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• Industrial robots can significantly promote firm productivity. • The main mechanism comes from the improvement of human capital structure and innovation of firms. • Industrial robots are more likely to increase the total factor productivity of non-state owned and small-scale firm. • Industrial robots are more likely to increase the total factor productivity in firms that have political affiliations and low market concentration. • Industrial robots are more likely to increase the total factor productivity in firms that low financialization, and high managerial competence. Industrial robotics has become a driving force in the development of high-quality manufacturing enterprises. This paper investigates the impact of industrial robotics on the total factor productivity of enterprises and its mechanism of action through the use of data from A-shares of Chinese companies listed on the Shanghai and Shenzhen exchanges from 2007 to 2019. We found that the use of industrial robots significantly increases the total factor productivity of enterprises, findings that still hold when Bartik instrumental variables are constructed. The mechanism of action suggests that industrial robot application improves both the human capital structure and the innovation capability of enterprises, thus increasing total factor productivity. Heterogeneity analysis shows that industrial robot applications are more likely to increase total factor productivity in firms that are non-state-owned and small scale and have political affiliations, low market concentration, low financialization, and high managerial competence. The findings of this study will help to accurately assess the productivity effects of industrial robotics, and the government should continue to promote the integration of industrial robotics and manufacturing development and actively cultivate independent innovation capabilities and professional skills in the field of industrial robotics in order to increase total factor productivity in enterprises. [ABSTRACT FROM AUTHOR]

The urgency of addressing climate change and reducing carbon emissions necessitates the establishment of effective carbon markets. Our paper focuses on assessing and interpreting the efficiency of carbon markets, which is a crucial aspect of their successful implementation. We selected Hubei, Shenzhen, and Guangdong carbon pilots as examples to measure their dynamic information efficiency by combining the rolling time window and wild-bootstrap variance ratio test. Furthermore, we combined SHapley Additive exPlanations (SHAP) and Extra Trees (ET) regressor to reveal the nonlinear relationship between market performance and efficiency. Our findings highlight the time-varying nature of carbon market efficiency and reveal that China's carbon market has not yet achieved stable weak-form efficiency. Additionally, we identified excessive trading volume volatility as a significant constraint on market efficiency, while the trading volume itself exhibits weak explanatory power. Our proposed hybrid model demonstrates superior predictive and explanatory capabilities, offering regulators a valuable tool for continuously monitoring and interpreting carbon market efficiency. Finally, we suggested that reducing trading volume volatility should be a major concern for regulators to optimize market operations at present. [ABSTRACT FROM AUTHOR]

The "regional advantage" hypothesizes that inference uncertainty/prediction error of geotechnical or geological properties at a target site (a site contains a group of records measured from different locations/depths using a variety of tests) can be smaller if we use a quasi-regional cluster (includes two or more database sites with geotechnical or geological properties similar to the target site) instead of the entire database. A tailored clustering enabled regionalization (TCER) framework has been proposed to verify this "regional advantage" hypothesis. TCER requires the target site should not be an outlier site relative to the database. However, it remains a challenge on how to detect an outlier site (or data group) from a database. In this paper, we modify the original TCER by introducing a novel outlier site detection step called maximum site similarity (MSS) into the original TCER. The capability of MSS is verified using synthetic and real examples. Additionally, three inference methods [e.g., probabilistic multiple regression (PMR), classical Bayesian model (CBM), and hierarchical Bayesian model (HBM)] are studied for the purpose of determining the optimal inference method for the modified TCER in terms of achieving the minimum inference uncertainty/prediction error with reasonable computational time. It is shown that the modified TCER with CBM outperforms other inference methods for the examples shown in this paper. • An efficient method that addresses a non-traditional class of outlier site detection problems is proposed. • The proposed outlier detection method is added to the original TCER framework for screening appropriate database. • A Shenzhen clay database named SZ-CLAY/7/3426 is compiled to validate the modified TCER framework. • Three inference methods are applied in the modified TCER framework and their inference uncertainties are compared. [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]

[Display omitted] • STPA-VIKOR method was proposed to assess settlement risk during slurry shield tunneling. • STPA method was used to identify risk factors. • Sensitivity analysis of VIKOR method improves the accuracy of the model. • Measured results of Chunfeng tunnel validates the proposed model. • Proposed model was imported into BIM platform to guide project management. This paper introduces an integrated approach for assessing and managing the risk of ground settlement induced by slurry balance shield tunnel construction. The methodology combines the System Theoretical Process Analysis (STPA) and the Visekriterijumsko Kompromisno Rangiranje (VIKOR) methods. STPA is used to identify potential factors contributing to ground settlement, and while VIKOR calculates the Q -value related to the risk grade. By fitting the field measurements and corresponding Q -values, a classification principle for risk level is derived. The efficacy of the model is demonstrated through an analysis of the Chunfeng tunnel in Shenzhen. The quadratic fitting results are used to classify the risk level. and the results of risk classification are as follows: Risk level I: Q < 0.57, II:0.57 ≤ Q < 0.74, III: Q ≥ 0.74. Yielding results are consistent with on-site settlement monitoring. To enhance practical application, the model is integrated into the Building Information Modelling (BIM) platform of the Shenzhen Chunfeng tunnel, enabling 3D visualization-based management. Ultimately, this proposed model offers valuable insights and methodologies for the assessment and management of ground-settlement risks. [ABSTRACT FROM AUTHOR]

Both state-led and market-led institutional arrangements have been experimented in China to form collaborative governance among diverse stakeholders in participatory urban regeneration. Relatively less is known about how institutional arrangements as structural constraints impact the formation of collaborative governance. Building upon Giddens' structuration theory and collaborative governance theory, this paper develops a novel framework that converges on the reciprocity between structural and agency elements and applies it to two residential regeneration cases in Shenzhen. The findings reveal that in residential regeneration, the state-led institutional structure has a greater capacity than the market-led structure to create relational links for community participation. Various factors, including variations in land property rights, path dependencies and institutional certainties, explain the findings. The formation of collaborative governance relies on the shaping effect of structural elements on agents' behaviors in institutional design, including well-designed rules to regulate sanctions, constitute common meaning and premium-allocated resources in accordance with stakeholders' responsibilities. These findings contribute to a better understanding of collaborative governance and help improve participatory urban regeneration policymaking in China. [ABSTRACT FROM AUTHOR]

Grouting lifting is the effective approach for treating differential settlement of existing tunnels, but it easily deteriorates the tunnel structure again and seriously threatens the operational safety of urban rail transit. Aiming to solve the above problem, this paper considers the interaction between the tunnel and the foundation, and establishes a series of analytical solutions of the stress, strain, axial force, and bending moment of the tunnel under single point grouting. Based on the characteristics of shield tunnel correction engineering and the analytical solution under single point grouting, the equation of the internal force and deformation of tunnel structure under multi-point grouting considering different grouting angles and different strata is obtained. The grouting correction engineering case for Shenzhen Metro Line 1 shows that the analytical solution to the displacement of the tunnel structure is well matched with the measured displacement of the tunnel structure is well matched with the measured data of the case and the difference is less than 15 %. The parameter analysis results indicate that the main loaded area of the tunnel under single point grouting is related to the stratum, and the main loaded area of the soft soil stratum is - 42. 4 ° + 42. 4 ° around the grouting point. Convergence deformation of the tunnel under multi-point grouting reaches the maximum value at the diameter passing through the center of the grouting zone and the minimum value at the diameter which is perpendicular to the former. Under the condition of limiting horizontal convergence deformation, the reasonable grouting angle range of the multi-point grouting zone is about 120 ° 150 °. This paper provides a theoretical basis that can immediately evaluate the mechanical structure performance and to determine whether to impose greater pressure when the tunnel is continuously lifted. [ABSTRACT FROM AUTHOR]

This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN-based trajectory predictions are often considered in a single traffic scene without time attributes, complete interaction information, dynamic graph-based model, etc. Time and interaction aware models are more challenging than the existing ones. Despite very well does the graph-based model describe the relationship between driving vehicles, the critical problem in the traffic scene is how to deeply explore the spatio-temporal characteristics of dynamic graphs. Therefore, a novel dynamic graph and interaction-aware neural network model called as DGInet is proposed by combining a semi-global graph mechanism and an M-product based graph convolutional network, which are built into novel dual-network architecture in the entire model. The DGInet is built by exploiting the dynamic interaction in depth between driving vehicles in urban traffic scenarios, and then realized by utilizing semi-global graph convolution operations on the input data cell to capture the basic spatial interaction features of the driving scene. Meanwhile, the dynamic graph is further extracted by a novel M-product approach, in which the embedding of the model is then established along with the embedding of the semi-global network to perform the final embedding. Extensive experiments have been conducted on the two public datasets, named NGSIM and Apollo respectively, to show that our approach outperforms the existing ones with better performance and less computing time. Besides the real-world Shenzhen traffic dataset, China, is also developed to verify the effectiveness of our approach. • We propose a novel vehicle trajectory prediction approach called as DGInet. • This work fully leverages the current popular Graph Convolutional Networks (GCNs). • The DGInet features a M-product based GCN module to process dynamic graphs. • The DGInet can efficiently extract spatio-temporal features in driving scenes. • This work conducts comprehensive experiments on two well-known traffic datasets, and a real-world Shenzhen traffic data, China. [ABSTRACT FROM AUTHOR]

Vector cellular automata (VCA) are effective models for cadastral-scale land use change modeling, leveraging fine spatial granularity information from cadastral plot data. The temporal dimension has the potential to improve the performance of VCA further. However, it is challenging to precisely capture long sequence information of cadastral plot temporal data for VCA while ensuring accurate capture of fine granularity information simultaneously. Our paper introduces the Temporal-VCA framework, which fully utilizes fine spatial and temporal granularity information of cadastral plot temporal data to enhance the accuracy of VCA. Applying Shenzhen's annual cadastral plot data from 2009 to 2014, this study shows how deep learning techniques can elucidate the temporal aspects of VCA models. Temporal-VCA notably improves precision by up to 22.12 %, outperforming the regular VCA models and traditional raster CA models. It reveals the complex nonlinear temporal patterns within cadastral-scale urban development processes. Designed simulations for 2030, including scenarios of disordered development and ecological protection, highlight the benefits of fully leveraging fine temporal granularity information of temporal data into urban planning, potentially reducing ecological damage by 70 %. Our findings offer a novel methodology for urban land use simulation, with significant implications for urban planning and the advancement of sustainable cities. • Temporal-VCA enhances cadastral land use modelling by improving VCA's long sequence data acquisition • Our model simultaneously utilizes the fine spatial granularity and time series data of cadastral data • Policy scenarios are designed to optimize future land resources use by evaluating policy impacts. [ABSTRACT FROM AUTHOR]

• A dual-camera computer vision monitoring system is proposed and applied in displacement monitoring of an underground prefabricated subway station for 5 months during construction and post-construction phases. • Field calibration experiment is carried out using movable targets equipped with spiral micrometers and the measurement errors are discussed. • The potential correlation between uneven displacement and some minor structural defects, as well as the temperature-induced deformation fluctuations are investigated. The increasing applications and structural vulnerability of prefabricated metro stations have resulted in a growing demand for monitoring their displacements during the construction and post-construction phases. This paper proposes a dual-camera computer vision monitoring system consisting of a dual-camera base station, a series of monitoring prisms and two reference prisms. The displacement and rotation of the camera station are considered to reduce measurement errors caused by camera motion. The system has been applied in the displacement monitoring of the 164 m assembly section of an underground station in Shenzhen during the construction and post-construction periods. A movable target equipped with a spiral micrometer is used for field calibration experiments, and the results show that the average measurement deviation is 0.01 mm and the standard deviation is 0.068 mm. During the 5-month monitoring, displacement caused by backfilling, rainfall, and other construction conditions are precisely reflected, which shows a potential correlation between uneven displacement and some minor structural defects. At the post-construction period, settlement variance is relatively small, and the temperature effect causes horizontal convergence to fluctuate. [ABSTRACT FROM AUTHOR]

In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

This paper traces the plunge and rebound of the taxi market in Shenzhen, China through the COVID-19 lockdown. A four-week taxi GPS trajectory data set is collected in the first quarter of 2020, which covers the period of lockdown and phased reopening in the city. We conduct a spatiotemporal analysis of taxi demand using the data, and then select taxis that continued to operate through the analysis period to examine whether and how they adjusted operational strategies. We find, among other things: (i) the taxi demand in Shenzhen shrank more than 85% in the lockdown phase and barely recovered from that bottom even after the city began to reopen; (ii) the recovery of taxi travel fell far behind that of the overall vehicle travel in the city; (iii) most taxis significantly cut back work hours in response to the lockdown, and many adjusted work schedule to focus on serving peak-time demand after it was lifted; (iv) taxi drivers demonstrate distinct behavioral adaptations to the pandemic that can be identified by a clustering analysis; and (v) while the level of taxi service dropped precipitately at the beginning, it quickly rebounded to exceed the pre-pandemic level, thanks to the government's incentive policy. These empirical findings suggest (i) incentives aiming at boosting supply should more precisely target where the boost is most needed; (ii) the taxi market conditions should be closely monitored to support and adjust policies; and (iii) when the demand is severely depressed by lockdown orders or when the market is oversupplied, taxi drivers should be encouraged and aided to use more centralized dispatching modes. [ABSTRACT FROM AUTHOR]

Multi-layer networks could reveal the carbon emission structure of urban traffic formed after residents choose the means and purpose of trips. In this paper, a multi-layer network model was proposed and the carbon emission characteristics of urban trips were analyzed. In addition, the carbon reduction potential assessment methods based on nodes and edge feature indexes to identify the carbon reduction areas of the trip network. An empirical study was carried out on Shenzhen and the results showed that: 1) The carbon emission in Shenzhen is unbalanced in spatial and is dense in the west and sparse in the east, but the carbon emission of different networks shows a similar fluctuation trend over time; 2) multi-layer network represents community structure, while communities of "residence-enterprise" network and "residence-park" network are internally closely connected; 3) the carbon emission reduction potential of residential nodes is low in the west and high in the east. The mode of the urban trip and the law of geographical space connection expressed by it were understood by establishing a multi-layer network embedded in geographic space in this paper. The conclusions hereof are of supportive significance for the formulation of space emission reduction policies. • The multi-layer networks that characterize residents' diversified trips was establish. • Carbon emission estimation method considering parameters of road type and speed class was proposed. • The correlation between residents' trips modes and carbon emissions was depicted. • The carbon emission network for residents' trips present community structure and spatial variation. • Differentiated zoning strategies were discussed based on carbon emissions and reduction potential. [ABSTRACT FROM AUTHOR]

Shenzhen is one of the world's cities with the fastest urbanization and the most astonishing changes, and it is also a typical benchmark of Chinese rapid urbanization miracle. Under the tide of neoliberalism and globalization, the central government gave Shenzhen the priority of reform and experimentation first, then it has been rapidly pushed to the front stage as a test field for China's reform and opening up. Shenzhen has become the world's first-tier city in a short time with its location advantage and high-quality talent attraction, as well as active private economy and scientific and technological innovation. Through the multi-dimensional data analysis of the great changes during Shenzhen's urbanization, and a summary of the advantages of the Shenzhen model, this paper systematically answers the question that why Shenzhen can achieve urbanization so quickly. Furthermore, it concludes challenges of Shenzhen's future development and discusses the inspirations of the Shenzhen model for other cities. Based on the above analysis, this paper constructs an organic framework comprehensively covering achievements, characteristics, challenges, and inspirations of Shenzhen's rapid urbanization model. In the future, Shenzhen needs to transform from "Shenzhen Speed" to "Shenzhen Quality" to realize sustainable high-quality urbanization. • The drastic changes of Shenzhen's urbanization in the past 40 years from three dimensions of population, economy and land. • Five prominent characteristics of Shenzhen's urbanization development model. • Summary of three challenges of Shenzhen's future sustainable development. • Some inspirations from Shenzhen model for other cities, especially global southern cities, to learn and replicate. • A comprehensive framework covering achievements, model, challenges, and inspirations of Shenzhen's urbanization development. [ABSTRACT FROM AUTHOR]

• We focus on three locations in the Asia-Pacific region—Hong Kong, Shenzhen, and Singapore. • We analyze the impact of variations in research and development (R&D) spending on total factor productivity (TFP) growth. • In each of the three cases, we compare and contrast the role of public and private R&D in boosting TFP growth as well as the pattern of causality between public and private R&D and the capacity of both types of R&D to generate economic spillovers. • We show that the impact of both private and public R&D varies across the three cases, with significant but limited outcomes for TFP growth in the case of Hong Kong, no significant growth in Shenzhen, but strong and positively significant growth in Singapore. In this paper, we focus on three locations in the Asia-Pacific region—Hong Kong, Shenzhen, and Singapore—to analyse the impact of variations in research and development (R&D) spending on total factor productivity (TFP) growth. In each of the three cases, we compare and contrast the role of public and private R&D in boosting TFP growth as well as the pattern of causality involving public and private R&D and the capacity of both types of R&D to generate economic spillovers. Our results show that the impact of both public and private R&D varies across the three cases, with significant but limited outcomes for TFP growth in the case of Hong Kong, no significant growth in Shenzhen, but strong and positively significant growth in Singapore. [ABSTRACT FROM AUTHOR]

The Yangtze River Major Conservation (YRMC) Action has been strategically crafted to catalyze industrial evolution within the Yangtze River basin, aligning with China's goal of achieving carbon neutrality by 2060, particularly from the corporate perspective. Despite its significance, the potential impact of this initiative on corporate environmental performance (CEP) remains understudied. To address this gap, we utilize panel data from Chinese firms listed on the Shanghai and Shenzhen A-shares (2012–2020) and employ the difference-in-differences (DID) method to investigate the causal relationship between the YRMC Action and the CEP. The main findings are threefold: (i) Firms located within a 100 km radius of the Yangtze River's main stream have exhibited an average increase of 0.1497 units in CEP due to the implementation of the YRMC Action, which are consistently validated across robustness tests; (ii) Mechanisms driving this enhancement include incentivizing firms to undertake green technological R&D programs, securing governmental green R&D subsidies, and credit incentives; and (iii) Heterogeneity analyses show that firms closer to the main stream of the Yangtze River, particularly those upstream and led by senior executives with awareness of green initiatives, have contributed to more pronounced improvements in CEP. This paper highlights policy implications for optimizing the incentive effects of the YRMC Action and strengthening the environmental regulatory governance to ensure enduring enhancements in CEP. [ABSTRACT FROM AUTHOR]

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes. • The flexible airport bus problem with last-mile ride-sharing services is introduced. • An arc-based mixed-integer programming model is proposed. • A logic-based Benders decomposition math-heuristic algorithm is designed. • An adaptive large neighborhood search algorithm is developed. • Numerical experiments using instances derived from Shenzhen airport are conducted. [ABSTRACT FROM AUTHOR]

In this paper, a CdTe photovoltaic double-skin ventilated window integrated with vacuum glazing (VPV-DSV) is proposed. The mathematical model of VPV-DSV was developed and validated through tests in Hefei, China. Annual energy performance and the economic analysis of the VPV-DSV was evaluated and compared with the other two PV-DSVs and an insulating glazed window (IG) under climate conditions of five selected cities, including Changchun, Hefei, Kunming, Shenzhen, and Shijiazhuang. Moreover, effects of window design factors, including the window-to-wall ratio, PV coverage, and pillar spacing of the VG, were explored. Results indicated that: (1) the VPV-DSV produced the best energy saving results and the least energy consumption. (2) NECs of the VPV-DSV in the five cities above was 955.05 kW•h, 464.31 kW•h, 110.66 kW•h, 53.71 kW•h, and 443.14 kW•h, respectively. (3) Due to high cost of the VG, the VPV-DSV only had the best economic performance in Shenzhen and the NPV was 3931.47 CNY. (4) The WWR and PV coverage had obvious impacts on the overall performance of the VPV-DSV, while the pillar spacing only had a more significant impact on the air conditioning energy consumption. [ABSTRACT FROM AUTHOR]

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy themselves by analyzing changing patterns between the traffic condition and signal timing plans. Multi-intersection traffic signal control problems face more challenges than classical single intersection problems such as the dimensionality issue as the joint action space of multiple agents grows exponentially with the number of intersections. The existing state-of-art multi-agent reinforcement learning algorithms developed for other areas may not suit well with traffic signal control given the complex spatial–temporal nature of traffic flows. In this paper, we propose a novel multi-agent counterfactual actor–critic with scheduler (MACS) framework for multiple interactions. In this method, decentralized actors control the traffic signals and the centralized critic combines recurrent policies with feed-forward critics. Additionally, a scheduler module that exchanges information among agents helps the individual agent better understand the entire environment. The proposed method was evaluated using simulation experiments based on a real-world urban street network in Shenzhen, China. Results showed that the proposed method outperforms the classic model-based method and several existing RL-based methods according to different measures such as queue length, average travel time delay, and throughput. • Develop an AI-based signal control framework to collaborate multiple intersections. • The counterfactual baseline can well assign the credit among multiple intersections. • The scheduler module helps agents better understand the entire environment. [ABSTRACT FROM AUTHOR]

Understanding how municipal solid waste (MSW) classification influences the physiochemical properties and incineration emission characteristics of MSW is imperative for sustainable waste management. To investigate how long-term changes in the composition and physical properties of MSW resulting from MSW classification affect the emissions of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and multiple flue gas pollutants, a five-year experiment (2018–2022) was conducted in a full-scale MSW incinerator in Shenzhen City, China. After MSW classification, the food waste in MSW was substantially reduced while the paper, textile, and wood components were notably increased. MSW classification also remarkably decreased the water and chlorine contents of MSW while concomitantly increasing the wet-based calorific value, sulfur content, and nitrogen content. Consequently, the PCDD/Fs emissions declined from 106.7 ng I-TEQ/t MSW in 2018 to 26.0 ng I-TEQ/t MSW in 2022. Moreover, a process-tracing experiment demonstrated noticeable decreases in PCDD/F synthesis in the post-combustion area, with mass-reduction efficiencies of 23.6 %–95.4 %. In a correlation analysis, the PCDD/F emissions were significantly positively correlated with food waste, water content, and chlorine and negatively correlated with the wet-based calorific value and nitrogen content. Furthermore, MSW classification reduced the emissions of conventional flue gas pollutants (SO 2 , CO, and HCl) and multiple heavy metals (total sum of Sb, As, Pb, Cr, Co, Cu, Mn, and Ni). In summary, the long-term study demonstrated that MSW classification can feasibly separate incombustible waste, improve combustion quality, and reduce pollutant emissions from sources. These findings offer practical guidance for promoting MSW classification. [Display omitted] • MSW classification remarkably changes composition and physical properties of MSW. • MSW classification mitigates PCDD/F emission from 106.7 to 26.0 ng I-TEQ/t MSW. • MSW classification effectively reduces the emission of various flue gas pollutants. • The long-term experiment demonstrated the feasibility of MSW classification. [ABSTRACT FROM AUTHOR]

Although studies have explored the travel behavior of residents in informal housing communities in recent years, few have been conducted in the context of a time series before and after the outbreak of COVID-19. This paper tracks individual-level daily mobility response for three months, using an interrupted time series (ITS) quasi-experimental design to infer daily causal effects of COVID-19 and community close-off management on individuals' daily travel distances. This study compares the mobility response to COVID-19 between people living in urban villages and commodity housing communities in Shenzhen, China. The results reveal that urban village residents reduced their travel less, with slower response and recovery rates after the COVID-19 outbreak. The community close-off management was less effective for urban village residents, with a smaller abrupt reduction in mobility and their recovery rate increasing faster during the post-lockdown period. Therefore, urban village residents may have experienced greater limitations in adjusting travel with higher exposure risks during the pandemic, compared with commodity housing residents. The variations in mobility change are significantly affected by some inherent differences between these two groups, even after controlling for their socioeconomic and built environment features. These results provide important insights for designing mobility restriction policies for different housing groups and planning more resilient neighborhoods in the post-COVID era. • Adopting an interrupted time series quasi-experimental design to infer daily causal effects of COVID-19 • Comparing mobility effects of COVID-19 in urban villages and commodity housing communities • Urban village residents reduced their travel less, with slower response and recovery rates. • The community close-off management was less effective for urban village residents. [ABSTRACT FROM AUTHOR]

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources. • Real trajectory data with 19,900 ETs is used for V2G potential and resilience analysis. • A method infers charging patterns, energy use, and reconstructs State of Charge is proposed. • A general method framework for modeling ET-V2G system on supply side is proposed. • An agent-based model is introduced for ET-V2G system modeling and simulation. • ET-V2G system can supply up to 60 MW and recover within 2–3 h during peak periods. [ABSTRACT FROM AUTHOR]

Detecting the changes of buildings in urban environments is essential. Existing methods that use only nadir images suffer from severe problems of ambiguous features and occlusions between buildings and other regions. Furthermore, buildings in urban environments vary significantly in scale, which leads to performance issues when using single-scale features. To solve these issues, this paper proposes a fused feature pyramid network, which utilizes both color and depth data for the 3D verification of existing buildings 2D footprints from oblique images. First, the color data of oblique images are enriched with the depth information rendered from 3D mesh models. Second, multiscale features are fused in the feature pyramid network to convolve both the color and depth data. Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings. Experimental evaluations using both the ISPRS benchmark datasets and Shenzhen datasets reveal that the proposed method outperforms the ResNet and EfficientNet networks by 5% and 2%, respectively, in terms of recall rate and precision. We demonstrate that the proposed method can successfully detect all changed buildings; therefore, only those marked as changed need to be manually checked during the pipeline updating procedure; this significantly reduces the manual quality control requirements. Moreover, ablation studies indicate that using depth data, feature pyramid modules, and multi-view voting strategies can lead to clear and progressive improvements. [ABSTRACT FROM AUTHOR]

• A concept called "zero waste construction site (ZWCS)" is articulated; • ZWCS includes meaning, system boundary, assessment period and operation strategies; • Prospects and challenges pertaining to ZWCS are also explored; • An analytical framework for ZWCS is finally developed. Around the global construction industry, there is an emerging trend to pursue a "zero-waste" goal at the site level, but little is known about it. This paper aims to shed insights on the waste management concept of "zero waste construction site" by delineating its meaning, system boundary, assessment period, and operation strategies, which are further formulated in an analytical framework. Owing to the nascent nature of the concept, we adopted a qualitative approach including archival study, a series of semi-structured interviews, and two in-depth case studies in Shenzhen, China to sketch the analytical framework. Meanwhile, an analogy between "zero waste construction site" and "net zero building" is continuously made to fine-tune and finalize the framework. This research demonstrates that the zero-waste goal is challenging but achievable on individual construction sites. The system boundary to examine the zero-waste goal is contingent on the project scope, be it a new construction, renovation, or demolition project. The assessment period is dependent on the duration of the construction project. However, it would be too costly, if not entirely impossible, to achieve a "zero waste construction site" by treating it as a closed system. Rather, one needs to consider open, off-site strategies, e.g., engaging third-party recycling services, reusing recycled materials in subsequent projects, or trading it in to a recycled material market. The analytical framework can be utilized to scrutinize existing construction waste management practices. In the long term, the research will contribute positively to a "zero waste" society. [ABSTRACT FROM AUTHOR]

Corporate innovation legitimacy is the key guarantee for listed companies to obtain returns from innovation activities. The purpose of this paper is to investigate the effect of analyst coverage on corporate innovation legitimacy based on effective supervision hypothesis and market pressure hypothesis. Moreover, it examines the moderating effect of analyst coverage characteristics, individual corporate behavior, external governance mechanism on the relationship between analyst coverage and corporate innovation legitimacy. The hypotheses are tested using a data set of Shanghai and Shenzhen A-share listed companies from China Research Data Service Platform (CNRDS) database, Hexun.com and CSMAR database. To overcome sample selection bias and reverse causality, this paper conduct robustness test using propensity score matching, lag variable, substitution variable and sample reduction to ensure the reliability of the research conclusions. Findings show that analyst coverage can effectively improve the innovation legitimacy of listed companies. In addition, based on the heterogeneity of the above relationship in different contexts, further research finds that the depth of analyst tracking, and the assessment of analyst optimism strengthen the positive impact of analyst coverage on innovation legitimacy. The implementation of green social responsibility brings a good reputation for the company, thereby helping analysts to focus on improving the legitimacy of innovation; In the market environment with relatively perfect external governance mechanism, the stronger the effect of analyst coverage on corporate innovation legitimacy. Prior studies mostly considered the R&D investment and other factors to overlook how other dimensions influence companies from the perspective of quantity. The research conclusion confirms the significance of analyst coverage as an external corporate governance mechanism, further enriches the theoretical research framework of innovation legitimacy, and has certain reference value for the improvement of innovation quality of listed companies. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Timely and reliable land-use/land-cover (LULC) change dynamic monitoring is the basis of urban understanding and planning. However, either the training sample shortage or the error accumulation in the multi-temporal processing inevitably restricts the monitoring performance. In this paper, to overcome these problems, we present a label-noise robust active learning method, which automatically collects reliable and informative samples from the images and builds a unified classification system with these augmented samples. In more detail, a Bayesian sample collection process that fuses the unsupervised transition information and the multi-temporal land-cover information is designed to provide candidate samples with "from-to" labels. A reliability-based multi-classifier active learning method is then proposed to adaptively allocate the more reliable samples to the classes that are difficulty to classify. Finally, a fusion of the multiple multi-date classifications trained by the selected samples is implemented to identify the change type of interest. The dynamic monitoring results for Shanghai, Shenzhen, and Shiyan in China, two megacities with rapid and obvious urbanization and a small city with relatively slow urbanization, indicate that the proposed method achieves a significantly higher accuracy than the current state-of-the art methods. The sample accuracy verified by the high spatial resolution reference maps endorses the applicability of the sample collection, while the reliability-based active learning further ensures the robustness of the proposed method in the label-noise situation. The presented method was tested in two difficult situations (a small training sample case and a training sample set without joint labeling), so that the robustness and accuracy of the approach can be expected to be of a similar or better quality in cases with more training samples. Given its effectiveness and robustness, the proposed method could be widely applied in LULC change dynamic monitoring. [ABSTRACT FROM AUTHOR]

• Context-specific built environment and transit travel behaviour of urban villages are measured in each village unit. • Total, direct and indirect effects of built environment on transit travel are revealed. • Residents in urban villages use less public transit when residential density increases. • Mixed land use has no significant effect on transit travel behavior. Context-specific research are necessary to promote public transit by optimizing the built environment in the process of urban renewal. Using data of residential travel survey in Shenzhen, China in 2014, this paper investigated how built environment affected transit travel in urban villages where low-income group resided, and they are all tenants. Structural equations modelling was employed to express the effects of socio-economic, public transit service related and built environment variables on travel mode choice, travel time and distance, with control for residential self-selection. It is found that after considering the self-selection effect, the influence of density and transit availability on travel distance, travel time and transit mode choice in urban villages are still significant. For urban villages, transit availability matters greatest for transit promotion, but the negative effect of density on transit mode choice and none effect of mixed land use are unusual as previous findings. This is partly because of relatively low income, low car ownership, and less elastic travel demand of tenants living in urban villages. Our findings provide some insights into transit-oriented urban renewal that when transforming urban villages, emphasis should be put on enhancement of transit availability, and the mixed land use could be put in the last consideration when there is limited time and funds. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

• Establish dual-platform MDP framework for optimizing hybrid bike-sharing platforms in the duopoly market. • Consider dynamic operation strategies (i.e., pricing and rebalancing) and battery level variation of ebikes. • Adopt modified D3QN algorithm for solving the game in the real-world case. • Gain managerial insights into the introduction of ebikes. The advent of electric bikes, or ebikes, has significantly enhanced competitiveness of bike-sharing systems, providing benefits to both riders (comfort during uphill and long-distance rides), platforms (more profit), and the environment. Operating such a hybrid bike-sharing system, i.e., with both bikes and ebikes, in a competitive multi-platform market, can be challenging due to the complex and unpredictable interplay among heterogeneous market participants, which becomes more pronounced with the ebike varying battery, and dynamic demand. Most related research is predicated on the assumption of a monopoly market, which is not always the case: in worldwide capital-oriented markets, many firms will quickly imitate and join in rapidly developing fields for profits. Thus, this paper addresses platforms' hybrid bike-sharing system operation problem with time-varying ebike pricing and rebalancing strategy in consideration of competition. We consider two docked hybrid bike-sharing platforms with charging stations at the site. Platforms utilize trucks for their own rebalancing operation including bike, ebike and mixed bike/ebike relocation tasks. We combine the Markov decision process (MDP) model with game theory, and establish the dual-platform MDP framework in which one mainstream platform and one competing platform optimize their profits by dynamic pricing and bike/ebike rebalancing based on highly dynamic and stochastic demand. Users' choice is described by a modified nested logit model and the endogenous demand is generated. We develop the tailored double dueling deep Q-network for solving dynamic gaming. A series of experiments are conducted based on the real-world dataset in Shenzhen and several strategy combinations are compared. The results show the win–win situation where both platforms improve profits with a higher market ratio and demonstrate how to introduce and operate ebikes in the system by analyzing detailed strategies in different games. [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]

• Shield soil dregs were prepared as synchronous grouting material based on the special treating agent. • The curing mechanism of the soil dregs synchronous grouting material was analyzed. • The use of 1 t soil dregs synchronous grouting material can reduce 59.05 kg CO 2 emission. Shield tunnel construction will produce a large amount of soil dregs. Transporting soil dregs directly to landfills raises costs and CO 2 emissions. In this paper, the shield tunnel of Intercity from Shenzhen to Huizhou is taken as an example, based on the special treating agent of shield soil dregs, the soil dregs were prepared as synchronous grouting material. The working performance and mechanical properties of soil dregs grouting material were studied. The optimal dosage of the treating agent was proposed, and the curing mechanism of the soil dregs synchronous grouting material was analyzed. The soil dregs grouting material was applied to practical engineering, and the application effect was verified by surface subsidence monitoring. The results showed that the setting time, bleeding rate, stone rate and compressive strength of the soil dregs grouting material were better than cement mortar grouting material. The treating agent realized the cementation of soil particles through the synergistic effect of epoxy resin and nano-SiO 2 , and the optimum dosage is 0.02 % of the solid component. The maximum surface settlement of the soil dregs grouting construction section was 10 mm, which is smaller than the maximum surface settlement of the cement mortar grouting section. Through the life cycle assessment (LCA) method, the CO 2 emission reduction capacity of the soil dregs synchronous grouting material was analyzed. The soil dregs grouting material reduced the production and transportation of fly ash and bentonite, and reduces the outward transportation of soil dregs. The use of 1 t soil dregs synchronous grouting material can reduce 59.05 kg CO 2 emission. [ABSTRACT FROM AUTHOR]

As an economically intensive city within the greater bay area (GBA), Shenzhen has suffered from severe ozone pollution in recent years. Numerical modeling is an important tool in daily air quality prediction. However, a combination of factors, such as outdated emission inventories and the uncertainty of weather and numeric models, pose challenges for accurately forecasting pollution periods and levels. Therefore, systematic evaluation of the numerical model performance is crucial for better manual revised prediction of the daily MAD8h and air quality index (AQI). In this paper, a multimodel air quality forecast system, which was built in our public-owned monitoring center station, was evaluated to understand the possibilities of improvements. Based on the 2021 observed data of the national air monitoring station, we utilized a series of metrics to access the predicted air quality index (AQI) and pollution concentration levels from models (including WRF-CMAQ, NAQPMS, and WRF-CHEM) and compared them to the manual prediction. The results indicated that WRF-CMAQ performs outstandingly in comprehensive analysis, with a yearly 24 h (hour) forecast accuracy (A) exceeding 91%. The NAQPMS 24 h forecast presented the lowest accuracy (A = 71%) with a high overprediction rate. The forecast accuracy of WRF-CHEM and NAQPMS in the winter period was 100%. However, frequently changing weather patterns interfered with spring forecasting. Forecasters can accurately predict mild pollution cases (A = 81.8% for 24 h, A = 72.7% for 48 h), whereas numerical models (NAQPMS and WRF-CHEM) showed better sensitivity to moderate pollution cases. For fine particulate evaluation, CMAQ's low biases occurred in winter, and the manually revised air quality forecast (MRAQR) normally gave higher prediction values except in pollution cases. Model underestimation could be explained by missing the long-range dust transport contribution from the northern part of China. The ozone 24 h forecast part revealed that WRF-CMAQ exhibited the highest correlation coefficient (R = 0.70, 19.74% < RMSE < 42.7%), whereas NAQPMS showed relatively poor results with the highest ME (43 μg/m3) and MFE (41.92%). Close examination of synoptic information revealed that ozone pollution cases were subjected to the western Pacific subtropical high (WPSH) system, tropical cycle, and regional strong pollution transport. The NAQPMS ozone overprediction could be explained by the overestimation of troposphere transmission from the surroundings during nonconductive weather patterns. The study investigates a theoretical method for model optimization, which provides solid support for precise emission reduction and targeted pollution control. • Established a method for evaluating operational air quality forecasting. • The performance in predicting mild pollution cases in the GBA is slightly weak. • The transportation process is the main source of current prediction bias. [ABSTRACT FROM AUTHOR]

Based on the wind speed records obtained by 356-m-high Shenzhen meteorological gradient tower (SZMGT) during 2019–2020, 10 sets of typical intense thunderstorm outflow records were obtained by screening. This paper suggests that moving average period T = 60 s is a suitable choice based on the correlation between time-varying mean wind and fluctuating wind. In order to discuss the wind characteristics of thunderstorm flow more precisely, it is divided into ramp-up stage, peak stage, and dissipation stage, and the average wind characteristics and fluctuating wind characteristics are analyzed in detail. Analysis revealed the change of average wind angle is usually caused by the movement of the downburst. The shape of the downburst flow profile is time-varying and has certain differences from several empirical profiles. The dissipation stage is closer to synoptic wind in terms of the wind profile than the ramp-up and peak stages. The turbulence intensity of downbursts is not significantly related to the height, but it is more significantly related to the average wind speed as well as the gust factor. • Analyze thunderstorm outflow records from 356-m-high meteorological tower. • Determining reasonable moving average period. • The wind speed before the peak of the downburst changes more sharply than that after the peak. • Large differences in the turbulence intensity distribution between thunderstorm flows and synoptic winds. [ABSTRACT FROM AUTHOR]

[Display omitted] • A network modeling framework suitable for various types of heterogeneous risk factors in urban floods has been proposed. • Simulation of the cascading effects of heterogeneous hazard-bearing bodies in the city from a functional availability perspective under continuous flood attack. • The heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. • The greater degree and more propagating hazard-bearing bodies are key factors influencing the spread of flood risk and the cascading failure of functionalities in urban areas. Increasing urban flooding posed serious threat to urban safety and sustainable development. The heterogeneity of hazard-bearing bodies is one of the important reasons for the variability of cascading failure scenarios in floods. Most previous studies have focused on failure propagation risks among specific types of hazard-bearing bodies, making it impossible to conduct a systematic analysis of the damage caused by persistent flooding in cities. In contrast to conventional graph representations, the heterogeneous hazard-bearing bodies network (HHBBN) based on heterogeneous functional graph theory was constructed in this paper and used to capture the relevant associations of urban resources in a flood. The HHBBN takes into account heterogeneity such as sensitivity and service diffusivity, and functionalities related to hazard-bearing bodies are represented as nodes, with service relationships and geographical associations depicted as edges. To further study the performance change of HHBBN in urban areas during rainstorm flooding, the cascading failure of HHBBN under continuous disturbance was also simulated. The proposed network modeling and cascading failure simulation methods were tested in Futian District of Shenzhen City, China. The case results demonstrate that the heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. Furthermore, the larger degree and more propagating hazard-bearing bodies are the key factors affecting the cascading failure of heterogeneous hazard-bearing bodies. [ABSTRACT FROM AUTHOR]

To advance industrial upgrading, the Government of Shenzhen Nanshan District constantly loosens restrictions and offers more funding for revitalization, creating incentives for village shareholding corporations (VSCs) to revitalize old industrial zones. Focusing on the transformation and upgrading of old industrial zones on collective land based on the case study of Longjing Industrial Zone, this paper investigates the implementation and effectiveness of the new revitalization policies. First of all, by comparing the industrial zone before and after the revitalization, the results of the revitalization are summed up: the renovation of physical environment and infrastructure, the professionalization of operational models, the diversification of enterprise composition, and the demographic change (the move-in of more high-income white-collar workers). Then, the performance of the revitalization is assessed in the aspects of industry cluster and social costs, and the limitation of the transformation and upgrading of industrial zones is identified. In comparison to government-led revitalization projects, based on the analysis of the motives and actions of the stakeholders (district government, VSCs, industrial park operators [IPOs] and tenant enterprises), three underlying factors that exert negative impact on the effectiveness of revitalization have been found: 1) formalistic industry planning; 2) recruiting enterprises with loose restrictions; and 3) imperfect regulation, supervision and final acceptance standards. Finally, based on the above-mentioned limitations and underlying reasons, the paper proposes suggestions on improving the revitalization of old industrial zones. [ABSTRACT FROM AUTHOR]

In this paper, comprehensive field measurements were conducted on a 600-m-high skyscraper in Shenzhen during Typhoon Kompasu based on structural health monitoring (SHM) system and a remotely non-contact interferometric radar system called IBIS-FS. The SHM system recorded wind field and structural responses, including accelerations at 10 building heights by accelerometers and horizontal displacements atop the building by global positioning system (GPS). Meanwhile, the IBIS-FS simultaneously measured the horizontal displacements of the building at multiple heights. This is the first time that the interferometric radar system, i.e., IBIS-FS, is applied to the displacement monitoring of a supertall building under typhoon conditions. The effectiveness of the IBIS-FS is verified through comparing the building responses measured by the IBIS-FS with those measured by the SHM system. Then, based on the field-measured responses, the mean, background, and resonant responses of the monitored supertall building are extracted and analyzed; and the amplitude dependencies and time-varying characteristics of the structural modal parameters (i.e., natural frequencies and damping ratios) are presented and discussed. The results show that the IBIS-FS can provide convenient and accurate displacement measurements of the skyscraper under severe weather conditions. This paper aims to provide useful information for SHM and wind-resistant design of supertall buildings in tropical cyclone-prone regions. • Conduct measurements on a 600-m-tall building by accelerometers, GPS and an interferometric radar system during a typhoon. • Analyse the mean, background, and resonant responses of the skyscraper. • Explore amplitude dependencies and time-varying features of structural modal parameters. • Provide useful information for structural health monitoring and wind-resistant design of high-rise buildings. [ABSTRACT FROM AUTHOR]

• A data-driven model was developed to rapidly quantify the demand response potential of building HVAC systems. • Multiple factors affecting demand response potential were considered in the data-driven model. • A co-simulation platform based on EnergyPlus and Modelica was built for fully getting the demand response potential data. • Two commonly used DR strategies were evaluated using a series of indices. Buildings and heating, ventilation and air-conditioning systems are appropriate resources for demand response, but there is still a lack of rapid quantification methods to reveal buildings' dynamic demand response potential under different meteorological conditions and control strategies. In order to achieve real-time power grid dispatch and to facilitate grid-building interaction, this paper proposes a framework for establishing models to rapidly quantify the demand response potential of resources. First, a detailed co-simulation model of the building and its energy system is constructed, followed by extensive stochastic simulations of two commonly used demand response strategies, after which the demand response potential is quantified using a set of indices. Then, feature selection is performed on factors that may affect demand response potential, and a data-driven model for quantifying demand response potential is proposed, whose results are compared with those from the simulation model. Also, the demand response effects of the two control strategies are compared in this paper. A case study was carried out in an industrial building in Shenzhen, China. The results showed that under different boundaries, the coefficient of determination (R 2) of the quantification indices are all above 0.9, and that the data-driven model rapidly and accurately captured the demand response potential of the building and its heating, ventilation and air-conditioning system, and was easy to deploy in practice. [ABSTRACT FROM AUTHOR]

• Synthesize existing indicators of TODness and related (expected) outcomes. • Formulate (new) indicators of TODness and the outcomes. • Identify aspects of TODness have the most impacts on the outcomes. • Show differences of the impacts across days of a week. TODness, i.e., the extent to which the existing conditions of TOD sites meet agreed-upon TOD standards often requires substantial investment and efforts. Given this, it is legitimate and understandable that decision-makers expect as many positive outcomes from TODness (or TODs) as possible. Non-traditional data (NTD) has provided more opportunities for us to develop (new) indicators for TODness and expected outcomes that we have for TODness. NTD, could, for instance, be used to formulate indicators across more spatiotemporal resolutions and samples and measure new expected outcomes, e.g., smartphone users per hour on weekdays by metro station across a city. Based on case study of Shenzhen, China, this paper introduces indicators for TODness and expected outcomes (partially) based on NTD. Through (spatial) regressions, it identifies the specific TODness (both at the site and regional levels) or station (area) characteristics that have the most impacts on different expected outcomes on weekdays and weekends. The findings and results can on the one hand revalidate the known relationships between expected outcomes in the existing literature and TODness and on the other hand reveal new relationships between the two, which can serve as extra references for TOD-related plan formulation, evaluation and decision-making. [ABSTRACT FROM AUTHOR]

Abstract A daylight harvesting system is to guide and distribute sunlight into the building where natural lighting is limited. An effective daylight harvesting system can reduce the electrical load, increase visual comfort and offer numerous health benefits. In this paper, we systematically analysis a new active daylight harvesting system (ADHS) with tracking, solar concentration, collimation and beam alignment. Through a novel four-mirror active tracking, a near collimated solar ray at 100 suns will pass through the light pipe deep into the building from sunrise to sunset. A 0.1 m diameter light pipe can transmit an average of 30,000 lumen natural light over 8 h per day at Shenzhen, China. With only 2.56% light extraction from the light pipe, a dummy room receives an even illumination with average value of 120 lux without over lighting and glare. The ADHS uses a high-pass mirror to filter out the unwanted infrared radiation, which is the heating load for HVAC. In addition, the effects of slope error, tracking error, incident light angle and solar divergence angle are thoroughly studied by ray tracing. The field test shows that the effective lighting time for an ADHS is more than doubled that of a non-tracking tubular system, and more 100 times increase in transmitted light flux. Other benefits of ADHS are the stability of beam pattern across all solar elevations and the ease of light extraction and control. The ADHS can be widely used in retrofit and new construction due to its robustness and low cost of ownership. Highlights • A daylight harvesting system with active tracking is successfully designed and tested. • It can effectively concentrate, collimate and distribute sun light deep into the building. • It reduces lighting power consumption and HVAC load. • The natural light can be distributed at various locations, and offer numerous health benefits. [ABSTRACT FROM AUTHOR]

Many studies have revealed the processes of urban expansion in numerous cities around the world based on three urban growth types: outlying, edge-expansion, and infilling. However, few studies have explored the landscape patterns inside newly developed urban areas that are defined based on the three growth types. In this paper, we used Landsat images to investigate the urban growth of Shenzhen from 1979 to 2017. In addition, we used high-resolution images to compare the pattern of greenspace in areas that developed from outlying, edge expansion, and infilling. Our results showed that Shenzhen experienced two expansion phases, which is in accordance with the diffusion-coalescence hypothesis. In addition, we found that the greenspace patterns in edge expansion and outlying patches were similar, but were different from those of infilling. In the infilling patches, the greenspace percentage, edge density (ED) and largest patch index (LPI) were significantly larger than those in the patches of the other two expansion types. This study linked landscape patterns and urban growth types that enhanced our understanding of the process of urban expansion and may provide valuable information for local policy making and urban planning. • From 1979 to 2017, the urban area of Shenzhen increased from 19.08 km2 to 1024.92 km2. • We revealed two expansion phases of Shenzhen, which is in accordance with the diffusion-coalescence hypothesis. • The main expansion type in Shenzhen is edge expansion, the average proportion of edge expansion was 74.6%. • More greenspace was found in infilling patches than that in edge expansion and outlying patches. [ABSTRACT FROM AUTHOR]

Abstract This paper aims at scrutinizing the influence of knowledge utilization on the actors' interplay and self-organized localized regeneration of an urban village in China, thereby developing alternatives to simple demolition and redevelopment approaches. In particular, it investigates a bottom-up place-making initiative for the upgrading of Xiasha village in Shenzhen. The case study shows how an urban village may take advantage of its favorable location factors by gradually and relatively carefully upgrading its built environment and developing its assets as profitably as possible at the same time. Despite its crucial role in understanding the regeneration of urban villages, the knowledge dimension is still absent from the respective debates in China. In the case of Xiasha, local, institutional and technical knowledge of stakeholders' rationalities, behavior and interpretations were utilized simultaneously and strategically. Technical knowledge contributed to the idea of a spacious central square; institutional knowledge enabled its implementation; and local knowledge allowed to give symbolic meaning and local identity to the place by making strong use of traditions, historic memories and cultural values when designing the layout and the furniture of the square and its surroundings. Traditional local culture and festivals even experienced a renaissance. With an increasing awareness of the importance of urban villages as living space, more careful and gradual approaches towards upgrading urban villages are needed notwithstanding the low quality and serious urban problems that are very often found in them. The case study shows that the way knowledge is used may serve as a role model for urban village regeneration during high-speed urbanization in China, instead of resorting to simplistic developer-led, socially exclusive models of renewal. [ABSTRACT FROM AUTHOR]

The deployment of electric vehicles has attracted growing attention and is now seen as a possible pathway for a transition towards sustainable transportation. This paper provides insight into the commercialization of electric vehicles in Shenzhen focusing on business innovation and the regulatory context in which it occurs. Using the business model canvas framework, this paper analyzes interactions between enterprises and governments along the value chain of electric vehicles in the bus and taxi fleets. It also discusses the strengths and weaknesses of the Shenzhen model both in business innovation and government regulation for promoting electric vehicle use. This paper finds that Shenzhen has succeeded in fostering a distinct government-enterprise cooperation model that not only reduces the financial pressure on the local government to promote electric vehicle use, but also gives enterprises significant leeway to experiment with various innovative business models. The joint result of these efforts is that the commercialization of electric vehicles has become feasible for delivering the public transport service (buses and taxis) in Shenzhen. Still, this paper argues that the current model of Shenzhen can be further enhanced by: 1) encouraging private investment in charging infrastructures by means of public–private-partnerships; and 2) standardizing electric-vehicle technologies and production to break down the local protectionism in the electric vehicle market. The Shenzhen model acts as a source of inspiration by pointing out the significance of integrating business innovations and government regulations to facilitate the deployment of electric vehicles, which provides practical lessons for industrial players and policy makers in other cities. Furthermore, this work offers theoretical references regarding the application of the multi-actor perspective and the business model canvas framework to analyze the actors and interactions along the value chain of innovative technologies. [ABSTRACT FROM AUTHOR]

To achieve its carbon peak and neutrality targets, China is in need of balancing environmental and economic sustainability; In addition, whether environmental regulations can gain economic dividends while protecting the environment is particularly important. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021, this study takes China's environmental fee-to-tax as a quasi-natural experiment, and adopts the difference-in-difference-in-differences model to evaluate the impact of environmental tax on enterprises' total factor productivity (ETFP). The results show that the environmental fee-to-tax significantly hinders the growth of ETFP. Further, this negative impact mainly stems from the fact that the reform prompts enterprises to choose strategic innovation and conduct greenwashing behavior. Furthermore, when considering enterprises' heterogeneity, we find that enterprises with large scale, high investment efficiency, state ownership, high market competitiveness and located in western regions are more resistant to policy shocks. This paper provides reliable empirical evidence and useful policy implications for further formulating environmental tax policies to accomplish the "win-win" goal for both the environment and the economy. [ABSTRACT FROM AUTHOR]

• When calculating urban ET, AHF should be included in the surface energy budget. • Considering building water dissipation can explain high water dissipation in urban. • Water ET > vegetation ET > soil ET > impervious surface ET, ET in buildings varies greatly. Urban evapotranspiration (ET) is an essential part of the hydrological cycle. A complete understanding of urban ET is necessary to understand the interaction between the atmosphere and the urban surface. However, estimating urban ET is challenging due to the influence of urban surface heterogeneity and human activities in urban areas. Therefore, this paper proposed an improved multisource parallel model (PM-urban) based on the Penman-Monteith model, which is suitable for the ET estimation for the underlying surface of the city. The PM-urban model takes into account the violent human activities in the city, and its main characteristics are: (1) the anthropogenic heat flux (AHF) was included in the surface energy balance formula; (2) The building water dissipation was considered. In this paper, taking Shenzhen, one of the cities with the fastest urbanization process globally, as an example, seven urban ET maps of Shenzhen were generated using Landsat 8 images. The model was verified using pan evaporation data corrected by complementary relationships. The results showed that: (1) The ET of each land use type has the following relationship: water body > vegetation > bare soil > no building impervious surface, and the value of building water dissipation varies due to the height and type of buildings significant; (2) The PM-urban model that considers AHF is better than the original model that ignores AHF; (3) This model can describe the characteristics of urban ET well and explain the phenomenon of high water consumption in cities. Our research results provide a new perspective for calculating the urban ET, which can be applied to urban management, planning, and reducing the urban heat island effect. [ABSTRACT FROM AUTHOR]

Forecasting cities' carbon emissions is an essential support for peak carbon emissions. Previous studies have mainly focused on estimating carbon emissions at large regional scales. Higher spatial resolution mapping of carbon emissions and simulation of future scenarios are important to support locally appropriate policy guidance to reduce carbon emissions. This paper proposes a bottom-up cadastral parcel-scale Carbon emission forecasting framework based on V ector-based C ellular A utomata (CarbonVCA) by integrating land use modelling and carbon emission estimation. Shenzhen's cadastral parcel-scale carbon emissions from 2020 to 2060 are predicted as a case study. A good performance was achieved using CarbonVCA (MAPE = 19.017 %, RMSE = 0.175 Mtpa (C)). Three progressive scenarios are designed for carbon emission simulation from land use planning and energy structure restructuring perspectives. The designed carbon emission reduction scenario limits carbon emissions and enables Shenzhen to achieve peak carbon emissions between 2025 and 2030. However, the efforts to reach peak carbon emissions may prompt the relocation of industrial land to the suburbs. Such areas will need appropriate infrastructure construction to break through terrain and landscape constraints, maintain economic growth and achieve sustainable development. This framework can forecast a high spatial resolution of land-use-based carbon emissions, which helps construct low-carbon cities. • CarbonVCA effectively improves the spatial resolution for forecasting carbon emission. • Practical evidence is provided for integrating land use into carbon emission accounting. • The future impacts of diverse policies are predicted to help adjust macro policies. [ABSTRACT FROM AUTHOR]

Stimulating enterprises to implement environmental management system certification (EMSC, that is to say ISO14001 certification) to the greatest extent and resolving their obstacles in time are the basis for promoting green development of enterprises. Using the full samples of Shanghai and Shenzhen A-share listed industrial enterprises from 2010 to 2019, and employing the Logit model and Propensity Score Matching method (PSM), this paper empirically analyzes the motivations and obstacles for Chinese enterprises to implement EMSC certification, further explores the transformation between advantages and disadvantages under certain conditions, and the differentiation characteristics among enterprises under different social responsibility, property rights and enterprise size. The results show that: ① There is a "U"-shaped relationship between corporate social responsibility and EMSC, and the social responsibility of non-state-owned enterprises could realize the transformation from obstacle to motivation earlier than that of state-owned enterprises. ② There is an inverted "U"-shaped relationship between environmental regulation and EMSC, precisely speaking, this inverted "U"-shaped relationship only exists in non-state-owned enterprises, large-scale enterprises and enterprises with high social responsibility. ③ The public environmental awareness could promote enterprises to implement EMSC, and would strengthen the impact of environmental regulations on enterprises' implementation of EMSC. ④ Based on Propensity Score Matching method, it is concluded that Chinese enterprises indeed improve their environmental performance rather than their financial performance by EMSC. The conclusions provide helpful practical guidance for comprehensively revealing the motivations and obstacles for China's enterprises to implement EMSC. [ABSTRACT FROM AUTHOR]

A good understanding of urban renewal mechanisms is conducive to establishing an efficient spatial governance system. Although previous theories have explained the factors affecting urban renewal, such studies have failed to identify the differences among the drivers of multifunctional types of urban renewal, leading generalized urban renewal policies to fail. To extend the urban renewal theory from a typological perspective, this paper presents a study that identified the similarities and differences in the mechanisms of the multifunctional types of urban renewal projects implemented in Shenzhen, China. Multisource data, such as planning permits, were used to identify the urban renewal projects implemented in Shenzhen, China, between 2010 and 2020 as well as the prerenewal and postrenewal functions of these projects. The results indicate that 1) the regression model accuracy is 60.3 % for urban renewal as a whole. The classification of urban renewal projects improves the goodness of fit of the regression model by 0.16. 2) Land location, land prices, and policies were found to exert a greater influence on urban renewal than other factors, indicating that public demand plays a weaker role in driving urban renewal projects than the government or developers do. 3) The major types of urban renewal can be categorized into three typical driving modes: government-oriented urban renewal, which includes new industrial and public service projects; production-oriented urban renewal, which includes the conversion of industrial land into commercial or residential functions; and consumption-oriented urban renewal, including new residential projects implemented to accommodate the housing needs of an increasing population. Urban planners and policymakers can apply the results to classify urban renewal projects and adopt appropriate strategies for each type to achieve reasonable resource allocation and sustainable development. • Multifunctional types of urban renewal were identified through planning permit data. • The government and developers play a stronger role in urban renewal than public demand. • The major types of urban renewal were categorized into three typical driving modes: government-oriented, production-oriented, and consumption-oriented urban renewal. [ABSTRACT FROM AUTHOR]

Intelligent forecasting model of stock price is an effective way to obtain ideal investment returns. Due to the impact of quantitative transactions, traditional forecasting methods face challenges and pressures. How to find a reliable forecasting model and improve the forecasting accuracy will be a scientific problem worthy of further discussion. This paper proposes an intelligent forecasting model of stock price based on neighborhood rough set (NRS) and multivariate empirical mode decomposition (MEMD) by means of the ideal of "granular computing" and "decomposition ensemble". Firstly, the multiscale permutation entropy (MPE) method is conducted to determine decision features, which can improve the stability of permutation entropy. Then, a new feature selection method, fusing mutual information (MI) and the NRS, is proposed to automatically capture and select the condition features from the stock market. Subsequently, aiming on revealing the more detailed feature information and maintaining relevance between features, the MEMD is utilized to simultaneously decompose all features. Finally, the decomposed features are inputted into the long short-term memory (LSTM) network to train the intelligent forecasting model and provide the forecasting results of stock price. The validity of our proposed model is assessed through the stocks of Shanghai and Shenzhen markets. The results show that our proposed intelligent forecasting model of stock price makes a beneficial attempt and discussion for the integration of "granular computing" and "decomposition ensemble". And it will provide scientific support and reference for investors' actual investment decisions. [ABSTRACT FROM AUTHOR]