Prefecture Level Research Articles

Analysis of the change in human resources of centers for disease control and prevention in different regions of China from 2010 to 2020

Objective: To analyze the change in human resources within China's Centers for Disease Control and Prevention (CDC) from 2010 to 2020. Methods: The self-reported information from provincial, prefectural (city), and county (district) levels of China's CDC, covering employee counts, staff composition, professional qualifications, educational backgrounds, technical titles, and tenure, were extracted from the China Disease Prevention and Control Information System. The demographic context was provided by the annual population figures from the China Statistical Yearbook (2010-2020). The profile of CDC personnel was described, and the average annual percentage rate change (AAPC), average annual percentage rate change (APC), human resource agglomeration degree (HRAD) and the difference between HRAD and population agglomeration degree (PAD) were calculated. The Joinpoint regression model was used to analyze the time trend. Results: The decade under review witnessed a net increase of 17 300 active and 18 300 enrolled personnel in the CDC, surpassing the national population growth rate with AAPCs of 0.93% and 1.03%, respectively. This upward trajectory was statistically significant (P<0.05). The ratio of disease control personnel per 10 000 population escalated from 1.14 to 1.21. An initial decline in active CDC workforce density (from 1.31 to 1.27 per 10 000 population between 2010 and 2017) was followed by an increase (from 1.28 to 1.37 between 2018 and 2020), with APCs of -0.40% and 3.73%, respectively. The proportion of professional and technical staff in 2019 was highest in the eastern region (86.01%), followed by the western (83.75%) and central regions (79.54%). The period also saw an enhancement in the average academic degree (from 1.91 to 2.43 points) and professional title scores (from 1.39 to 1.53 points) of CDC personnel. While the average tenure in the eastern and western regions showed a slight decline, the central region experienced an increase, with HRAD values indicating a higher concentration in the eastern and central regions compared to the western region. The HRAD-PAD discrepancy revealed a negative value in the eastern region, nearing zero in the central and western regions. Conclusion: Between 2010 and 2020, China's CDC experienced notable growth in human resources and underwent structural optimization, albeit with significant regional disparities in concentration.

The impact of housing prices and land financing on economic growth: Evidence from Chinese 277 cities at the prefecture level and above.

With the rapid progress of urbanization in China, the real estate industry, characterized by a long industrial chain, has become a pillar industry for economic development. Therefore, we inspect the nexus between land finance, housing prices, and economic growth. For this purpose, we use the panel data of 277 cities at the prefecture level or above in China from 2011 to 2019, and empirically examine it by using the Panel Vector Auto Regression (PVAR) model. The results show that there is a causal relationship between housing prices and economic growth. Housing prices promote economic growth in the short term and inhibit it in the long term. Both economic growth and housing prices have a significant impact on land finance. The economic growth show a significantly positive impact, while housing prices promote land finance in the short term with a long-term trend from positive to negative. This is the first study that tries to probe the relationship between urban housing prices, land finance, and economic growth by considering 277 prefecture-level and above cities in China. To promote the stable development of the regional economy, local governments need to overcome their dependence on the housing market and land finance and promote the healthy development of the housing market.

Does Environmental Court Lead to Local Green Growth? Evidence from 230 Cities in China

To tackle the multifaceted environmental challenges and bolster environmental law enforcement in China, environmental courts have proliferated across many cities since 2007. However, their actual effects on economic growth, a central concern for governments at all levels, have yet to be thoroughly investigated. Leveraging the panel data of 230 cities from 2007-2016 and taking the establishment of environment courts as a quasi-natural experiment, this paper uses the difference-in-differences (DID) approach to examine the effects of building environmental courts on green economic growth at the prefectural level. We find that first, the effect is positive and significant; second, on possible channel for the boosting effect of environment court is through increasing technological innovation; third, the effect of court on green growth is more significant after two to three years. These findings provide references for both scholars of environmental politics and practitioners of green development in local governments.

Relationships among six urban air pollutants and identification of pollution types – A case study of Chinese cities above prefecture level

Urban air pollution is caused by the interplay of urban development and natural conditions, but it's unclear which factor has a greater impact. Studying the link between air pollutants and identifying the types of pollution causes is crucial for understanding the pollution mechanisms and formulating efficient management measures. During the research period from 2015 to 2020, this study analyzed the relationships between six air pollutants, calculated the city capacity (CC) and natural condition (NC) scores of 285 cities with the help of the index system and entropy weight TOPSIS model, and employed Gaussian Mixture Model (GMM) and random forest regression model to identify air pollution types. The results show: During the study period, the concentrations of most pollutants decreased, with SO2 experiencing the largest decline, reflecting the significant effectiveness of China's recent atmospheric pollution control efforts. In terms of spatial distribution, the North China Plain (NCP) and the Loess Plateau (LP) remain focal areas of pollution. The correlation analysis results indicate a significant positive correlation among all air pollutants except O3, underscoring the necessity of coordinated pollution control efforts, but the complexity of O3 pollution should not be overlooked. High-value agglomerations of CC scores calculated by the model are predominantly located in the eastern coastal region, while NC scores exhibit two high-value agglomerations in the north and south. Utilizing the GMM, the 285 cities were classified into three categories, and the random forest regression model was employed to identify them as CC dominate, NC dominate, and intermediate types. This paper proposes a novel approach for categorizing city air pollution types, aiming to tailor prevention measures and foster sustainable urban development.

Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman’s rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman’s rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Agglomeration Externalities vs. Network Externalities: Impact on Green Technology Innovation in 283 Chinese Cities

The prominence of agglomeration externalities (AEs) and network externalities (NEs) in urban sustainable development has intensified in recent times, with advances in transportation infrastructure and information technology acting as key accelerators. Despite the scholarly attention they receive, the specific spillover effects that these externalities exert on green technology innovation (GTI) remain under-explored. In an effort to bridge this knowledge gap, the present study employs a spatial Durbin model to scrutinize, spanning a decade from 2011 to 2021, the impact and spatial spillover of AEs and NEs on GTI across 283 Chinese cities of prefecture level and above. The findings reveal the following: (1) AEs exert a U-shaped influence on GTI, initially inhibiting it, before ultimately fostering its growth. (2) NEs are found to consistently promote GTI. (3) The spatial spillover effects of AEs on GTI are significantly positive, while those from NEs are not statistically significant. (4) The influences of AEs and NEs on GTI exhibit marked regional variations. This study extends the research scope on the factors influencing GTI by examining the role of AEs and NEs, thereby aiming to offer valuable insights for enhancing the level of GTI.

The Impact of the Digital Economy on Employment Scale in the Yangtze River Delta Region

Abstract Employment is the greatest livelihood and the most basic support for economic development. In recent years, the scale and scope of digital economy has expanded dramatically, triggering revolutionary changes in employment patterns. Especially in the Yangtze River Delta region. This paper is based on panel data of 41 prefecture level cities in the Yangtze River Delta region from 2011 to 2021. Firstly, the fixed-effects regression model is used to empirically analyze the impact of the digital economy on the employment scale in the YRD region, and the intermediary mechanism model is further used to verify the role of industrial structure upgrading in the digital economy's impact on the employment scale. The study finds that: The digital economy has significantly expanded the employment scale in the YRD region, indicating that the employment creation effect is greater than the employment substitution effect. In the heterogeneity analysis, this impact is more significant in the central cities of the YRD region, areas with higher levels of urbanization and well-developed digital infrastructures. The digital economy can promote the optimization and upgrading of the employment market in the YRD region through the upgrading of the industrial structure, expanding the scale of employment, optimizing the employment structure. Keywords: Digital economy development, Yangtze River Delta region, Employment scale, Industrial structure upgrade.

Spatial–temporal evolution and peer effects of urban green development efficiency in China

In the process of global urban development, there are urgent ecological security and environmental pollution problems, green development is the fundamental way for urban sustainable development, economic transformation and mitigation of ecological and environmental problems. Based on the panel data of 283 cities at prefecture level and above in China from 2003 to 2017, this paper analyzes spatial–temporal evolution characteristics of urban green development efficiency (UGDE) and the peer effects of UGDE between cities of different grades. It is found that during the study period, in terms of temporal evolution, the average UGDE in China increased from 0.47 in 2003 to 0.61 in 2017, with a cumulative growth rate of 29.79%, showing a rising trend in general. In terms of spatial evolution, the number of low-efficiency cities and medium-efficiency cities continued to decrease. The eastern region has always been the main distribution area of higher-efficiency cities and high-efficiency cities; in the central region, UGDE in most cities improved significantly; in the western region, UGDE has always lagged behind that in the eastern and central regions. In addition, the center of gravity of UGDE presented a trend of northwest migration in general, with a total displacement of 100.07 km, and UGDE showed a spatial dispersion trend. The empirical results indicate that the improvement of UGDE in large cities has a driving effect on that in neighboring medium cities and small cities through the positive peer effect, and the growth of UGDE in medium cities has a promoting effect on that in neighboring small cities through the positive peer effect; the increase of UGDE in medium cities has a positive peer effect on that in neighboring large cities, and the growth of UGDE in small cities has a positive peer effect on that in neighboring medium cities; UGDE promotes each other between large cities through the positive peer effect.

Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities

Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.

The internal mechanism and impact of digital economy on China's smog pollution: based on the perspective of industrial structure coordination

ABSTRACT This study adopts an industrial structure coordination perspective to elucidate the theoretical mechanism through which digital economy (DE) development influences smog pollution (SP), and employs panel data encompassing 214 cities at the prefecture level and above in China over the period 2010–2019 to empirically examine this relationship. The findings reveal the following: (1) Industrial structure rationalisation (ISR) and advancement (ISA) positively contribute to the nexus between DE and governance in SP. Nevertheless, ISR stands amenable for refinement. (2) Despite regional variations in economic development planning and competitive investment behaviours, DE consistently hampers the SP of adjacent cities, contingent on diverse geographical weights. (3) Augmentation in science and technology expenditure and human capital levels enhances the environmental governance effect of the DE.

Impact of high-speed rail on the mismatch of labor and industry allocations: Evidence from Chinese cities in 2000-2019

The uneven development of China can be reflected by the mismatch of labor and industry distributions (MLIS) in terms of spatial structure. Industries usually agglomerate in large cities where a strict household registration system causing high barriers with respect to population and labor entry. Infrastructures that reduce the cost of labor and technology mobility, as represented by high-speed rail (HSR), are possible improvement to regulate such distortions as the two agglomerated into opposite directions. This paper focuses on the impact of HSR on MLIS using a large panel dataset comprising 285 Chinese cities in 2000–2019. A difference-in-difference analysis is introduced to carried out empirical analyses. This paper finds that HSR can significantly mitigate the MLIS in prefecture level cities, but not in cities above prefecture level. The heterogeneous effects of HSR are then discussed with respect to industries and MLIS types. Further mechanism tests show that HSR’s mitigating effect on MLIS comes from the employment effect and the innovation effect. Finally, policy recommendations are provided on HSR investment to improve China's uneven development from the perspective of synergy labor and industry distributions.

Digital infrastructure and urban economic resilience: evidence from the “Broadband China” pilot policy

ABSTRACT This paper investigates the impact of digital infrastructure development on urban economic resilience with a quasi-natural experimental approach. Based on prefecture level dataset pertaining to 281 cities from 2011 to 2019, we find that the implementation of ‘Broadband China’ pilot policy has had a positive and significant impact on economic resilience and such an effect is more pronounced in coastal regions than other regions. Further, we show that the transition of labour skill is the underlying mechanism to explain the favourable effect of digital infrastructure on economic resilience.

Inequality characteristics and influencing factors of CO2 emissions per capita in Jiangsu Province, China.

Analyzing the inequality characteristics and influencing factors of CO2 emissions per capita (CEPC) is conducive to balancing regional development and CO2 emissions reduction. This study applied the Gini coefficient and Theil index to investigate the CEPC inequalities during 2005-2017 at the county level in Jiangsu Province, China. Considering the spatial spillover and interaction effects, the factors influencing CEPC were analyzed by a hierarchical spatial autoregressive model. The results showed that the inequalities in CEPC first increased and then decreased at the inter-regional, and inter-county levels. The spatial pattern of CEPC was stable, and there was a significantly positive spatial autocorrelation of CEPC at the county level. The High-High type counties were mainly located in Sunan (southern Jiangsu). The spatial interaction effects of the CEPC between the prefecture and county levels indicated that governments at the prefecture level should integrate their county governments to reduce the CEPC. Moreover, carbon intensity, GDP per capita, land urbanization, and industrial structure play an important role in reducing CEPC. Our findings provide a scientific basis for formulating reasonable and effective carbon emission reduction policies.

The impact of digital economy on rural revitalization: Evidence from Guangdong, China

BackgroundDigital technology and economy have profound impact on rural revitalization and become important initiatives to promote rural development. This study analyzes the spatial effect of digital economy on rural revitalization in Guangdong, China. MethodThe entropy weight method is used to evaluate the level of rural revitalization and digital economy in 20 prefecture level cities of Guangdong province during 2011–2021. Spatial panel model is used to analyze the spatial spillover effect of digital economy on rural revitalization. Threshold model is employed to analyze the marginal effects of the digital economy on rural revitalization. Heterogeneous analysis is made by grouping the prefecture-level cities into four regions: the Pearl River Delta (PRD), the East Wing, the West Wing and the Mountainous region. ResultsBoth the level of rural revitalization and digital economy development in every prefecture-level city experienced an increase during 2011–2021 and the PRD region take the lead in both the rural revitalization and digital economy. There is significant spatial spillover effect of digital economy on the rural revitalization as a whole (the coefficient is about 0.3537). However, in the regions with a low level of digital economic development, there is a negative impact (−0.1847) of the digital economy on rural revitalization; while in regions with medium or high level of digital economy, it has a significant positive effect (0.3835) on rural revitalization. The impact of digital economy on rural revitalization development is significantly greater in the PRD (0.3141), the East Wing (0.7215), the West Wing (0.5467) than in the mountainous region (0.2238). ConclusionsThere is significant spatial differentiation in the digital economy and rural revitalization. The digital economy has significant spatial spillover effect on the rural revitalization. And this effect is found out to have heterogeneous and non-linear characteristics. In order to promote rural revitalization, it is necessary to make full use of the digital economy and technology in rural areas. Besides, it is important to improve the digital skills and literary of the rural population so that the digital economy could play a greater role in rural revitalization.

Decomposing China’s bilateral export growth: A firm-regional-transactions structural gravity approach

This paper undertakes a decomposition of China’s firm-transaction-level exports with an emphasis on aggregates thereof at the prefectural level. Chinese prefectures are large and, relative to some smaller countries, the larger ones are visible players in the world market. We decompose China’s prefecture-product-level bilateral exports to foreign countries into their main components. These are factor costs, market potential (including trade frictions and foreign expenditure) as well as quality and productivity. We consider the latter two as to be drawn from a bivariate Pareto distribution that is specific to prefectures, products, and countries. This strategy enables retrieving fixed market access costs and permits conducting counterfactual experiments with a focus on the distribution across Chinese prefectures.

The Impact of Data Agglomeration on Export Structure Upgrading in Cities: A Factor Mobility Perspective

The digital economy and export upgrading are important topics of common concern for policymakers and academics during the period of high-quality economic development. From the perspective of factor mobility, this paper constructs the two-way fixed effect, mediation effect, and spatial Durbin models to analyze the impacts of data agglomeration on urban export structure upgrading. Using panel data of 280 cities at the prefecture level and above in China from 2005 to 2018, the empirical analysis reveals a positive impact of data agglomeration on urban export structure upgrading, with capital transfer and technology diffusion further reinforcing this impact. Additionally, data agglomeration promotes urban export structure upgrading by optimizing innovation resource allocation and exhibits a spatial spillover effect on urban export structure upgrading. Finally, the facilitating effect on urban export structure upgrading is heterogeneous. Consequently, it is imperative to expedite the construction of new digital infrastructure, foster the integration and symbiotic evolution of data and traditional production factors, and implement distinct innovation development pathways based on regional comparative advantages.

Career Mobility and Performance Evaluation of China's SOE Managerial Elites

Abstract This paper examines the career mobility and performance evaluation of the leaders of China's state-owned enterprises (SOEs) at the central, provincial and prefectural levels. Using data on executive turnover within publicly listed SOEs, we find that central and local SOE leaders have a similar career flow pattern. Specifically, vertical mobility within the same business group is common and more than 60 per cent of SOE leaders leave their office within three years. Only a minority of SOE leaders achieve political promotion. We also find that performance evaluation criteria are different across central and local SOEs. The leaders who provide better economic outcomes for central SOEs obtain a higher evaluation score, while close political connections boost the promotion prospects of local SOE leaders. Overall, our findings provide granular evidence on the personnel management of China's SOEs from a comparative perspective.

Study on the urban digital transformation gyroscope model

Purpose Urban digital transformation has become a key strategy in global countries. This study aims to provide a comprehensive and dynamic exploration of the intrinsic traits associated with urban digital transformation, in order to yield detailed insights that can contribute to the formulation of well-informed decisions and strategies in the field of urban development initiatives. Design/methodology/approach Through analysis of parallels between urban digital transformation and gyroscope motion in physics, the study developed the urban digital transformation gyroscope model (UDTGM), which comprises of seven core elements. With the balanced panel dataset from 268 cities at and above the prefecture level in China, we validate the dynamic mechanism of this model. Findings The findings of this study underscore that the collaboration among infrastructure development, knowledge-driven forces and economic operations markedly bolsters the urban digital transformation gyroscope’s efficacy. Practical implications This research introduces a groundbreaking framework for comprehending urban digital transformation, potentially facilitating its balanced and systemic practical implementation. Originality/value This study pioneers the UDTGM theoretically and verifies the dynamic mechanism of this model with real data.

Climate resilience of urban water systems: A case study of sponge cities in China

Resilience is an important aim of climate adaptation in urban development planning. Since 2015, sponge city (SC) pilot construction in China has highlighted the importance of urban water systems, but has paid insufficient attention to extreme and long-term climate change risks. This paper aims to provide a new method for evaluating the urban water system climate resilience (UWSCR), classify China cities in combination with climate hazards, conduct case studies on SC pilots, and propose ways to improve the UWSCR in SCs. Firstly, Taking heavy rain and drought as hazards, an evaluation index system of UWSCR is established, including engineering resilience (ENR), ecological resilience (ECR) and evolutionary resilience (EVR). A combination model based on the analytic hierarchy process and entropy weight method is used to measure the UWSCR of 27 SC pilot cities and 253 non-pilot cities at or above the prefecture level in China. Secondly, coordinating hazard index and UWSCR index, cities are divided into four types. The results show that the ENR and EVR of SC pilots exhibited an increasing trend from 2010 to 2020 and were greater than those of non-pilots. Among the four types, Resilient Cities and Low Risk Cities with high climate resilience (CR) were mainly distributed in the eastern and central regions of China. High Risk Cities and Vulnerable Cities with low CR were mostly distributed in the central and western regions. Among the cities of high CR, SC pilots accounted for 63.0% of the pilot samples, while non-pilots only accounted for 26.5% of the non-pilot samples. Finally, this paper analyzes the typical cases of SC pilots, and puts forward some suggestions, such as considering the UWSCR systematically and holistically, improving the ECR of UWSCR, making medium-term to long-term adaptation planning and providing differentiated policy support. The originality of this work is to identify four types of cities from the perspective of UWSCR and provide reference for improving the CR of SCs.

Research on the green innovation effect of digital economy network – Empirical evidence from the manufacturing industry in the Yangtze River Delta

The digital economy is open and borderless, and the development of an urban digital economy forms an associated network. Based on the social network theory, the digital economic association network formed between cities can enable manufacturing enterprises to obtain heterogeneous resources, stabilize the network relationship between nodes, and improve innovation performance. The digital economy's spatial correlation network drives the manufacturing industry's green innovation to improve quality and efficiency. Through the integration of external relations, green innovation, proximity, regional innovation system, and other dimensions of the theory, this study takes 41 prefecture level cities in the Yangtze River Delta from 2006 to 2021 as samples to construct the digital economy correlation network of urban agglomerations and uses the social network analysis method (SNA) to reveal the evolution law of the digital economy correlation networks. We establish the panel fixed effect model to explore the green innovation effect of digital economy networks. The results show that the Yangtze River Delta urban agglomeration constitutes a spatial correlation network of the digital economy, promotes the sharing and exchange of manufacturing resources through factor linkage, and produces a green innovation effect. The network centrality and relationship strength have a significant inverted U-shaped relationship with the green innovation of the manufacturing industry. There is a significant U-shaped relationship between structural holes and the green innovation of the manufacturing industry. Accordingly, we should further improve the design of digital economy development and promote the development of the manufacturing industry.