Case Of Shenzhen Research Articles

Effect of pre- and post-migration neighborhood environment on migrants’ mental health: the case of Shenzhen, China

ABSTRACT Existing mental health studies usually disregard people’s neighborhood experiences in the past, which may have long-lasting mental health effects. This may particularly be true for migrants. To assess how the perceived pre- and post-migration neighborhood environment shapes migrants’ mental health later on in life, a quasi-longitudinal survey (N = 591) among migrants was conducted in Shenzhen, China. The risk of poor mental health was screened with the General Health Questionnaire (GHQ). Perceptions of the pre- and post-migration neighborhood environment were measured retrospectively and assessed with structural equation models. The results show that the direct pathways linking the perceived post-migration neighborhood physical (NPE) and social environment (NSE) to migrants’ mental health are significant. No direct association is found between the pre-migration neighborhood environments and mental health. The indirect path between the pre-migration NPE/NSE and mental health is significantly mediated by the post-migration NPE and NSE. Migrants’ SES development and their neighborhood attainment interplay overtime which have long-term impacts on their mental health. Our findings suggest that the pre-migration neighborhood plays a crucial role in migrants’ mental health. This confirms a path dependency of migrants’ neighborhood environment throughout their migrations. Future mental health studies are advised to incorporate neighborhood characteristics along migrants’ residential histories.

Exploring collective activity space and its spatial heterogeneity using mobile phone signaling Data: A case of Shenzhen, China

The planning ethos of providing proximity-based services to all inhabitants has been prevailing recently, and underlines the importance of knowing areal differences in collective activity space (AS) of populations. Mobile phone signaling data (MSD) has great potentials for this end, especially in mega-cities with fast changing and spatially varying demographic composition. However, two problems need to be addressed when applying MSD-based AS measurement for planning practices, including the identification of regularly visited locations and the selection of measure indices. This paper proposes a three-step workflow to apply the MSD to measure local collective AS with considering addressing the problems. This three-step workflow aims to illustrate the procedure of using MSD to measure collective AS for supporting planning practice in urban China, with clarifying some key concerns when doing so. We apply the workflow to examine the spatial heterogeneity of the collective AS in Shenzhen City and discuss the transferability of the workflow in different social and institutional contexts.

A Redundancy Allocation Model for Uncertainty Random Water Supply System in Water Saving Management Contract Projects

Water saving management contract (WSMC) projects provide advanced technology and management for a water supply system to achieve water conservation and set redundant components to ensure water supply reliability. Project managers focus on the reliability optimization problem and require redundancy allocation strategies of the above system. This paper presents an optimization method by dealing with the lifetime of the whole water supply system. Assuming the lifetimes of advanced components are uncertain variables and the old ones are random variables, a reliability optimization model of water supply systems is established based on chance theory, and the redundancy allocation solutions are obtained by an optimization toolkit. A WSMC case in Shenzhen, China is studied and the results show that the reliability of the water supply system has been in a high state based on the allocation strategy. This study provides theoretical support for improving water-saving safety and popularizing the WSMC service mechanism.

Renovation and Reconstruction of Urban Land Use by a Cost-Heuristic Genetic Algorithm: A Case in Shenzhen

Renovation and Reconstruction of Urban Land Use by a Cost-Heuristic Genetic Algorithm: A Case in Shenzhen

Exploring the production of spatial inequality in dockless bicycle sharing in Shenzhen

There have been rising concerns over the unequal geographical distribution of bicycle sharing services in cities. Previous research on inequalities of urban bikeshare has often focused on quantitatively mapping inequalities with big data from travel records. It has thus been unable to examine the causes, reproduction mechanisms, and consequences of observed spatial inequalities in detail. Using the case of Shenzhen, a Chinese megacity with strong governmental control on mobility and a historical legacy of urban segregation, this study aims to achieve a nuanced understanding of the mechanism through which operators, regulators, and users of dockless bicycle sharing interact to produce and reproduce inequalities in accessing and using the service. To achieve this, this study adopts a mixed-method approach consisting of GIS mapping analysis, survey, and interviews with cyclists and experts. The result shows that both the use frequency and perceived accessibility of dockless bicycles are higher in wealthier and more central neighbourhoods. Moreover, strong governmental regulation in the form of a designated parking system, fleet quota distribution and point-based service evaluation have reproduced and enhanced spatial inequalities in dockless bicycle use and access. The findings contribute to the literature by demonstrating how preexisting urban infrastructure and certain regulatory arrangements interact to form a reproduction cycle of inequalities in accessing and using dockless bicycles that favours more central and well-off neighbourhoods at the expense of marginalized communities in Shenzhen.

State-led versus market-led: How institutional arrangements impact collaborative governance in participatory urban regeneration in China

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.

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis.

Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

“Is What We See Always Real?” A Comparative Study of Two-Dimensional and Three-Dimensional Urban Green Spaces: The Case of Shenzhen’s Central District

This paper takes the central area of Shenzhen as an example to explore the correlation and differences between 2D and 3D green spaces on urban roads during the summer of 2023. By collecting street view image data and using convolutional neural networks for image semantic segmentation, the Green View Index (GVI) was calculated and combined with the Normalized Difference Vegetation Index (NDVI) for analysis. The results show that the road greening levels in Nanshan District, Futian District, and Luohu District of Shenzhen are relatively high, with GVI exceeding 25%. The Pearson correlation coefficient between the 2D and 3D greening data is 0.5818, indicating a moderate correlation. By analyzing four typical greening scenarios (high NDVI and high GVI, high NDVI and low GVI, low NDVI and high GVI, and low NDVI and low GVI), the study found specific reasons for the differences in green data in different dimensions; the analysis revealed that factors such as building height, density, and elevated transportation facilities significantly affect the accuracy of NDVI in urban spaces. The study suggests that in urban greening assessments, the complementarity and differences between street view data and remote sensing data should be comprehensively considered to improve the accuracy and comprehensiveness of the analysis.

Effectiveness of policies for electric commercial vehicle adoption and emission reduction in the logistics industry

Electric commercial vehicles (ECVs) offer a promising solution to reduce carbon emissions, but barriers like high purchase costs, limited range, long charging times, and battery degradation hinder their adoption. This research develops a mathematical model to explore the impact of government policies, specifically carbon tax policy and the cap-and-trade mechanism, with or without purchase subsidy, on the mixed fleet configuration of electric and internal combustion commercial vehicles. Through a comparative analysis based on a real case in Shenzhen, this research provides insights into the impact of policies on ECV adoption and emission reduction. The findings indicate that higher tax rates and carbon prices effectively promote ECV adoption, while the carbon emission quota does not. When choosing policies, the government should consider its situation and objectives. Combining cap-and-trade mechanisms with purchase subsidies is the most effective for ECV adoption and emission reduction, assuming financially feasible. Alternatively, implementing a carbon tax policy without purchase subsidy is more practical. The cap-and-trade mechanism in the long term supports the phase-out of purchase subsidies and contributes to sustainable economic and environmental development. What's more, the findings provide guides for the government to determine an appropriate industry regulator factor when allocating quotas for the logistics industry.

A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed.

Data governance for smart cities in China: the case of Shenzhen

Establishing an appropriate system for governing various data is a critical challenge in developing smart cities. In China, with its distinctive institutional characteristics, it is not yet well investigated what kinds of data governance mechanisms are introduced, how data are collected, shared, and used, and how potential risks concerning data security and privacy are addressed. This paper conducts an exploratory study of the case of smart city development in Shenzhen and examines critical opportunities and challenges in data governance. A centralized approach led by the government has been emphasized in data governance, with its focus evolving from addressing the fragmentation of government data to fostering the integration of various kinds of data in society. Open data platforms have been developed through close cooperation between government and technology enterprises. Regulations have been introduced to protect data security and privacy and facilitate the exchange and use of data for innovation. On the other hand, stakeholders are not sufficiently incentivized to provide accurate information. The value of data is not appropriately recognized or measured, discouraging the sharing of data to facilitate the use of data. Citizens are not well-informed about what kinds of data are collected and how these data are used. Institutional mechanisms have not yet been established to ensure that the data collected from citizens are properly handled by the public authorities. It is crucial to encourage citizens’ engagement in data governance to fully implement the people-centered approach to smart city development.

A Branch-and-Price Algorithm for Large-Scale Multidepot Electric Bus Scheduling

Electric buses (e-buses) are increasingly adopted in the transit systems for their benefits of reduced roadside pollution and better onboard experience. E-bus scheduling is a critical problem in the operation planning stage of transit management to ensure efficient and reliable transit service. In Chinese mega cities, the e-bus networks are operated with many bus routes characterized by high service frequency and long operation time, making the e-bus scheduling a large-scale problem. Besides, the transit agency requires the vehicle-depot constraint to limit the e-bus reposition since it is not cost-efficient. An efficient method to generate optimized schedules that meet the above requirements is desired by the transit agencies. In this paper, we address a large-scale multi-depot electric bus scheduling problem considering the vehicle-depot constraint and partial recharging policy. A mixed integer programming model and an efficient branch-and-price (BP) algorithm are developed to solve the problem. In the BP algorithm, we devise a heuristic method to generate good initial solutions and adopted heuristic decisions in the label setting algorithm to solve the pricing problem. In this way, the efficiency of the BP algorithm is achieved and the large-sized problem instances can be solved. We conduct extensive numerical experiments based on the fixed-route and multi-route operation cases in Shenzhen. The results show that the BP algorithm can generate provable high-quality solutions. Sensitivity analysis indicates that increasing battery capacity and charging rate can reduce the operational cost. The optimal charging schedules can also provide guide in determining the capacity of the charging facilities.

Supply-demand relationship and spatial flow of urban cultural ecosystem services: The case of Shenzhen, China

Cultural ecosystem services (CES) have garnered significant attention due to their capacity to comprehensively evaluate the influence of ecosystem degradation on human well-being. It is very important to evaluate the spatial coordination relationship between the supply and demand of urban CES and explore its spatial flow mechanism for guiding the sustainable development of urban regions. However, research on the relationship between supply and demand of urban CES and their spatial flows is still in the conceptual exploration stage, and its quantitative assessment methods are not yet mature. Taking Shenzhen as an example, this study uses the Social Values for Ecosystem Services model to evaluate the supply of CES based on environmental background and survey statistics; Uses the topic model of natural language processing, quantify the demand of CES based on social media data. The Bivariate Local Indicators of Spatial Association index is utilized to examine the correlation between supply and demand, while a crowd activity extraction method based on mobile phone signaling data is developed to simulate the spatial flow pattern of CES. The results show that: (1) The supply of urban CES shows obvious spatial differentiation; There are differences in the perception of the needs of various CES. Among them, the beneficiaries have higher needs for Aesthetic and Recreation. (2) The relationship between supply and demand of urban CES shows different degrees of spatial mismatch, with the proportion of the supply-demand matching area to the total area being less than 20%, and the main type of supply-demand mismatch being low supply-high demand. (3) All kinds of CES flow from the center of the study area to the east, among which the flow of recreation and Aesthetic is more active, with the highest flow of 189.7 and 135.3, respectively. Overall, the coordination between various types of CES in the region is still insufficient. We should encourage the establishment and improvement of a diversified transportation system connecting the central urban area and other regions while enhancing the condition of the local ecological conditions, to improve the capacity of urban ring roads and rail transit, to promote the sustainable development of the regional ecological environment and social economy, and to enhance human well-being.

Trends in Antibiotic Resistance Patterns and Burden of Escherichia Coli Infections in Young Children: A Retrospective Cross-Sectional Study in Shenzhen, China from 2014-2018.

The emergence of multi-drug resistant ESBL-producing E. coli poses a global health problem. In this study, we aimed to investigate the prevalence of E. coli infections and their antibiotic susceptibility profiles in paediatric clinical cases in Shenzhen, China from Jan 1, 2014, to Jan 30, 2019, while also determining temporal trends, identifying ESBL-producing strains, and recommending potential empirical antibiotic therapy options. We isolated a total of 4148 E. coli from different specimens from a single paediatric healthcare centre. Additionally, we obtained relevant demographic data from the hospital's electronic health records. Subsequently, we performed antimicrobial susceptibility testing for 8 classes of antibiotics and assessed ESBL production. Out of the 4148 isolates, 2645 were from males. The highest burden of E. coli was observed in the age group of 0-1 years, which gradually declined over the five-year study period. Antimicrobial susceptibility results indicated that 82% of E. coli isolates were highly resistant to ampicillin, followed by 52.36% resistant to cefazolin and 47.46% resistant to trimethoprim/sulfamethoxazole. Notably, a high prevalence of ESBL production (49.54%) was observed among the E. coli isolates, with 60% of them displaying a multi-drug resistance phenotype. However, it is worth mentioning that a majority of the isolates remained susceptible to ertapenem and imipenem. Our findings also highlighted a decrease in E. coli infections in Shenzhen, primarily among hospitalized patients in the 0-1 year age group. However, this decline was accompanied by a considerably high rate of ESBL production and increasing resistance to multiple antibiotics. Our study underscores the urgent need for effective strategies to combat multi-drug resistant ESBL-producing E. coli Infections.

Adaptive distributionally robust cluster-based healthcare network design problem under an uncertain environment

County medical alliances (CMAs) in China are considered to be effective in optimizing the layout of medical resources and improving primary healthcare services. To facilitate the implementation of CMAs in multiple geographical clusters, we examine a cluster-based healthcare network design (CHND) problem with patient referral in uncapacitated and capacitated cases. Considering the uncertainty of health demand and unit referral cost, we develop adaptive distributionally robust models for the CHND problem, where the location and allocation decisions anticipate the worst-case expected patient treatment and referral cost over an ambiguity set. In terms of tractability, we reformulate the adaptive distributionally robust models as tractable forms. Further, a hybrid genetic algorithm that combines a genetic algorithm with a commercial solver is designed to improve the computational efficiency for large-scale instances. Finally, we apply our models to a real-world case in Shenzhen, China, to verify the effectiveness of the proposed algorithm and assess the value of the adaptive distributionally robust models over the corresponding robust model. The results show that our algorithm has outstanding efficiency and accuracy when compared to the hybrid particle swarm algorithm and the commercial solver. Moreover, the adaptive distributionally robust model outperforms the robust model in terms of solution quality.

Higher Education and Local Innovation Systems: Lessons From Public Attitudes in Shenzhen, China

This article examines public attitudes on the local innovation system of Shenzhen with a focus on the role of the local government and higher education (HE). Based on data collected from an online platform, Zhihu, this article examines netizens’ perceptions of local government, institutional environment, HE and labour force development. Text analysis reveals that netizens hold relatively positive attitudes towards the local government and the future development of Shenzhen due to its fair institutional environment. Local HEIs needs to be further developed to support Shenzhen’s economic transformation but the close linkage with Hong Kong universities help to mitigate the challenges in human capital supply. This paper presents a novel way of examining public perceptions on local innovation systems and the case of Shenzhen also provides policy lessons to other cities aiming at developing innovation centres.

Epidemiological and genomic analyses of human isolates of Streptococcus suis between 2005 and 2021 in Shenzhen, China.

Streptococcus suis (S. suis) is an important food-borne zoonotic pathogen that causes swine streptococcosis, which threatens human health and brings economic loss to the swine industry. Three-quarters of human S. suis infections are caused by serotype 2. A retrospective analysis of human S. suis cases in Shenzhen, a megacity in China, with high pork consumption, between 2005 and 2021 was conducted to understand its genomic epidemiology, pathogen virulence, and drug resistance characteristics. The epidemiological investigation showed that human cases of S. suis in Shenzhen were mainly associated with people who had been in close contact with raw pork or other swine products. Whole-genome sequence analysis showed that 33 human isolates in Shenzhen were dominated by serotype 2 (75.76%), followed by serotype 14 (24.24%), and the most prevalent sequence types (STs) were ST7 (48.48%) and ST1 (39.40%). ST242 (9.09%) and ST25 (3.03%), which were rarely reported, were also found. Phylogenetic analysis showed that the Shenzhen human isolates had close genetic relatedness to isolates from Guangxi (China), Sichuan (China), and Vietnam. We found a new 82 KB pathogenicity island (PAI) in the serotype 2 isolate that may play a role in sepsis. Similarly, a serotype 14 isolate, containing 78 KB PAI, was isolated from a patient presenting with streptococcal toxic shock syndrome (STSLS) who subsequently died. Multi-drug resistance (MDR) was high in human isolates of S. suis from Shenzhen. Most human isolates were resistant to tetracycline, streptomycin, erythromycin, and clindamycin, and 13 isolates had intermediate resistance to penicillin. In conclusion, swine importation from Guangxi, Sichuan, and Vietnam should be more closely monitored, and the use of antibiotics limited to reduce the potential for antimicrobial resistance (AMR).

Using a Data Mining Method to Explore Strategies for Improving the Social Interaction Environment Quality of Urban Neighborhood Open Spaces

With the intensification of population aging and the increasing awareness of public health protection in the post-epidemic era, the renewal of the old urban community neighborhood space is facing many new challenges and problems. Neighborhood Public Open Space (POS) is the main place for people to carry out various social activities in community life. The quality of the social interaction environment that a neighborhood POS can provide can have a vital impact on people’s well-being, as well as their physical and mental health. Therefore, the purpose of this research is to identify and clarify the key physical environmental design attributes/features of the old urban community neighborhood POS, and to explore the relationship between them from the perspective of creating a high-quality social environment. Through the investigation of relevant cases in Shenzhen and Guangzhou, China, the classification performance of each case on the key physical and environmental elements is used as the conditional attribute, and the quality of the social interaction environment in the current situation of each case is used as the decision making attribute to conduct a data mining analysis. Using rough set theory, this study screened out four important elements: greenbelt form planning (C1); ped and bike system (C2); space organization and zoning planning (C6); Public facilities (C8). Moreover, this study also presents a set of hierarchical decision rules to describe the classification status of the matching physical environmental design elements when the social interaction environment reaches a high quality in the neighborhood POS. This study provides local policy makers with key current situation assessment and diagnostic tools in urban-built environmental renewal projects. The results of this study can help designers draw up the renovation design plans of neighborhood POS on the basis of efficiently obtaining the practical experience of relevant cases, and then create a high-quality social interaction environment.

How much urban air quality is affected by local emissions: A unique case study from a megacity in the Pearl River Delta, China

In March 2022, the resurgence of COVID-19 cases in Shenzhen, a megacity in the Pearl River Delta (PRD) region of China, led to unusual restrictions on anthropogenic activities within a single city, in contrast to the restrictions COVID-19 caused on a national scale at the beginning of 2020. In this unique event, we found that only under unfavorable meteorological conditions did substantial urban local emission reductions have an impact on air pollutant changes (−42.4%–6.6%), whereas the deweathered changes were very small (−8.3%–3.4%) under favorable meteorological conditions. Primary anthropogenic pollutants, such as NO2, toluene, BC, and primary organic aerosol (POA), responded most considerably to emission reductions from early morning to noon during unfavorable meteorological days; for secondary organic aerosol (SOA), regulating the daytime total oxidant (Ox = O3 + NO2) was found to be more effective than controlling its precursors within the city scale, whereas secondary nitrate displayed the opposite trend. Since Ox changed little during the urban lockdown despite the remarkable decrease in precursors, it is emphasized that regionally coordinated control of VOCs and NOx is necessary to effectively reduce Ox levels. In addition, Shenzhen's NOx emission reduction efforts should be sustained in order to control PM2.5 and O3 pollution synergistically for long-term attainment.

Urban higher education development with Chinese characteristics–The case of Shenzhen

In the age of higher education globalization and marketization, regional development of higher education in China diversifies as a result of the processes of decentralization both localize and stratify the Chinese institutions. The higher education sector in Shenzhen has developed rapidly over the last four decades and it is now home to a number of leading universities and colleges as an emerging education hub in South China. This paper aims to understand the local process in Shenzhen, through a case study of its higher education sector, drawing on interviews with city policymakers and university managers as well as other extent literature and materials. By dividing the process into three different phases, this paper begins by providing a periodized description of the higher education development in the city. Then, it summarizes Shenzhen’s experience in promoting its higher education through negotiating with and navigating within the existing policy framework. Finally, it concludes by discussing a potential ‘Shenzhen model’ of higher education development and providing further information for international scholars to better understand the higher education development in the city context of China.