Contemporary evolution and scaling of 32 major cities in China

Tracking the scaling of urban open spaces in China from 1990 to 2020

Urban open spaces (UOS) are crucial for urban life, offering benefits across individual and societal levels. However, the understanding of the systematic dynamic of UOS scaling with city size and its potential non-linear performance remains a limited clarity area. This study bridges this gap by integrating urban scaling laws with remote sensing data from 1990 to 2020, creating a framework to analyze UOS trends in China. Our findings reveal that UOS growth is sub-linear scaling with city size, exhibiting economies of scale with scaling exponents between 0.55 and 0.65 and suggesting potential shortages. The distribution structure of UOS across cities is becoming increasingly balanced, as indicated by the rising Zipf's slope from 0.66 to 0.88. Southeastern coastal cities outperform, highlighting spatial variations and path dependency in UOS development. Additionally, using metrics of Scale-adjusted metropolitan indicator (SAMI) and the ratio of open space consumption to population growth rates (OCRPGR), we observe a trend towards more coordinated development between UOS and population, with a declining proportion of uncoordinated cities. Our long-term, large sample coverage study of UOS in China may offer positive significance for urban ecological planning and management in similar rapidly urbanizing countries, contributing to critical insights for quantifying and monitoring urban sustainable development.

Spatio-temporal dynamics and human-land synergistic relationship of urban expansion in Chinese megacities

Megacities play important roles in countries' politics, economy, culture, etc. Exploring the law of urban expansion of megacities has important reference for sustainable urbanization. Here, the spatiotemporal dynamics of urban expansion were quantified analyzed in 21 Chinese megacities from 2000 to 2020 with quantitative measurement indicators and explored the human-land synergistic relationship used the decoupling model. Results are as follows: (1) China's megacities experienced significant expansion, and urban expansion characterized as rapid initially but slowed down thereafter. (2) Urban expansion in megacities was characterized as having significant spatial differences, and rapidly expanding megacity centers moved from eastern to midwestern China. (3) Urban spatial expansion of megacities was mainly an enclave type in 2000-2010 and marginal type in 2010-2020. (4) The main type of human-land synergistic relationship in megacities were weak decoupling, there is a significant increase in expansive coupling and expansive negative decoupling in 2010-2020; (5) Lastly, human-land synergy relationship in most megacities was uncoordinated based on the per capita urban land area and decoupling type. The findings of this study can deepen the understanding of the characteristics and quality of urbanization evolution, and provides reference for high-quality development planning and decision-making in megacities.

Vegetation growth enhancement modulated by urban development status

Cities are natural laboratories for studying the vegetation response to global change due to their own climatic, atmospheric, and biological conditions. However, whether the urban environment promoted vegetation growth is still uncertain. Using the Yangtze River Delta (YRD), an economic powerhouse of modern China, as a case study, this paper investigated the impact of urban environment on vegetation growth at three scales: cities, sub-cities (rural-urban gradient) -pixels. Based on the satellite observations of vegetation growth indicated during 2000-2020, we explored the direct (replacement of original land by impervious surfaces) and indirect impact (e.g., climatic environment) of urbanization on vegetation growth and their trends with urbanization level. We found that significant greening accounted for 43.18 %, and significant browning accounted for 3.60 % of the pixels in the YRD. Urban area was turning green faster than suburban area. Moreover, land use change intensity (D) was a representation of the direct impact ω_d of urbanization. The direct impact of urbanization on vegetation growth was positively correlated with the intensity of land use change. Furthermore, vegetation growth enhancement due to indirect impact ω_i occurred in 31.71 %, 43.90 % and 41.46 % of the YRD cities in 2000, 2010 and 2020. And vegetation enhancement occurred in 94.12 % of highly urbanized cities in 2020, while in medium and low urbanization cities, the averaged indirect impact was near zero or even negative, proving that vegetation growth enhancement was modulated by urban development status. Also, the growth offset (τ) was most pronounced in high urbanization cities (4.92 %), but there was no growth compensation in medium urbanization cities (-4.48 %) and low urbanization cities (-57.47 %). When urbanization intensity reached a threshold value of 50 % in highly urbanized cities, the growth offset (τ) tended to saturate and remained unchanged. Our findings have important implications for understanding the vegetation response to continuing urbanization process and future climate change.

Dynamic multi-dimensional scaling of 30+ year evolution of Chinese urban systems: Patterns and performance

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory β_c = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies.

Scaling of Chinese urban CO2 emissions and multiple dimensions of city size

Cities are both the primary cause of global climate change and the key to the mitigation agenda. China's unprecedented urbanization has paralleled a growth in energy demand and urban areas have emerged as the crux of CO₂ emissions reduction in China. There is a crucial need for policymakers to understand how CO₂ emissions scale with city size and adopt economies of scale (cost savings) for mitigation, particularly through a multidimensional lens of city size. This study reveals a set of scaling relations between urban scope 1 CO₂ emissions and five dimensions of city size in 340 Chinese cities, including population (POP), built-up area (BA), building height (BH), specific built-up area (SBA), and built-up volume (BV). The findings show that CO₂ emissions in Chinese cities scale linearly with POP and BA but sublinearly with BA, SBA, and BV, and more diverse regimes exist across various geographic zones, population hierarchies, administrative hierarchies, and governance contexts. The prevalent sublinear scaling regime between CO₂ emissions and SBA and BV demonstrates the potential importance of optimizing the vertical built-up landscapes for establishing a zero‑carbon society. Furthermore, the top 10 % and bottom 10 % performance of individual cities in emissions identified by the Scale-Adjusted Metropolitan Indicator (SAMI) (the smaller the better) highlights the imprints of the socioeconomic context (e.g., Low Carbon City Initiative) on the scaling of CO₂ emissions in Chinese cities, which is critical for developing decarbonization strategies. Our multidimensional analysis can assist in the local-tailored low-carbon development of Chinese cities.

Scaling of Energy, Water, and Waste Flows in China's Prefecture-Level and Provincial Cities

Cities have been envisioned as biological organisms as the integral part of nature's energy and material flows. Recent advances in urban scaling research have uncovered systematic changes in socioeconomic rates and infrastructural networks as urban population increases, providing predictive contents for the comparison between cities and organisms. However, it is still unclear how and why larger and smaller cities may differ in their per capita environmental impacts. Here, we study scaling patterns of urban energy, water, and waste flows as well as other relevant measures in Chinese cities. We divide cities into different groups using an algorithm that automatically assigns cities to clusters with distinct scaling patterns. Despite superlinear scaling of urban GDP, as predicted by urban scaling theories, resource consumption, such as the supply of electricity and water, and waste generation, such as wastewater and domestic waste, do not show significant deviations from linear scaling. The lengths of resource pipelines scale linearly in most cases, as opposed to sub-linearity predicted by theory. Furthermore, we show two competing forces underlying the overall observed effects of scale: a higher population density tends to decrease per capita resource consumption and infrastructure provisions, while intensified socioeconomic activities have the opposite effect.

Evaluation of urban expansion and the impacts on vegetation in Chinese Loess Plateau: a multi-scale study

Vegetation degradation caused by rapid urban expansion is a pressing global challenge. Focusing on the Chinese Loess Plateau (CLP), we use satellite observations from 2000 to 2017 to evaluate the spatiotemporal pattern of urban expansion, and its imprint on vegetation across old urban, new urban, urban, non-urban areas as well as the entire urbanization intensity (UI) gradient (from 0 to 100%). We found a massive increase of urban impervious surface area (UISA) in the CLP from 2000 to 2017, and an uneven expansion of UISA at different urban agglomerations and cities. Less green were found in urban and new urban areas, while old urban and non-urban areas generally showed an improved greening pattern. In addition, the annual maximum EVI (EVI_max) differences between urban and non-urban areas were - 0.0995 on average from 2000 to 2017. The Guanzhong Plain urban agglomeration (GPUA) witnessed the most significant EVI_max differences (- 0.120), and the Ningxia Yanhuang urban agglomeration (NYUA) witnessed the lowest EVI_max differences (- 0.012). The EVI_max showed significantly decreased trends along the entire spectrum of urbanization gradient for 97.4% (38 of 39) cities and five urban agglomerations. The most significant decrease was found in the GUPA (slope =  - 0.0197/10a, p < 0.01), while the smallest drop was found in the NYUA (slope =  - 0.011/10a, p < 0.01). This study offered a fundamental support for understanding the vegetation variation along the urban-rural gradient, which may help stakeholders to make better ecological management policies for urban vegetation in ecologically fragile areas.

Understanding urban concentration of complex manufacturing activities in China

The increasing prominence of urban scaling laws highlights the importance of a systematic understanding of the variational scaling rates for different economic activities. In this article, we utilize several datasets to provide the first systematic investigation of the urban scaling of manufacturing industries in China. Most existing literature assumes that the divergence in urban scaling can be explained by returns to agglomeration, with a few exceptions instead highlighting the role of knowledge complexity or a mixture of both. Our main purpose in this paper is to explain the inter-sector variation of urban scaling rates. In doing this, we provide a clearer approach to demonstrating the relations between urban scaling, returns to agglomeration, and knowledge complexity. Our findings are twofold. First, after uncovering the scaling rates (denoted as urban concentration) and returns to agglomeration (denoted as urban productivity) for each sub-manufacturing sector, we prove that, rather than being a positive predictor, returns to agglomeration is slightly negatively associated with urban scaling rates. This finding reveals that urban concentration of manufacturing may not simply be a natural consequence driven by the maximization of performance. We also show that this result of the manufacturing system contrasts with what would be found in other pure knowledge systems such as patents. Secondly, we measure the complexity for each sector and demonstrate that the variation of urban concentration can be largely explained by their complexity, consistent with the knowledge complexity perspective. Specifically, complex manufacturing sectors are found to concentrate more in large cities than less complex sectors in China. This result provides support for the view that the growth of complex activities hinges more on diversity than on efficiency. The findings above can greatly reduce the current level of ambiguity associated with urban scaling, returns to agglomeration and complexity, and have important policy implications for urban planners, highlighting the significance of a more balanced and diversified configuration of urban productive activities for the growth of innovation economy.

A building height dataset across China in 2017 estimated by the spatially-informed approach

As a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R² = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R² = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R² = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.

Measurement(s)	1 km gridded building height across China in 2017
Technology Type(s)	Sentinel-1 SAR; Spatially-informed Gaussian Process Regression
Factor Type(s)	Sentinel-1 SAR
Sample Characteristic - Location	China

Contemporary Urban Expansion in the First Fastest Growing Metropolitan Region of China: A Multicity Study in the Pearl River Delta Urban Agglomeration from 1980 to 2015

Contemporary urbanization in the Pearl River Delta (PRD) Urban Agglomeration is the epitome of China’s urbanization process as the PRD is the first fastest growing metropolitan region of China. Here, we mapped and quantified the spatiotemporal dynamics of urban expansion for seven major cities in the PRD between 1980 and 2015, using remotely sensed data integrated with landscape metrics, urban growth form, and rank clocks. Results showed that rapid land urbanization occurred in all the seven cities since the execution of reform and opening up, with the annual increase rate ranging from 8.1% to 11.3% among cities, suggesting a relatively equal level of urbanization within the PRD. Socioeconomic drivers underlying urban expansion in Guangzhou, Shenzhen, and Zhuhai can be characterized as “top–down” mechanisms led by the municipal government, while those in Foshan, Jiangmen, Dongguan, and Zhongshan are “bottom–up” ones from low–level administrative organizations. The trajectory of urban expansion in Shenzhen conformed to the diffusion–coalescence urban growth hypothesis in terms of temporal evolution of landscape metrics and urban growth types. This is related to the fact that Shenzhen, the first special economic zone established by the Chinese government, was the first mover of urbanization in China and functioned under the umbrella of a robust socialist market economy relative to a highly centralized planned economy for other cities. The changes of Shenzhen in rank order in terms of both urban population and urbanization area were the largest, exemplifying its evolution from a small fishing village to a metropolis. Furthermore, we found that moving up in the rank order in terms of land use efficiency of wealth creation over time for all cities was accompanied with rank clocking up of population per area (crowd). How to balance trade–offs between the benefits and costs of urbanization is the challenge faced by the urban agglomeration.

On the relation between transversal and longitudinal scaling in cities

Does the scaling relationship between population sizes of cities with urban metrics like economic output and infrastructure (transversal scaling) mirror the evolution of individual cities in time (longitudinal scaling)? The answer to this question has important policy implications, but the lack of suitable data has so far hindered rigorous empirical tests. In this paper, we advance the debate by looking at the evolution of two urban variables, GDP and water network length, for over 5500 cities in Brazil. We find that longitudinal scaling exponents are city-specific. However, they are distributed around an average value that approaches the transversal scaling exponent provided that the data is decomposed to eliminate external factors, and only for cities with a sufficiently high growth rate. We also introduce a mathematical framework that connects the microscopic level to global behaviour, finding good agreement between theoretical predictions and empirical evidence in all analyzed cases. Our results add complexity to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. The longitudinal analysis can reveal differences in scaling behavior related to population size and nature of urban variables. Our approach also makes room for the role of external factors such as public policies and development, and opens up new possibilities in the research of the effects of scaling and contextual factors.

Urban land expansion in China's six megacities from 1978 to 2015

Megacities pose both challenges and opportunities for the transition towards sustainability, and understanding the evolution of urbanization in megacities has profound implications for human societies in an increasingly urbanized world. Here, we mapped and quantified spatiotemporal dynamics of urban expansion in China's six megacities (i.e., Beijing, Chongqing, Guangzhou, Shanghai, Shenzhen and Tianjin) from 1978 to 2015, integrating remote sensing and GIS technology combined with landscape metrics and urban growth type analysis. The results show that six Chinese megacities have all undergone extensive physical expansion over the past four decades, and the magnitude of urban expansion is ranked in the order of Shenzhen, Guangzhou, Chongqing, Shanghai, Tianjin and Beijing, with annual growth rates of 11.02%, 8.07%, 5.80%, 5.37%, 4.56% and 3.46%, respectively. The megacities with smaller initial urban areas were associated with higher urban expansion rates. Differences in the direction, extent and location of expansion for each megacity related largely to the topography, policies and urban master planning. Temporal dynamics of urban growth and landscape metrics suggested that the urbanization processes of Beijing, Shanghai, Shenzhen and Tianjin were basically consistent with urban growth theory, while those of Chongqing and Guangzhou did not match the theory well. Temporal coevolution of the urban area with urban population implied efficiency of urban land use in Shenzhen and Beijing, which are the first special economic zone and the capital of China, respectively. The efficiency of wealth creation in the urbanized area base was observed for all Chinese megacities, signifying the effectiveness of urban expansion as a vehicle to promote economic growth. We face the challenge of managing trade-offs between the benefits and costs of urban agglomeration.

Drivers of urban expansion over the past three decades: a comparative study of Beijing, Tianjin, and Shijiazhuang

Urban expansion is influenced by various natural and anthropogenic factors. Understanding the driving forces of urban expansion is crucial for modeling the process of urban expansion as well as guiding urban planning and management. Here, we quantified and compared the effects of natural, socioeconomic, and neighboring factors on urban expansion and their temporal dynamics in three large cities in the Jing-Jin-Ji Urban Agglomeration: Beijing, Tianjin, and Shijiazhuang. We used remote sensing imagery from six epochs (circa 1980, 1990, 1995, 2000, 2005, and 2010) integrated with GIS techniques and analyzed using binary logistic regression. The relative importance of the three types of driving forces was further decomposed using variance partitioning. We found that the direction and/or magnitude of effects on the drivers of urban expansion varied with both epoch and city. Natural factors placed significant constraints at early stages of urban expansion, but this constraint relaxed over time. As precursor drivers of urbanization, socioeconomic factors significantly influenced urban growth in most epochs for each city. Non-urban lands near existing urban areas were more likely to be urbanized, due to easier access to existing transportation infrastructure and other facility resources. Furthermore, with urbanization, individual effects of drivers tended to be replaced by joint effects, especially for the neighboring factors. Similarities and differences in the individual and joint effects of drivers on urban expansion across cities and through time will provide valuable information for adaptive urban development strategies in the national capital region of China.