Shenzhen Research Articles

Renovation of Collective Rental Housing in Shenzhen's Urban Villages: Model, Resident Satisfaction, and Implications-A Case Study of Yuanfen New Village in Shenzhen City

Renovation of Collective Rental Housing in Shenzhen's Urban Villages: Model, Resident Satisfaction, and Implications-A Case Study of Yuanfen New Village in Shenzhen City

Evolutionary trajectory and characteristics of Mpox virus in 2023 based on a large-scale genomic surveillance in Shenzhen, China

The global epidemic of Mpox virus (MPXV) continues, and a local outbreak has occurred in Shenzhen city since June 2023. Herein, the evolutionary trajectory and characteristics of MPXV in 2023 were analyzed using 92 MPXV sequences from the Shenzhen outbreak and the available genomes from GISAID and GenBank databases. Phylogenetic tracing of the 92 MPXVs suggests that MPXVs in Shenzhen may have multiple sources of importation, and two main transmission chains have been established. The combination of phylogenetic relationships, epidemiological features, and mutation characteristics supports the emergence of a new lineage C.1.1. Together with the B.1 lineage diverging from the A.1 lineage, C.1.1 lineage diverging from the C.1 lineage may serve as another significant evolutionary events of MPXV. Moreover, increasing apolipoprotein B mRNA-editing catalytic polypeptide-like 3 (APOBEC3) related mutations, higher rate of missense mutations, and less mutations in the non-coding regions have been shown during MPXV evolution. Host regulation proteins of MPXV have accumulated considerable amino acid mutations since the B.1 lineage, and a lineage-defining APOBEC3-related mutation that disrupts the N2L gene encoding a viral innate immune modulator has been identified in the C.1.1 lineage. In summary, our study provides compelling evidence for the ongoing evolution of MPXV with specific features.

Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area

Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, we built a SLIR (susceptible-latent-infectious-removed)-based age-stratified meta-population model, integrating mobile phone location data, to simulate the spreading process of an infectious disease across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicate that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance.

Executive team's digital background, financial flexibility and corporate innovation: Evidence from China

Taking China's Shenzhen and Shanghai A-share non-financial listed companies as a research sample from 2018 to 2022, this study explores the relationship between executive team's digital background and corporate innovation. The empirical study shows that executive team's digital background can significantly promote corporate innovation. Financial flexibility plays a mediating role in the relationship between executive team's digital background and corporate innovation. In non-state-owned firms with longer executive tenure, executive team's digital background has a more significant effect on corporate innovation. This study provides theoretical support and practical guidance for strengthening corporate innovation.

The influence of two and three-dimensional spatial characteristics of industrial parks on the emotional well-being of employees: A case study of Shenzhen

Enhancing the emotions of the workers within industrial parks is crucial for achieving the sustainable development goal (SDG). However, evidence on the impact of industrial park environmental layouts on emotions is limited due to the low attention on the workers in the industrial park. Based on a framework of two-dimensional (2D) industrial park morphology indicators and three-dimensional (3D) spatial objective perception, this study aims to compare and analyze the effect of environmental characteristics in Shenzhen's industrial parks on workers' emotions by using emotional geography and social media data. The potential mechanism that environmental characteristic variables influence the positive emotion index (PEI) is also explored. Using the natural language processing (NLP) to analyze the Weibo check-in data, the study captures the daily emotions of workers in industrial parks. By adopting the linear and spatial regression models, this research reveals the complex relationship between the environmental layout of the total 425 industrial parks in Shenzhen city and the corresponding emotions of the workers. The conclusions are as follows: (1) There is a significant correlation between the environmental layout of the industrial parks and the emotions of the workers, with the correlation among 3D variables being higher than that among 2D variables considering spatial effects. (2) There is a certain degree of coordination and contradiction between the morphological indicators of the industrial parks and the spatial objective perception variables, necessitating the planners to consider both macro-scale and micro-perception environmental characteristics to enhance the workers' emotions. (3) In addition to higher accessibility and more public transportation options, the safer, wider, relatively more open streets, more perceivable green spaces, and richer street life can promote the positive emotions among the workers. On the contrary, the enclosed environments, the dense and overwhelming buildings, and the excessive road density may lead to negative emotions. This study contributes to establish the environmental layouts for the industrial parks that foster positive worker emotions and offer insights for the sustainable development of the industrial parks.

Application and Development of Smart City in Guangdong, Hong Kong and Macao Greater Bay Area

With the acceleration of the global urbanization process, all countries and regions pay more and more attention to the sustainable development of cities. As of October 2021, 132 countries and regions have upgraded the goal of "carbon peak and carbon neutral" to a national strategy, and have proposed a vision of a zero-carbon future. The construction of a smart city has become an important factor in sustainable urban development. The special geographical location of Guangdong Hong Kong and Macao Greater Bay Area (hereinafter referred to as GBA) determines its development flexibility, and this region is bound to become one of the important development strategic regions in China. This paper selects the three core cities of GBA, Hong Kong, Guangzhou, and Shenzhen, as the research objectives, and to integrate, compare, and analyze the achievements of these cities in terms of smart cities. The research results can provide a reference for the construction direction of smart cities in other cities.

Epidemiological characteristics of human respiratory syncytial virus in influenza-like illness in Shenzhen City from 2019 to 2023

Objective: To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among cases presenting with influenza-like illness (ILI) in Shenzhen City from 2019 to 2023. Methods: Respiratory specimens were collected from two national sentinel hospitals in Shenzhen from March 2019 to December 2023, specifically targeting cases of ILI. The real-time PCR method was used for the detection and genotyping of HRSV. Basic demographic information was collected and used for the epidemiological analysis. Results: A total of 9 278 respiratory specimens of influenza-like cases were collected and detected, with a total positive rate of 4.77% (443/9 278) for HRSV. In 2021 (8.48%, 167/1 970), the positive rate of HRSV was significantly higher than in 2019 (3.35%, 52/1 552), 2022 (1.80%, 39/2 169), and 2023 (4.49%, 133/2 960), and the difference was statistically significant (χ2=102.395, P<0.001). The prevalence of HRSV was mainly in summer and early autumn (September), and there was an abnormal increase in the positive rate of HRSV in winter 2022. The highest positive rate of HRSV was in children under five years old (9.84%, 330/335). The typing results showed that in 2022, the prevalence of HRSV-A was predominant (71.79%, 28/39), and in 2023, HRSV-A and HRSV-B subtypes coexisted. Conclusions: The prevalence of HRSV in Shenzhen from 2019 to 2023 has obvious seasonality, mainly in summer and early autumn. Children under five years old are the main population of HRSV infections.

Maternal prenatal systemic inflammation indexes predicts premature neonatal respiratory distress syndrome

Neonatal respiratory distress syndrome (NRDS) is one of the leading causes of neonatal mortality in low-income countries. It is caused by a lack of surface-active substances in the lungs, and the maternal inflammatory response plays an important role in the formation of surface-active substances in the fetal lungs. We aimed to investigate the correlation between maternal prenatal systemic inflammatory indices and respiratory distress syndrome in preterm neonates. This is a retrospective case–control study that collected data from all patients who delivered between 28 and 36 weeks of gestation at Longhua District People's Hospital in Shenzhen City and whose infants were admitted to the neonatal unit, newborns with respiratory distress syndrome were in the experimental group (NRDS group), and newborns without NRDS were in the control group (non-NRDS group). To minimize the effect of confounders on the results, propensity score matching was performed on baseline characteristics. Totally, 524 patients were included (93 in the NRDS group and 431 in the non-NRDS group), and 71 matched pairs (142 patients). The neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI) and neutrophil lymphocyte to platelet ratio (NLPR) were higher in the NRDS group than in the non-NRDS group (p < 0.05). The ROC curves of NLR, dNLR, SII, SIRI, AISI and NLPR for the diagnosis of NRDS were plotted, and it was found that the combined diagnostic efficacy of these six systemic inflammatory markers was better (AUC: 0.643, P = 0.003). Patients were divided into two groups based on the cut-off values determined from the ROC curves, and analysis using binary regression models revealed that SII ≥ 1199.94 (OR, 2.554; 95% CI 1.245–5.239, P = 0.011) and NLPR ≥ 0.0239 (OR, 2.175; 95% CI 1.061–4.459, P = 0.034) were independent risk factors predicting NRDS. Maternal prenatal SII ≥ 1199.94 and NLPR ≥ 0.0239 are independent risk factors for NRDS, and clinicians may be used to prevent neonatal respiratory distress in advance to reduce the incidence of NRDS.

Quantifying Residents’ Perceptions of Smart Community Construction through a Hybrid EWM-PROMETHEE II Method: A Case Study of Shenzhen, China

Smart community construction (SCC) is pivotal for urban modernization, yet extant research often neglects the residents’ perceptions of these initiatives, focusing predominantly on technological advancements. A hybrid evaluation method integrating the Entropy Weight Method (EWM) and PROMETHEE II was developed to quantify residents’ perceptions of SCC. Through the literature review and expert interview, the evaluation indicators for residents’ perceptions of the SCC were identified, leading to the development of an evaluation indicator system consisting of 5 dimensions and 25 indicators. The hybrid evaluation method that combines EWM and PROMETHEE II was then applied to quantify the level of residents’ perceptions of SCC across five communities in Shenzhen city of China, using data from 410 valid questionnaires. The results indicated that the Baolong community had the highest level of the SCC, while the Nanyuan community exhibited the lowest ranking. Sensitivity analysis confirmed the robustness of these results, thereby validating the evaluation process. Moreover, recommendations were proposed to enhance residents’ perceptions of smart community initiatives. This research not only introduces a novel evaluation method that expands the body of knowledge on the SCC and residents’ perceptions but also helps the government develop sustainable SCC strategies.

Urban equilibrium: legal imperatives for sustainable development and habitat preservation in Shenzhen, China

Shenzhen, a bustling metropolis in China, is currently grappling with the delicate balance between urban expansion and environmental preservation. This study finds that the disintegration of habitats due to urban development in Shenzhen city significantly impacts the ecosystem of animals and plants, detrimentally altering their living conditions. The study results reveal that the expansion of construction land in China has surged by an impressive 84.39% between 2000 and 2020, which has come at the cost of diminishing wetlands, farmland, and woodlands by 57.24%, 16.58%, and 1.86%, respectively. Between 1980–2020, the urban expanse of Shenzhen expanded dramatically, growing from 16.0 square kilometres to 850.2 square kilometres. This transformation was primarily driven by the rapid conversion of forested areas, which saw a staggering decrease of approximately 300% to 780 km2 by 2020. Additionally, half of Shenzhen’s expansion can be attributed to the transformation of agricultural land, with an additional 40% stemming from the clearing of forests and approximately 9% resulting from the decrease in water resources. Furthermore, non-urban green spaces, particularly built-up areas, have significantly expanded, now encompassing approximately 60% of the city in 2018. Therefore, based on the existing literature and authoritative legal texts, this study aims to examine the legal frameworks of the Shenzhen government and, along with China’s national legislation, to make room for wildlife and biodiversity conservation in a metropolis like Shenzhen.

New media surveillance, environmental information uncertainty and corporate environmental information disclosure

This paper selects A-share heavy polluting enterprises in China's Shanghai and Shenzhen cities from 2009 to 2022. The following conclusions are obtained from the empirical evidence: new media monitoring can encourage enterprises to disclose information. Environmental information uncertainty can encourage enterprises to disclose information. The positive effect of environmental information uncertainty on environmental information disclosure is more significant for firms with poorer environmental performance than those with better environmental performance. The effect of the new media monitoring on environmental information disclosure of non-state-owned enterprises is more significant when other information is held constant.

The Nearshore Marine of ShenZhen Environmental Risk Assessment Method Based on the DPSIR Framework

The nearshore marine areas are the most intensely concentrated regions for human economic activities and serve as vital carriers for the development of the marine economy. Accurately assessing the environmental risks in these areas is of significant importance for the sustainable development of the marine economy. This study constructs an environmental risk assessment system for the nearshore marine areas of Shenzhen City based on the DPSIR framework and evaluates it using the comprehensive index method. Specifically, the study begins by selecting 27 indicators related to the marine environment to construct the evaluation index system. It employs the grey relational analysis and entropy weight method to weight the indicators and finally assesses the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 using the comprehensive index method. The study finds that the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 are generally low, with the risk score increasing from 0.3277 to 0.7308 year by year, shifting from a more dangerous to a safe risk level. The research indicates that the marine environment of the nearshore areas in Shenzhen is greatly influenced by local human economic activities and the industrial structure of Shenzhen. The study suggests that the environmental safety level of Shenzhen's nearshore marine areas could be improved by leveraging policy guidance, optimizing the industrial structure, and perfecting tourism management methods.

A study of the impact effect of digital transformation on environmental performance ——Based intermediation of financing constraints

This paper selects A-share listed companies in China's Shanghai and Shenzhen cities from 2015 to 2022 as the research sample, and puts forward the corresponding research hypotheses and empirically tests the relationship between digital transformation and environmental performance through the fixed effect model of digital transformation and environmental performance. The results show that corporate digital transformation can improve corporate environmental performance. The driving effect of digital transformation on corporate environmental performance is more prominent in enterprises with non-dual CEO and chairman positions, state-owned and high gearing ratios. Empirical evidence is provided at the firm level for the role of digital transformation in improving environmental performance, which contributes to the synergistic development of digital greening in firms.

The peer effects of corporate poverty alleviation behavior: Empirical evidence from China.

This study explores the peer and economic effects of corporate poverty alleviation behavior. Using the data of A-share non-financial listed corporates in Shanghai and Shenzhen of China from 2016 to 2020, the empirical analysis of this study finds that: corporate poverty alleviation behavior has significant peer effects; the guidance of local poverty alleviation policies weakens the peer effects of corporate poverty alleviation behavior; compared to private enterprises, the poverty alleviation behavior of the peer firms has a more significant impact on state-owned enterprises; and corporate poverty alleviation behavior can result in the backflow of economic benefits and achieve the organic unity of economic and social benefits. The purpose of this paper is to explore the peer effects of corporate poverty alleviation behaviors through empirical analysis using available public data. The results of the study not only increase the motivation of corporate to participate in poverty alleviation from a peer effects perspective, but also reveal key factors for sustaining corporate poverty alleviation behaviors.

Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments

AbstractThe lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.

Remote Family Communication as Discounted Compensation: Mobile Use and Psychological Well-Being Among Internal Older Migrants Within China

Among the growing floating population in China, many older people—who migrate along with their adult children to provide family support—have formed a specific social group: lao-piao-zu. This group of older migrants is a population with particular experiences and challenges, which merits more scholarly attention to their well-being. The study first presents a three-dimensional conceptual framework for understanding multimodal mobile phone communication among older migrants. Guided by this framework and based on a face-to-face survey ( N = 376) conducted in China’s first-tier city Shenzhen, the study specifically examined how various forms of mobile communication could affect psychological well-being (PWB) among these older migrants. The results showed that mobile phone use could increase multimodal communication activities. However, only communication with the hometown could reduce loneliness and promote PWB. We argue that mobile communication has limited effects on older migrants’ PWB. It is a promising channel for maintaining remote ties; however, it should be considered more as a form of compensation than as the optimal option for older people who are away from their hometowns.

Governance of Cross-border Regions &amp; SEZs: The case of Gwadar under CPEC

Purpose: Examining the transformative impact of the China-Pakistan Economic Corridor (CPEC), a flagship project of China's Belt and Road Initiative (BRI), this research investigates how CPEC fosters cross-border cooperation and regional development. The research focuses on transnational institution building and the creation of multi-scalar geographies involving diverse public and private agencies, reshaping regional, sub-regional, and local territories. Design/Methodology/Approach: Employing a case study approach, the research focuses on Gwadar, the pivotal node of CPEC where the economic belt meets the maritime silk road. It integrates a literature review, analysis of official documents, and empirical data, supplemented by insights from independent researchers and international organizations such as the World Bank and IMF. Findings: The designation of Gwadar as a Special Economic Zone (SEZ) with a globally competitive deep-sea port has introduced significant economic opportunities. However, multi-scalar interactions have led to tensions between central and local authorities, reflecting conflicts between the Centre-led macroeconomic agenda and its socio-economic sustainability at the local level. Implications/Originality/Value: The research emphasizes the need to rescale political, economic, and social structures to establish sustainable institutions in cross-border regions. It provides policy recommendations for Gwadar's development, drawing lessons from global best practices such as Dubai, Shenzhen, and King Abdullah Economic City.

Hypoxia exerts greater impacts on shallow groundwater nitrogen cycling than seawater mixture in coastal zone.

There is no doubt that hypoxia and seawater mixture are profoundly affecting the global nitrogen (N) cycle. However, their mechanisms for altering N cycling patterns in shallow coastal groundwater are largely unknown. Here, we examined shallow groundwater N transformation characteristics (dissolved inorganic N and related chemical properties) in the coastal area of east and west Shenzhen City. Results showed that common hypoxic conditions exist in this study area. Ions/Cl- ratios indicated varying levels of saltwater mixture and sulfide formation across this study area. Dissolved oxygen (DO) affects the N cycle process by controlling the conditions of nitrification and the formation of sulfides. Salinity affects nitrification and denitrification processes by physiological effects, while sulfide impacts nitrification, denitrification, and dissimilatory nitrate reduction to ammonium (DNRA) processes through its own toxicity mechanism and the provision of electron donors for DNRA organisms. Redundancy analysis (RDA) results indicate that the influence magnitude is in the following order: DO > sulfide > salinity. Seawater mixture weakened the nitrification and denitrification of groundwater by changing salinity, while hypoxia and its controlled sulfide formation not only weaken nitrification and denitrification but also stimulated the DNRA process and promotes N regeneration. In this study area, hypoxia is considered to exert greater impacts on N cycling in the coastal shallow groundwater than seawater mixture. These findings greatly improve our understanding of the consequences of hypoxia and seawater mixture on coastal groundwater N cycling.

Can entrepreneurship in the new era promote green innovation efficiency? evidence from manufacturing enterprises in China

ABSTRACT Based on panel data of 398 listed manufacturing companies in China's Shenzhen – Shanghai A-shares from 2012 to 2022, this study uses the double fixed effect model to explore the direct impact and heterogeneity of entrepreneurship in the new era on the green innovation efficiency of manufacturing enterprises and utilises the intermediary effect model to investigate the indirect mechanism of entrepreneurship in the new era on the green innovation efficiency of manufacturing enterprises. Research has shown that entrepreneurship in the new era can significantly improve the efficiency of green innovation in manufacturing enterprises, especially for state-owned, technology-intensive, labour-intensive, and non-heavy polluters. The mechanism test results show that entrepreneurship in the new era increases enterprise performance, promotes the driving effect of digital transformation on manufacturing enterprises’ green innovation efficiency, and is an important channel for improving this efficiency. This study contributes to a deeper understanding of the driving effect of entrepreneurship in China's new era, provides a reference for the innovation practice of promoting green transformation in the manufacturing industry, and provides important theoretical and empirical evidence for expanding the research frontier of green innovation efficiency.

A hybrid multi-criteria decision-making model for waste facilities location considering system resilience

The location of waste treatment facilities is a crucial strategic decision in urban management. Most existing studies focus on mathematical modeling and algorithm solving for multi-objective models aimed at minimizing cost or pollution. They lack consideration for the resilience of decision solutions in the face of uncertain and unpredictable disruptive incidents. This paper aims to bridge the research gap by proposing a top-down hybrid multi-criteria decision-making model that incorporates cost, pollution, social discontent, and system resilience simultaneously. The proposed model integrates various methods such as integer programming modeling, simulation modeling, and catastrophe analysis. It addresses the challenge that disruptive incidents cannot be precisely expressed. Additionally, it quantitatively analyzes the factors that impact system resilience. Finally, the proposed model is validated by applying it to a waste management network based on the real case of Shenzhen city. The results demonstrate notable differences in the resilience indexes among the solutions in the Pareto-optimal set, which confirms the significance of system resilience from another aspect. Further catastrophe analysis reveals several valuable insights for urban waste management. This paper offers a new perspective to cope with various risks related to waste management and provides a method reference and decision support for the construction of urban resilience.