Polycentric Spatial Structure Research Articles

Polycentric Urban Spatial Structure Identification Based on Morphological and Functional Dimensions: Evidence from Three Chinese Cities

Regarding whether a polycentric urban spatial structure is a sustainable urban development model, various studies use different employment center identification methods, resulting in inconsistent conclusions. This paper takes Hangzhou, Wuhan, and Nanning, three Chinese cities at different stages of development as cases, and adopts various methods to identify employment centers to compares the polycentric spatial structure differences from morphological and functional dimensions, and puts forward a multi-dimensional perspective for analyzing the characteristics of the polycentric spatial structure. The results show that the polycentric model of GL_SC threshold method has the relatively good explanatory power, which can best represent the characteristics of urban development in China. The explanatory power of the polycentric model of the Wuhan commuter flow method (adjusted R-squared value is 0.37) is slightly weaker than that of the GL_SC threshold method (adjusted R-squared value is 0.40), indicating that the agglomeration development forces are equivalent to the decentralized development forces and it is a balanced polycentric development structure. In Hangzhou and Nanning, the GL_SC threshold method (adjusted R-squared values are 0.28 and 0.36, respectively) is stronger than the commuter flow method (adjusted R-squared values are 0.19 and 0.33, respectively), which reflects the strong characteristics of agglomeration polycentric development. However, from the comparison of the number and distribution of employment center identification, the polycentric development characteristics of Hangzhou are stronger than those of Nanning. The comparison of the identification results of the morphological and functional dimensions shows that the two dimensions as a unified evaluation process can more fully reflect the characteristics of an urban polycentric development structure, thereby providing preliminary research support for the study of the impact of polycentric structure on economic development, air pollution, work and residential commuting, etc.

How did polycentric spatial structure affect carbon emissions of the construction industry? A case study of 10 Chinese urban clusters

PurposeDue to the close connection between urban cluster and carbon emissions (CEs) but a lack of study on it of the construction industry, this paper aims to explore the relationship between the polycentric spatial structure (PSS) of the urban clusters and CEs of the construction industry (CECI).Design/methodology/approachThis research uses panel data of 10 Chinese urban clusters from 2006–2021, calculates their PSSs in the aspects of economy and employment and adopts a panel regression model to explore the effect of the spatiotemporal characteristics of the PSSs on the CECI.FindingsFirst, the CECI in 10 Chinese urban clusters showed a rising trend in general, and the CECI in the Yangtze River Delta (YRD) was much higher than those in the rest of urban clusters. Second, both Shandong Peninsula (SP) and Guangdong-Fujian-Zhejiang (GFZ) exhibited high degrees of polycentric characteristics, while Beijing-Tianjin-Hebei (BTH) showed weaker degrees. Third, the results demonstrated that the polycentric development of urban clusters did not help reduce the CECI but rather promote the CE. The polycentric index, considering the linear distance from the main center to sub center, had a more significant impact on the CECI.Originality/valuePrevious studies have investigated the impact of urban spatial structure (USS) on CEs; however, few of them have studied in the field of construction industry. Moreover, most research of CEs have concentrated at the national and provincial levels, with fewer studies on urban clusters. This paper contributes to this knowledge by investigating how the PSS of urban cluster influence the CECI.

The effect of polycentric urban spatial structure on PM2.5: An empirical study of China

Environmental pollution causes widespread concern due to rapid urbanization and the ongoing expansion of the urban scale. Although the polycentric spatial structure is an important idea for regional environmental governance, the actual impact of optimizing the spatial structure differs significantly depending on socioeconomic variations. Existing studies lack an analysis of the effect of spatial structure on PM2.5 pollution. In this study, population centers from 2001 to 2018 in 369 Chinese cities were chosen to calculate the polycentricity. Then, the two-way fixed effects model and the threshold model were constructed to investigate the effect of spatial structure on PM2.5. The results indicate that cities in China have a tendency for polycentric spatial structure, with the most prominent in the east. According to the panel model results of polycentricity and PM2.5 concentration, polycentricity significantly increases PM2.5 pollution. Moreover, there is a significant single threshold effect for the population with a threshold value of 1.75 million. When the population exceeds 1.75 million, the effect of polycentricity on PM2.5 pollution becomes weaker. These findings can help provide valuable empirical analysis from the urban planning standpoint for preventing and managing PM2.5 pollution.

Whether polycentric spatial structure is conducive to regional coordinated development: A study on urban agglomerations in China

AbstractWith the aggravation of congestion, pollution, and other negative externalities generated by continued urbanization, polycentric strategies have gradually become one of the main urban and regional spatial strategies. It remains unclear, however, whether the polycentric structure is conducive to regional coordinated development. This study examines these issues using China's urban agglomerations (UAs) as a sample. The Prolonged Artificial Nighttime‐light Dataset of China (PANDA) was used to measure the polycentric structure. The results of the nonparametric identification of sub‐centers show that almost all of China's UAs exhibit multiple (sub)centers, and the number of (sub)centers in most UAs has increased from 1992 to 2020. Empirical analysis shows that a polycentric structure is beneficial for narrowing regional disparities within the UA, and the narrowing effect of the polycentric structure on regional disparities increases as the distance between centers increases. Furthermore, a polycentric spatial structure is more conducive to the economic growth of small cities in UA, thereby promoting the coordinated development of regions within UA.

Polycentric Spatial Structure Evolution and Influencing Factors of the Kunming–Yuxi Urban Agglomeration: Based on Multisource Big Data Fusion

The polycentric spatial structure is the most common spatial form of urban agglomerations, so exploring the evolution of this structure and analyzing its influencing factors is of great significance for the optimization of the spatial structure of urban agglomerations. However, there are relatively few studies on the topic that fuse multisource big data analysis, especially in the urban agglomeration of Western China. Therefore, this study uses a fusion of nighttime light (NTL) data, point of interest (POI) data and LandScan data to identify the polycentric spatial structure and its evolution in the Kunming–Yuxi (Kunyu) urban agglomeration and analyzes the factors that have dominated its evolution at different periods using geographic detectors. Results show that the fusion of multisource big data are more in line with the actual development process of the Kunyu urban agglomeration and the factors that have dominated the spatial evolution at different periods vary but the government and sectors have gradually become increasingly important. This study provides a feasible path for exploring urban spatial evolution through the fusion analysis of multisource big data in the Kunyu urban agglomeration and provides a reference for the key directions of urban agglomeration planning and development at different periods.

Does polycentric spatial structure narrow the urban-rural income gap? – Evidence from six urban clusters in China

As urbanization and economic development continue to accelerate, social equity issues are becoming increasingly prominent and the urban–rural income gap is an important manifestation of this. This study uses the data of urban clusters from 2010 to 2019 and focuses on the relationship between the polycentric spatial structure of urban clusters and the urban–rural income gap. The findings show that the development of urban cluster polycentricity can reduce the urban–rural income gap, and the higher the degree of polycentricity in morphology, the smaller the gap. However, the relationship between polycentric spatial structure and the urban–rural income gap is “U” shaped, indicating the need for rational planning of urban polycentric development patterns rather than simple expansion. In addition, the polycentric spatial structure of urban clusters can reduce regional disparities by promoting factor mobility and industrial structure optimization. However, this depends on the level of industrial development and infrastructure. This study highlights the importance of a balanced approach to urban development that takes into account regional disparities and issues of social equity.

Polycentricity: The last episodes or the new season?

This article presents a systematic review of empirical studies on polycentric spatial structures at a regional scale in order to assess their effectiveness as prescriptive and normative models in spatial planning. The results show that very few studies have emphasised primarily the positive effects of polycentricity, while a large number have evaluated the performance of non-polycentric (monocentric) structures more positively. Our study shows that evaluating the effectiveness of polycentricity as a normative model is both theoretically and empirically challenging, and that polycentricity is still the subject of a research agenda with hypotheses that need to be tested. The findings indicate that polycentricity is not the superior model it has been frequently advertised as and that its effectiveness is significantly influenced by a range of factors relating to its political foundation, weak theoretical positioning, ambiguous conceptualisation, context dependence, and highly variable governance frameworks. The study recommends that scientific theorising of polycentricity should be aligned with close scrutiny of the relevant contexts to overcome its idealistic nature and lack of adaptability. The article cautions planners and policymakers against a sweeping promotion of polycentric development, as the implementation of this concept is not necessarily associated with fostering economic performance, social cohesion, and environmental sustainability.

Polycentric spatial patterns and urban economic growth quality: A discussion from fintech development

Based on 138 Chinese cities from 2004 to 2019, we examine the effect of polycentric spatial patterns on urban economic growth quality (EGQ) from the perspective of urban fintech development. We found that the morphological polycentric spatial pattern does not improve urban EGQ overall. Still, there is a significant positive moderating effect of urban fintech development on the relationship between the two. Further, in the eastern region, fintech development can mitigate the negative effect of the morphological polycentric spatial structure on urban EGQ. In medium-sized and small cities, fintech development can also positively moderate the negative relationship between the two.

Characteristics and Influencing Factors of Population Migration under Different Population Agglomeration Patterns—A Case Study of Urban Agglomeration in China

China’s urban agglomerations (UAs) are striving to build a new development pattern oriented towards the new era and new stage, and the population distribution is facing new problems of synergy with the layout of labor factor productivity and regional coordinated development. Therefore, this study couples UAs with population distribution, using data from three population censuses and nighttime light data in 2000, 2010, and 2020, to measure the population agglomeration patterns of Chinese UAs using population agglomeration indicators and to explore the influencing factors and spatial stratification heterogeneity characteristics by constructing an econometric model. The results show that: (1) the population agglomeration patterns of Chinese UAs can be classified into four major categories: weakly polycentric, weakly monocentric, strongly monocentric, and strongly polycentric UAs, and China’s UAs are in a low-level stage dominated by weakly polycentric UAs at present. (2) In terms of influencing factors, 15 indicators, such as economic development and social conditions, are important factors affecting the population agglomeration patterns of the four UAs, but their effects vary greatly due to specific patterns. (3) For specific agglomeration models, the total passenger volume has always been the strongest positive influencing factor for weakly polycentric UAs; the industry location entropy index, scale of fiscal expenditure, and total passenger volume in municipal districts are relatively strong positive effects to weakly monocentric UAs, the per capita GDP and urbanization rate are relatively strong positive effects to strongly monocentric UAs, and the urbanization rate is always the strongest positive effect to strongly polycentric UAs. The refined analysis of population migration in Chinese UAs in this study enriches the theoretical results related to population migration in Chinese UAs to a certain extent and provides a feasible basis for the development of new development patterns in Chinese UAs and the formulation of regional population policies in the new stage. Meanwhile, this study divided the polycentric attributes of different UAs, which provide a reference for the theoretical development of polycentric spatial structure of UAs.

Influence Mechanism of Polycentric Spatial Structure on Urban Land Use Efficiency: A Moderated Mediation Model.

Under the background of green development, the spatial structure of urban agglomerations (UA) has an important impact on urban land use efficiency (ULUE), but few studies have explored the impact mechanism between the two. This research explores the impacts of polycentric development on ULUE of UA, using data for 140 cities in China's top ten key UA covering the period from 2004-2019. The linkage between polycentric development and ULUE is explored by estimating models of determinants of ULUE. This research also examines the mechanism of the polycentric spatial structure of UA on ULUE by using a moderated mediation model. The main findings of the research can be concluded as below. The eastern UAs show a mostly polycentric spatial structure, whereas the central and western UAs show a weak polycentric spatial structure. The polycentric spatial structure of UA has a positive impact on ULUE. An inverted U-shape curve depicts the relationship between the polycentric spatial structure of UA and ULUE. However, the mediating variables, integration of industrial structure and factor mobility have a positive and partially mediating effect between the polycentric spatial structure of UA and ULUE. The infrastructure level has a positive U-shaped regulation effect, in which the impact coefficient of transport infrastructure is more significant. These findings provide empirical evidence for the coordinated development of China's regional space planning and ULUE.

City centrality, population density and energy efficiency

We use continuous night light data and LandScan population data to construct a monocentric index to measure whether a city tends to be a monocentric or polycentric spatial structure, and estimate the impact of such structure on energy efficiency by using a two-way fixed effects model. In order to solve the endogeneity of urban structure, the historical census data and topographic relief data at the city level are used to construct the instrumental variables. We find that an increase in the urban monocentricity index would significantly reduce the level of the urban energy intensity. Meanwhile, the relationship between monocentricity and urban energy intensity is U-shaped. When the degree of monocentricity is at relatively low level, its impact on energy efficiency is positive, implying the dominance of agglomeration effect. When economic agglomeration is greater than a certain degree, the impact of monocentricity becomes negative which results from the dominance of congestion effect. Moreover, impact of urban structure on energy intensity varies with urban population density. An increase in population density would weaken the negative impact of monocentricity on energy intensity, that is, in sparsely populated cities, the monocentricity is more conducive to energy saving. In terms of city form, monocentric structure may improve energy efficiency in the cities with poor transportation infrastructure more than in the cities with good transportation infrastructure. Monocentricity facilitates energy efficiency primarily by increasing corporate innovation and saving commuting time and distance. We perform a series of robustness tests and the findings are basically consistent with the baseline result. Our findings suggest that the cities at this stage should continue to strengthen the development of monocentricity, pay attention to compactness of urban spatial structure, and develop the polycentric structure cautiously. Meanwhile, in the development process, the local population density and the carrying capacity of the transportation infrastructure should be taken into consideration to avoid the negative effects caused by the congestion in main center.

Urban spatial structure and environmental efficiency: Empirical analysis from prefecture-level cities in China

China’s environmental problems are emerging with the continuous development of the economy and urbanization. The study of the impact of urban spatial structure on environmental efficiency has important significance concerning exploring ways to reduce the environmental cost of economic activity. Based on the fusion-corrected DMSP-OLS and NPP-VIIRS nighttime light data, this paper constructs spatial structure indicators of 276 prefecture-level cities and then empirically tests the impact of urban spatial structure on environmental efficiency using a fixed-effects model. The results show that: 1) The monocentric spatial structure has higher environmental efficiency than the polycentric spatial structure. 2) The mechanism analysis shows that the monocentric spatial structure could improve environmental efficiency through learning, sharing, and matching. 3) Population size and density have a robust moderating effect that when the resident population size of a city is more than 5 million or the population density is beyond 280 persons/km2, the monocentric spatial structure has a negative impact on environmental efficiency. In contrast, the polycentric spatial structure is more effective. 4) The point-based household registration policy adopted by the government to control population size has no significant impact on environmental efficiency, while the environmental information disclosure policy can effectively play a role. Our findings provide a basis for the Chinese government to formulate urban planning and environmental protection policies and offer the experience to other developing countries.

The Formation of a Polycentric City in Transitional China in a Three-Level Analysis Framework: The Case Study of Hangzhou

Given cities’ different development backgrounds, the spatial evolution of each city has a path-dependent development track. Understanding the characteristics of urban spatial structure evolution in contemporary China is essential for sustainable urban development. By establishing a theoretical framework comprising a macro-background, planning tool, and resource allocation, this paper takes Hangzhou as an example to analyze the evolutionary process of the polycentric spatial structure and reveal the formation characteristics of its polycentric urban spatial form. The results show that (1) its institutional background and planning tools are the driving forces behind the formation of the polycentric structure in Hangzhou, i.e., the evolution of the polycentric structure mainly relies on planning guidance and government forces rather than being market-driven. (2) Under the government-led mode of polycentric formation, urban elements, such as education, medical care, transportation, and green space, are still concentrated in the main urban center, while subcenters lack the necessary public facilities and are underdeveloped. (3) The spatial distribution of the population and nightlights also shows the trend of diminishing concentric circles with the main center at the core. Therefore, it is impossible to implement polycentric cities by decentralizing the population and industries alone. The comprehensive consideration of capital investment, production services, and the supply of life service facilities is also required. This empirical study can complement empirical evidence in order to verify the validity of polycentrism as a potential solution that can alleviate the sprawl of central urban areas.

Influence mechanism of urban polycentric spatial structure on PM2.5 emissions in the Yangtze River Economic Belt, China

Urban spatial structure has important impact on environmental pollutant emissions , but few studies have investigated the possibility of a bidirectional influence mechanism between the two. This study used panel data of 108 prefecture-level cities in the Yangtze River Economic Belt (China) from 2009 to 2018. The objective was to test empirically the dynamic emission reduction effect of polycentric spatial structure on environmental pollution using the STIRPAT model as a framework to decipher the bidirectional causality effect. System generalized method of moments and the Sobel Test were applied to construct a panel intermediary effect model to verify the mechanism of urban polycentric development mode on urban haze pollution in China. Results showed that the urban polycentric structure in the Yangtze River Economic Belt has produced substantial pollution reduction effects, and that there are heterogenous regional, size, and functional differences in the impact of urban spatial structure of the Yangtze River Economic Belt on the environment. Moreover, the results of the intermediary effect test showed that market integration and industrial structure optimization are important ways via which to improve environmental pollutant emissions using the polycentric spatial development model. On this basis, suggestions were proposed for optimizing urban spatial structure to promote high-quality development of the Yangtze River Economic Belt. • Urban polycentric spatial structure has a dynamic abatement effect on environmental pollution. • There are heterogeneous regional, size, and functional differences in the influence mechanism. • Market integration and industrial structure optimization play an intermediary role. • Two intermediary variables can more effectively release the positive externality of polycentric spatial structure. • Cities should strengthen polycentric spatial structure to reduce PM 2.5 pollution.

Do polycentric structures reduce surface urban heat island intensity?

City planners are increasingly captivated by the possibilities of transforming urban spatial structures as an important strategy for reducing heat island intensity. This study addresses the mixed findings of polycentric urban spatial structures on surface urban heat island intensity, using a multiple regression method and pathway analysis for the city region and city proper in China. We found that the polycentric spatial structure can reduce the surface urban heat island intensity at the scale of both the city region and city proper, although the reduction was economically insignificant at the city region scale. Path analysis explained the reduction in surface urban heat island intensity in the city proper: polycentric structures disperse industrial firms to the “rural” areas and centralizes green spaces in the “urban” areas. Our findings may serve as references for policymakers when optimizing urban spatial structures to improve the thermal environment.

Does a Polycentric Spatial Structure Help to Reduce Industry Emissions?

City planners are increasingly drawn to ways of transforming urban spatial structure as an important strategy for reducing pollutant emissions. As its main contribution, this paper uses firm-level emissions data to quantify impact mechanisms related to factor flow, firm size, and division of labour. We examine the effects of spatial polycentricity on firm-level industrial emissions, using a pooled cross-sectional model, based on emissions data from individual firms in China. We show that, all else being equal, polycentric spatial structures help to reduce the emissions of industrial firms. This finding is not affected by index measures, changes in industrial structure, or city-sample selection. A mechanism analysis shows that polycentric structures not only enhance the emission-reduction effects of factor flow and firm size, but also reduce firm-level emissions by strengthening the urban division of labour. Our findings support the emission-reduction performance of polycentric spatial structures, promoting the integration of city planning and industrial policies that jointly contribute to reducing firm-level emissions and preventing and controlling air pollution.

Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion

Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.

Panacea, placebo or pathogen? An evaluation of the integrated performance of polycentric urban structures in the Chinese prefectural city-regions

Although the polycentric urban structure is perceived as a bridging concept to achieve various policy goals, such as promoting economic efficiency, diminishing spatial inequity, and encouraging environmental sustainability, most studies tend to focus only on a singular dimension of polycentricity. Drawing on the non-radial direction distance function approach, our study aimed to provide a three-dimensional integrated assessment of the benefits of polycentric spatial structure in the Chinese prefectural city-regions. However, our findings were not as expected; specifically, a lower degree of polycentric city structure is robustly associated with better overall performance, and this performance is size-dependent and dominated by economic efficiency. These results shed light on the prevailing role of agglomeration economies and suggest that a polycentric-oriented spatial strategy may not be a feasible policy for Chinese prefecture-level cities.

Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019

Curbing carbon emissions by restricting economic growth could decrease human well-being across the world and especially in developing countries, suggesting that we need to find alternative approaches to reducing carbon emissions. Against this background, this paper investigates the relationship between urban spatial structure and carbon emissions in the Chinese context from 2002 to 2019. Specifically, urban spatial structure of 286 Chinese cities, represented by the two dimensions of polycentricity and compactness, are calculated based on the gridded (1 km × 1 km) LandScan dataset on population, while carbon emissions of these cities are aggregated from the gridded (1 km × 1 km) Open-source Data Inventory for Anthropogenic CO2 (ODIAC) dataset on carbon emissions. The empirical results based on different regression models find that overall (1) more dispersed and less monocentric (i.e., less compact and more polycentric) cities are often associated with lower levels of carbon emissions, ceteris paribus; (2) the impact of polycentricity on carbon emissions could be moderated by the economic development levels of Chinese cities. For cities with gross domestic product of more than 173 billion yuan, a more polycentric spatial structure is usually associated with a higher level of carbon emissions; (3) a city’s urban spatial structure could have positive spatial spillovers on carbon emissions of its neighboring cities.

Polycentric spatial structure and its economic performance: evidence from meta-analysis

ABSTRACT Although there seems to be a widely shared expectation that polycentricity increases economic competitiveness, the empirical findings are inconsistent. As the first meta-analysis, this paper examines whether emerging empirical evidence provides an insight into the polycentricity–productivity effect. Based on 139 estimates, we find no compelling evidence to support that polycentricity is either beneficial or detrimental for economic development. Further meta-logit regressions show that the measurement of polycentricity and the control of agglomeration economies indeed affect the polycentricity–productivity effect. In contrast, measuring polycentricity from different perspectives and the potential endogeneity do not appear to significantly influence this effect.