Urban Agglomerations In China Research Articles

Assessment and spatial effect of urban agglomeration business environments: A case study of two urban agglomerations in China

The business environment is becoming a yardstick for assessment of local governments in China, and thus the question of how to evaluate the business environment of urban agglomerations has become an important new issue. This paper proposes a quantitative method for the assessment of business environments in urban agglomerations for purposes of city ranking. First, a business environment evaluation index system for urban agglomerations, including the four dimensions of market, government, law, and humanistic, was established. Second, A hybrid model combined entropy weight method (EWM), genetic algorithm (GA), and back propagation (BP) neural network was constructed to quantise the business environment. Then, the Chengdu-Chongqing and Beibu Gulf urban agglomerations in China, the two most important urban agglomerations of the new western land-sea corridor, were selected as cast study. The results are as follows: (1) The business environments of the two most important urban agglomerations of the new western land-sea corridor have been improved, but the differences between them are significant. The level of development of the business environment of Chengdu-Chongqing is higher than that of Beibu Gulf; specifically, the biggest gap is of the rule of law environment. The gap in the governmental business environment is the smallest. (2) From 2015 to 2020, the difference in business environment between the two most important urban agglomerations in the new western land-sea corridor decreased. However, intra-group differences were the largest. (3) The spatial pattern of the business environment in the new western land-sea corridor shows a “northwest-southeast" distribution, with the center of gravity of the business environment moving to the southeast from 2015 to 2020. (4) Compared with focusing on the development of the business environment in large cities, promoting the business environment in small and medium-sized cities has a more significant effect on improving the business environment in urban agglomerations.

Investigation on the complex relationship between urbanization and eco-efficiency in urban agglomeration of China: The case study of Chengdu-Chongqing urban agglomeration

Ecological and environmental protection is a key issue that cannot be ignored in the process of urbanization. To achieve the goal of urban sustainable development, the dynamic and complex relationship between urbanization and the ecological environment must be coordinated. A comprehensive evaluation of 16 cities within the Chengdu-Chongqing urban agglomeration from 2011 to 2020 utilized the entropy weight method, super-SBM model, and coupling coordination degree model to analyze urbanization levels, ecological efficiency, and their interplay. The findings reveal a fluctuating descent in urbanization levels with a fluctuating upward in eco-efficiency across Chengdu-Chongqing from 2011 to 2020. The coupling coordination degree, exhibiting a dynamic, bimodal distribution, reflects the intricate balance between urbanization and ecological efficiency. A kernel density model and Tobit model further elucidate the evolving nature of the coordination degree and its principal determinants. This research underscores the role of green initiatives and consumption patterns in enhancing coordination, while governmental intervention and industrial structure emerge as critical factors undermining coordination. The study provides insights for government policy formulation, offering substantial contributions to the recalibration of urban development strategies and the optimization of city planning paradigms.

The Effects and Mechanisms of the Rural Homestead System on the Imbalance of Rural Human–Land Relationships: Evidence from the Yangtze River Delta Urban Agglomeration in China

The imbalance of rural human–land relationships has become a notable problem in China’s urbanization process. The dual urban–rural system is widely regarded as the crucial factor contributing to this problem in China. Although the significance of institutional forces has been substantially recognized, the rural homestead system seems to be generally under-evaluated in this issue. Most of the previous literature focuses on the dual household registration system, while the effects and the detailed mechanisms of the rural homestead system on human–land relationships lack depth in research. The objective of this research is to help fill this gap in the literature on the complex effects and the detailed mechanisms of the rural homestead system on rural human–land relationships. In view of this, this paper establishes a conceptual framework on the basis of land function theory and public domain of property rights theory and proposes two mechanism hypotheses: one is the land attachment mechanism of farmers’ rights and interests (LAM), the other is the land finance preference mechanism of local governments (LFPM). Then, this article examines them empirically using the panel model with the data of 41 cities from 2010 to 2021 in the Yangtze River Delta of China. The main conclusions are as follows: (1) LAM promotes the imbalance of rural human–land relationships due to the attachment of farmer’s social security rights and property expectant interests to the rural homesteads; (2) LFPM drives the imbalance of rural human–land relationships, owing to both the preference of land transfer revenue and the exclusion of rural migrants’ citizenship financial cost on local governments; (3) the moderating effects suggest that LFPM can strengthen the effect of LAM, and the spatial Durbin model results show that both LAM and LFPM have spatial spillover effects. It is hoped that the findings will provide a reference for deepening the rural homestead system reform.

Unveiling the impact mechanism of urban resilience on carbon dioxide emissions of the Pearl River Delta urban agglomeration in China

With the increasingly severe global climate change situation, the construction of low-carbon and resilient cities is expected to be an effective means of accelerating the achievement of carbon peak and carbon neutrality goals. As a major global economic and carbon-emitting power, it is necessary to advocate for China to accelerate the construction of low-carbon and resilient cities. We have formulated a comprehensive methodological framework to appraise the effects of urban resilience (UR) on carbon dioxide (CO2) emissions. This study first constructs a “social-economic-institutional-ecological-engineering” UR assessment framework. Based on this framework, we establish an evaluation system for the UR of the urban agglomeration to explore the dynamic evolution of its spatial and temporal patterns. And then this study uses the SBM-DEA method that includes undesirable outputs to measure the carbon emission efficiency of the urban agglomeration. Finally, the spatial Durbin model is used to analyze the spatial spillover effects of UR on CO2 emissions, and the mechanism of CO2 emission spatial effects is theoretically explained by introducing geographical laws. The results show that the overall UR level in the region increased from 0.107 in 2000 to 0.318 in 2020, with an average carbon emission efficiency of 0.82, but still at a relatively low level. Moreover, there are significant differences in the regional level, with Guangzhou and Shenzhen and their surrounding cities showing significantly higher levels compared to other cities. Regarding the spillover effects analysis, CO2 emissions exhibit significant spatial spillover effects between cities. Improving UR can reduce urban CO2 emissions, but it may also have negative spillover effects on surrounding cities. This research can provide a new perspective for improving the study of regional CO2 emissions reduction and its influencing mechanisms while also offering important references for accelerating the construction of UR and achieving regional carbon peak targets.

Analysing the differential impact of the COVID-19 pandemic on the resilience of the tourism economy: A case study of the Chengdu-Chongqing urban agglomeration in China

The outbreak of the COVID-19 pandemic in 2019 had a profound impact on the tourism economy, underscoring the critical importance of assessing and analyzing tourism economic resilience. Traditionally, prior research predominantly focused on constructing evaluation systems based on three dimensions: Risk preparedness capability, Restoration capability, and Reorganization and modernization capacity. In this study, we take an innovative approach by incorporating urban network thinking and establishing a tourism economic network, while introducing the dimension of “The rationality of network structure.” To comprehensively understand the dynamics of tourism economic resilience, we divided the period from 2018 to 2021 into three distinct phases: Stable period, Pre-shock period, and Shock period. This division allowed us to conduct comparative research that highlights the variations in tourism economic resilience across these different time frames. Additionally, we employed advanced methods, such as kernel density estimation and the GTWR model, for empirical analysis of tourism economic resilience within the Chengdu-Chongqing city cluster. The results of our research unveiled that Chengdu and Chongqing both demonstrate a remarkable level of resilience within their tourism economies. However, given their status as the “dual core” of the Chengdu-Chongqing city agglomeration, they are inherently more susceptible to significant fluctuations when confronted with shocks. The spatial pattern of tourism economic resilience is characterized by prominent wings on both sides, a North-South balance, and a central region with vulnerabilities. The predominant evolutionary patterns are marked by multi-level stabilization, moderate growth, and moderate decline. Ziyang is an exceptional region within the broader growth zone, and its reduced risk preparedness capability has led to an overall decline in tourism economic resilience. Furthermore, key influencing factors, including the economy, infrastructure, and ecological environment, exhibit significant spatial heterogeneity. In conclusion, our study offers valuable insights into researching tourism economic resilience under external shocks, such as the COVID-19 pandemic. These findings can be instrumental in guiding policymakers as they develop effective strategies to bolster tourism economic resilience.

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

The impact of innovation capability on green development in China's urban agglomerations

As the main form of new urbanization in China, the effect and mechanism of urban agglomerations' innovation capability on green development are still unclear. Based on the panel data from 197 prefecture-level cities across 19 urban agglomerations in China, we explore the impact of the innovation capability of urban agglomerations on green development from 2008 to 2021. It is found that the innovation capability of urban agglomerations can significantly improve the level of green development, and talent agglomeration and industrial structure upgrading can positively adjust the impact of the innovation ability of urban agglomerations on green development. Heterogeneity results reveal that the impact of innovation capacity on green development in urban agglomerations is different in regional distribution, policy orientation, innovation capacity intensity, and whether it is cross-provincial. Finally, this study suggests that differentiated policies should be adopted to optimize the role of urban agglomeration innovation capability in promoting green development.

Spatiotemporal characteristics and the impact on economic development efficiency of the functional division of urban agglomerations in China

Spatiotemporal characteristics and the impact on economic development efficiency of the functional division of urban agglomerations in China

Spatial evolution of green technology cooperation among three major urban agglomerations in China and its carbon emission reduction effect

Spatial evolution of green technology cooperation among three major urban agglomerations in China and its carbon emission reduction effect

Remote sensing monitoring and spatiotemporal characteristics analysis of urban construction land expansion in urban agglomerations of China in the past 30 years

Remote sensing monitoring and spatiotemporal characteristics analysis of urban construction land expansion in urban agglomerations of China in the past 30 years

Spatiotemporal evolution and climbing curve simulation of land green use efficiency in urban agglomerations of China

Spatiotemporal evolution and climbing curve simulation of land green use efficiency in urban agglomerations of China

The driving factors of recreational utilization of ecological space in urban agglomerations: The perspective of urban political ecology

Understanding factors driving the recreational utilization of ecological space in urban agglomerations is crucial for the sustainable management of regional systems. However, existing literature focuses on the provision, distribution, and accessibility of urban green space at small scales, lacking explorations into ecological recreational spaces at an urban agglomeration scale. The current study explores the recreational utilization of ecological space in the Pearl River Delta Urban Agglomeration in China by using panel data regression and field investigations from the perspective of urban political ecology. The results show that the forest area proportion, natural reserve area proportion, fiscal revenue, and tourist numbers all have a positive effect on the recreational utilization rate of ecological space; the fixed assets investment exerts unstable prohibitive impacts on the RUoES rate, and the wetland area proportion presents a negative effect on the RUoES rate; land use rights and regional governance structure exacerbate the imbalanced distribution of the rate across different cities. The research contributes to the existing literature by researching urban ecological space from a recreational perspective and at a broader spatial scale, thereby promoting the theoretical integration of recreational geography and political ecology.

Spatiotemporal prediction of carbon emissions using a hybrid deep learning model considering temporal and spatial correlations

Accurate prediction of carbon emissions plays a crucial role in enabling government decision-makers to formulate appropriate policies and plan necessary response measures in a timely manner. This study explored the spatiotemporal prediction methods for carbon emissions from temporal and spatial correlation perspectives. Specifically, a deep learning-based hybrid prediction framework for carbon emissions was developed. It includes three sequentially linked modules: gated recurrent units for modelling temporal correlation features, graph convolutional networks for modelling spatial correlation features and spatiotemporal prediction. The proposed model enables one- and multi-step spatiotemporal prediction of carbon emissions. The monthly Open-source Data Inventory for Anthropogenic CO2 data for three major urban agglomerations in China were utilised to assess the performance of our model. Results indicate that our model outperforms the baseline models in terms of predictive accuracy for single- and multi-step spatiotemporal predictions. Additionally, our model demonstrates good generalisation through further application experiments.

Evaluation of the Spatiotemporal Change of Ecological Quality under the Context of Urban Expansion—A Case Study of Typical Urban Agglomerations in China

As a significant manifestation of human activities influencing natural environment, rapid urbanization has enhanced economic prosperity while simultaneously posing threats to ecological quality. Beijing–Tianjin–Hebei (BTH), the core region of the Yangtze River Delta (CYRD), and the Pearl River Delta (PRD) stand as three major economic centers characterized by the highest level of urbanization in China, encompassing areas of heightened ecological sensitivity. Nevertheless, the ecological quality at the scale of urban agglomerations remains ambiguous, with many studies failing to develop a comprehensive and effective method for comparing diverse urban agglomerations. Consequently, this study integrates multi-source remote sensing data, including information on land cover and other socio-economic parameters, to construct the Ecological Quality Index (EQI) based on the “Function–Interaction–Pressure–Stability” (FIPS) framework. Through a stratified determination of indicator weights grounded in both objective importance and empirical knowledge, we mapped the spatiotemporal changes of EQI and analyzed the impact of urbanization on ecological quality in three urban agglomerations from 2001 to 2020. We determined the following: (1) The calculated EQI can further capture the nuanced details with better performance at both underlining the discrepancy of highs and lows of EQI and describing the spatial detail of urban agglomerations’ characteristics. (2) Substantial disparities in EQI and its changes are evident across different urban agglomerations. Notably, only the average EQI improves in PRD, while ecological degradation is prominent in specific regions, such as the southeastern plains of BTH area, along the Yangtze River, and around Shanghai in CYRD and central PRD. The CYRD exhibits the largest affected area. (3) Urbanization predominantly influences ecological quality through land cover transitions. In expansion areas, ecological deterioration is significantly more pronounced, constituting approximately 90% of the total area. (4) Despite significant urbanization, city-level analysis in CYRD reveals a better coordination between urban expansion and ecological protection, with a lower intensity of ecological degradation compared to urban expansion rates. Conversely, some cities in the BTH, despite modest urban expansion, exhibit substantial declines in ecological quality, highlighting the need for targeted policy interventions. In conclusion, this study elucidates the intricate relationship between urbanization and ecological quality, offering valuable insights for the development of targeted protection strategies and sustainable urban planning.

Spatial effects and influencing factors of urban sustainable development: An analysis of urban agglomerations in China

As the world becomes more environmentally conscious, the exploration of sustainable development paths for urban agglomerations has become a new platform for debate. This paper uses data from Chinese cities to study the level of urban sustainability. Three methods, kernel density, Gini coefficient and spatial measurement model, were used to explore the spatial effects and influencing factors of sustainable development of cities and urban agglomerations. Through the study, the following conclusions are obtained: First, the temporal and regional differences in sustainable urban development differ between cities belonging to urban agglomerations and those not belonging to urban agglomerations. In terms of overall distribution, there is a tendency for the eastern seaboard to radiate outwards. Second, the role of educational resources in urban sustainable development is particularly prominent. Finally, the sustainable development of urban agglomerations shows large spatial differences. The spillover effects of influencing factors vary from urban agglomeration to urban agglomeration. This study provides valuable guidance to policymakers and stakeholders. It can promote balanced and effective sustainable urban development.

The driving factors and spatial evolution of industrial water pollution governance efficiency in the Yangtze River Delta urban agglomeration in China.

In order to explore the effective path to improve the efficiency of industrial water pollution governance efficiency (WGE), this study takes 27 prefecture-level cities in the Yangtze River Delta urban agglomeration in China as the research object, measures WGE through the improved SBM model, and tests the impact of WGE drivers using the spatial Durbin model (SDM). The study found that environmental pollution governance investment (EPGI) is positively correlated with WGE, and industrial agglomeration status (IAS) has inhibitory effects on the improvement of WGE. By testing IAS2, it was determined that the impact of IAS on WGE has a "U"-shaped relationship. The direct impact of EPGI on WGE is 0.5016, and the indirect impact on WGE is 0.6428; the direct impact of IAS on WGE is -0.3036, and the indirect impact on WGE is -0.5158. Among the other tested impact drivers, per capita GDP (PCG), industrial structure (IS), and level of technological innovation (TIL) are positively correlated with the dependent variable WGE, while energy consumption intensity (ECI), environmental regulation intensity (ERI), and degree of openness to foreign investment (FIR) are negatively correlated with the dependent variable WGE. In addition to the impact of the aforementioned main drivers, IAS and EPGI, these six drivers also largely influence and determine the final impact on WGE.

Resilient urban expansion: Identifying critical conflict patches by integrating flood risk and land use predictions: A case study of Min Delta Urban Agglomerations in China

The increasing occurrence of floods due to global climate change is a challenge to the growth of urban resilience. However, few studies have investigated strategies to avoid flood risks during long-term and comprehensive land use and development. To address this issue, this study proposes a method that integrates flood risk and land use predictions to identify critical conflict patches that impact urban resilience. Further strategies for differential development based on the attributes of these critical conflict patches were formulated. Taking the Min Delta Urban Agglomeration (MDUA) in China as a case study, critical conflict patches for expanded urban resilience were identified based on a multi-source data set, a random forest (RF) algorithm, and a patch-generating land use simulation model (PLUS) combined with cellular automata (CA). These patches were categorized into three levels of risk prediction—low, medium, and high risk—based on flood risk attributes. These risk forecast levels were further divided into seven categories. The best land use approach was determined by assessing the quantity, spatial distribution, and risk-based priority ranking of these conflict patches. The results demonstrated that most conflicting patches were located in bare land within built-up areas, urban-rural intersection zones, and remote suburban areas with high traffic accessibility. The identified conflicting patches were applied to three different land use patterns: intra-city renewal, urban edge expansion, and the creation of derived cities. The total number of conflicting patches for land use decisions in the MDUA was 122 (170.82 km2) in 2025 and 685 (1205.81 km2) in 2035. Approximately 108 km2 of urban land was added for resilience expansion following optimization. In all development scenarios, the optimized land use pattern facilitated resilient growth of urban development while maintaining the stability of the flood disaster environment. Based on our methodology and case study, we proposed a decision-making framework to assist cities or potential urban regions to avoid water-related problems and related issues. This approach could also contribute to decisions involving different geographical areas for resilient land use.

Delineating urban growth boundaries by coupling urban interactions and ecological conservation

Urban growth boundaries (UGBs) are efficient tools for controlling disorderly urban expansion/sprawl and ensuring sustainable urban development. Previous studies have delineated UGBs from the perspective of ecological conservation or efficiency improvement and have widely applied them to spatial planning. However, few studies have investigated the role of urban interactions and connections in delineating UGBs, especially in rapidly developing urban agglomerations. Our study proposed a novel framework for UGBs delineation in urban agglomerations by coupling urban interactions and ecological conservation, with two innovative points. First, interurban interactions and connections were quantified to elucidate the transition rules of cellular automata (CA), and ecological conservation was configured as a constraint of the CA model. Second, the hierarchical generalized linear model (HGLM)-CA model was used to simulate the urban land pattern in 2035, and the dilation-erosion algorithm realized the final UGBs. The proposed framework was applied to UGBs delineation in the Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration, a rapidly developing urban agglomeration in China. Furthermore, a comparison among multi-scenario UGBs shows that urban interactions and connections can accelerate the clustering of urban development, and the delineated UGBs are more practical. Our study highlighted the importance of urban interactions for UGBs delineation in urban agglomerations and evaluated a case of UGBs delineation to validate our appeal.

The impact of digital economy on carbon total factor productivity: A spatial analysis of major urban agglomerations in China

Amid global climate imperatives and intensified economic competition, pivoting from China's conventional growth paradigms to innovative economic catalysts emerges as pivotal for its transformative agenda. Drawing on panel data from 141 principal urban conglomerates spanning 2011–2021, this investigation delves into the intricate nexus between the digital economy and carbon total factor productivity. Our empirical analysis unveils a U-shaped trajectory characterizing the digital economy - carbon total factor productivity interplay, accompanied by a congruent spatial spillover dynamic. While digital economy fortifies environmental governance mechanisms through amplified data and media channels, such regulatory frameworks, albeit efficacious in emission abatement, may inadvertently impede economic vitality, thus attenuating carbon total factor productivity. Progressing from digital economy's foundational phase to its comprehensive deployment, its reverberations on capital productivity manifest in a U-shaped curve, invigorating local carbon total factor productivity while potentially undermining adjacent regions. This digital economy - carbon total factor productivity interrelation is accentuated in advanced, non-resource-reliant metropolises with subdued innovation propensities. This discourse proffers nuanced policy implications for sculpting digital economy trajectories and bolstering carbon total factor productivity in a sustainable context.

How does the green efficiency of urban land use evolve in the urban agglomeration of China's middle Yangtze river?

AbstractThe urban agglomeration in the middle reaches of the Yangtze river (MYRUA) is the second largest national urban agglomeration in China, with an excellent ecological foundation. As the construction of urban agglomerations accelerated towards both extension and connotation, land prices of various cities rose, and the contradiction in the demand and utilization of land resources became acute. Therefore, exploring the spatiotemporal evolution and driving mechanisms of land green utilization efficiency within MYRUA had positive significance. This article focused on 28 prefecture‐level cities in the MYRUA and measured their urban land green use efficiency (ULGUE) from 2006 to 2018 using the super slack‐based measure model with undesirable output (Super‐SBM‐U). Then, the improved Fixed Malmquist–Luenberger index was developed to improve the accuracy of dynamic efficiency analysis. Further, the internal and external driving factors were explored using the index decomposition and the spatial error model with fixed effects. The results showed that the land use mode of the urban agglomeration in the middle reaches of the Yangtze River was relatively green, but efficiency gaps still existed. Although the green total factor productivity of the whole cluster has improved by 3.30% a year on average, the excessive pursuit of technological innovation has led to the stagnation of urban land management in various cities, and there are numerous inherent contradictions in urban development. But at the same time, feasible economic development directions, industrial upgrading, high‐quality employees, land marketization, and positive environmental behaviors are conducive to the green and sustainable development of ULGUE. This article depicted the internal land use characteristics of the MYRUA from the perspective of spatiotemporal evolution and efficiency, providing a theoretical reference for the green development and coordinated management of urban agglomeration land.