on ZY-3 images (2.5 m), we constructed the first multi-view datasets for both BA detection and change detection. Each ZY-3 image includes a multi-spectral image with red, green, blue, and near-infrared bands and a multi-view image with nadir-, forward-, and backward-views. The BA detection dataset contained 86,166 image-level samples (256 × 256 pixels for each sample), covering 48 major cities in China, while the BA change detection dataset consisted of ZY-3 bi-temporal images at rapidly urbanizing areas (i.e., Beijing and Shanghai). Experiments showed that the proposed method can detect BA changes and suppress pseudo changes effectively, yielding 88.2% F1-score in BA detection and 79.3% for Shanghai and 78.5% for Beijing in change detection. Further analysis demonstrated the proposed method to be advantageous in the following two fronts: 1) the image-level weak labels can achieve pixel-wise BA change detection at low cost; and 2) the multi-scale CAM and temporal correlation are effective in the scenarios with limited labels. Datasets and codes will be accessed at https://github.com/lauraset/MSWS. • A multi-scale weakly supervised method for built-up area change detection. • The first multi-view built-up area detection and change detection datasets. • Multi-scale class activation maps for generating pixel-level pseudo labels. • Adaptive online noise correction for obtaining reliable built-up areas. • Reliable change pseudo labels for training a change detection network. [ABSTRACT FROM AUTHOR]

• City-centric global multi-region input-output models for Beijing and Shanghai are developed. • Factor contribution and critical path analysis are adopted. • Sector aggregation effect varies across sectoral datasets. Sector aggregation under the input-output framework may lead to deviation in results and the lack of detailed sectoral information, which hinder the targeted implementation of accurate sectoral adjustment policies. This paper explores the effects of sector aggregation on the embodied carbon emission of the residential consumptions of Beijing and Shanghai based on the city-centric global multi-region input-output (CCG-MRIO) model. Integrating with structural path analysis and structural decomposition analysis, the impacts of sector aggregation on the identifying of critical emission transmission paths and driving forces are also revealed. Three sector aggregation datasets are discussed to track the sector aggregation effect in 2012 and 2017. The findings show that the carbon emission results at 8 sector level better align with the results at the 22 sector level than those at 32 sector level. Moreover, sector aggregation will lead to the absence of some critical carbon transmission paths, some factor contributions on carbon emission also changed their directions at different sector levels. On the basis of clarifying the sector aggregation effect on embodied carbon emission, the research results provide a reference for similar studies to select the appropriate level of sector aggregation, to realize the goal of simplifying the calculation or gaining more detailed sectoral emission information. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose variational mode decomposition optimized by capuchin search algorithm. • Propose a prediction model of optimized LSTM by pelican optimization algorithm. • Consider error sequence, and propose the error correction module CVMD-POA-LSTM. • Propose a new hybrid prediction model for PM2.5 concentration, named CEEMDAN-ApEn-CVMD-POA-LSTM-EC. • In this paper, all data and code links are shared on GitHub. With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the region. Therefore, a new hybrid prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), approximate entropy (ApEn), variational mode decomposition optimized by capuchin search algorithm (CVMD), long short-term memory optimized by pelican optimization algorithm (POA-LSTM) and error correction (EC), named CEEMDAN-ApEn-CVMD-POA-LSTM-EC, is proposed. First, CEEMDAN is used to acquire a limited amount of intrinsic mode functions (IMFs). Second, calculate ApEn value for each IMF component, and divide each IMF component into high-complexity and low-complexity components by the size of ApEn values. Third, variational mode decomposition optimized by capuchin search algorithm (CVMD), named CVMD, is proposed. CVMD is used as a secondary decomposition method to further decompose high-complexity components adaptively into a finite number of IMFs. Fourth, long short-term memory optimized by pelican optimization algorithm, named POA-LSTM, is proposed. POA-LSTM predicts all IMF components, and the results of their predictions are combined to generate the original prediction results. Final, error sequence is decomposed and predicted again by the EC module CVMD-POA-LSTM to obtain prediction results of error sequence, and final prediction results are acquired by combining original prediction results and prediction results of error sequence. The datasets in Beijing, Shanghai, and Xi'an were selected for simulation experiments to demonstrate the superiority of the proposed model. Taking Beijing as an example, RMSE, MAE, MAPE and R2 values are 1.9947, 1.5577, 0.1157 and 0.9947, which are superior to other comparison models and have the best performance. [ABSTRACT FROM AUTHOR]

Although urban land leasing is supposedly a local government matter, local leaderships' proactive pursual of economic growth for their career advancements can sometimes be at odds with national policies. Yet, how land conveyance decisions are made in midst of the implementation of national policies and land financing has been a rather overlooked aspect in the studies of China's land market. Hence, this paper explores this issue through a study of land conveyance decisions in Beijing and Shanghai's residential and commercial land markets from 2007 to 2018, and their subsequent land price effects. Deploying endogenous treatment effect models, it is revealed that land conveyance decisions in Beijing were highly subject to national policies and the status of its City Party Secretary, but not in Shanghai. Also, the use of Guapai resulted in higher residential and commercial land prices in Shanghai, but lower prices for Beijing's commercial land parcels. The findings indicate that Beijing, due to its closer proximity to the Central Government, appears to have stringently followed the national policy directions, whereas land officials in Shanghai seem to have had a higher degree of autonomy in their decisions. Additionally, the contrasting price effect of Guapai on land prices for residential and commercial land parcels enabled Beijing's government to compensate for the latent losses in land revenue when leasing residential land parcels, due to Nation #10, by leasing commercial parcels at higher prices via tender. [ABSTRACT FROM AUTHOR]

• Knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities. • The quality of this output has increased over time Chinese cities improve the quality of their knowledge output before increasing the quality of technology. • Only Beijing, Shanghai & Shenzhen compete globally in terms of quality of output – other Chinese cities do not. • This can be a mechanism for increasing regional inequality within China. Are Chinese cities becoming smarter in terms of the knowledge and technologies they produce? For decades, the widely held believe in the global North is that China merely copies, imitates and only incrementally improves ideas. Recently, this believe is increasingly being challenged. In this paper I quantify this process. Using data from about 6,1 million patents and 60 million academic publications I examine the complexity of technologies and knowledge developed in major Chinese cities and compare these to those produced in other global cities. The results show that (1) knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities; (2) the quality of this output has increased over time; (3) Chinese cities improve the quality of their knowledge output before increasing the quality of technology; and (4) only Beijing, Shanghai and Shenzhen compete with other global cities in terms of the quality of output, suggesting emerging regional inequality within China linked to knowledge and technology production. Not only is the size of the Chinese economy increasing, so is the quantity and quality of its knowledge and technology output. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

Agricultural sector is the basic industry supporting the construction and development of national economy. With the continuous deterioration of agricultural pollution, measuring and understanding agricultural total factor productivity (AGTFP) is the important premise to achieve agricultural green development and clean production. Regarding carbon emissions and agricultural non-point source pollution (ANSP) as undesired outputs, this paper applies a three-stage Data Envelopment Analysis (DEA) method combined with the Slack-Based Measure (SBM) model to eliminate the influences of environmental factors and random errors and explore the real AGTFP of 30 provinces in China from 2000 to 2017.On this basis, the spatial distribution and dynamic changes of AGTFP before and after adjustment are further discussed to seek the underlying reasons. The empirical results demonstrate that AGTFP is lower when carbon emissions and ANSP are both considered, and it decreases from east to west in StageⅠbut the Northeast region surpasses the East to achieve the highest productivity after removing the interferences. Moreover, China's AGTFP has been restricted by the external environment, and superior external environment disguises the poor management efficiency of Beijing and Shanghai. Therefore, some policy implications to balance agricultural environment and economic development are proposed. [Display omitted] • Non-point source pollution and CO 2 are integrated to measure agricultural green efficiency. • A three-stage SBM-DEA approach is employed to calculate the real agricultural green total factor productivity. • Different environmental factors have different effects on the slack of agricultural inputs. • China's agricultural green total factor productivity is restricted by the external environment. [ABSTRACT FROM AUTHOR]

• The net CO2 emissions of the Beijing-Shanghai high-speed rail are assessed. • The framework incorporates modal substitution and traffic generation effects. • Avoided infrastructure expansion and vehicle manufacturing are considered. • Large share of traffic diverted from ordinary-speed trains may raise emissions. This paper provides an ex-post assessment of net carbon dioxide (CO2) emissions of the Beijing-Shanghai high-speed rail (HSR) by including both mode substitution and traffic generation effects with a life-cycle approach. We are among the first authors to examine the emissions effects of a long-haul HSR and to consider the effects of avoided infrastructure expansion and vehicle manufacturing due to traffic diversion. We find that avoided infrastructure expansion plays a limited role in offsetting CO2 emissions from the construction of an HSR infrastructure, but the reduced demand for passenger vehicles can offset a larger share of emissions from the manufacturing of HSR rolling stock. Initially, too much traffic was diverted from ordinary-speed rail (OSR) to HSR, increasing emissions at the operation stage. As traffic diverted from road and air increased, the net emissions at the operation stage turned negative, offsetting emissions from infrastructure construction and vehicle manufacturing. [ABSTRACT FROM AUTHOR]