Chinese Urban Agglomerations Research Articles

Change in aerosol optical depth in Chinese urban agglomerations during the early phase of dual-carbon actions

Dual-carbon policies were implemented by Chinese government to mitigate climate warming; however, changes in aerosol optical depth (AOD) during early phases of these actions (2020-2022) remain unclear. Thus, AOD variations during this period were investigated compared to the baseline (2015-2019 mean) across seven urban agglomerations (UAs) using multi-source data. Significant temporal variations in AOD anomalies (ΔAOD) were observed at annual and seasonal scales, with varying magnitudes. On average, AOD decrease by -0.210, -0.225, -0.180, and -0.038 in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Guanzhong Plain (GZP), and Liaozhongnan (LZN), respectively. In contrast, increases of 0.041 and 0.057 were observed in Pearl River Delta (PRD) and Tianshan Mountains northern foothills (TSBP), while Lhasa metropolitan area (LS) remain stable (0.0001). In 2021, the most significant decrease in AOD was recorded in YRD, whereas the greatest declines in other urban areas were noted in 2022. The largest ΔAOD in LZN, BTH, and YRD occurred in autumn, while the peak ΔAOD in GZP was observed in spring. In contrast, the highest ΔAOD in PRD, TSBP, and LS occurred in summer. These temporal variations in ΔAOD can be attributed to dual-carbon policies, natural and meteorological anomalies, along with the impacts of stringent lockdown policies and abrupt dust events. Path analysis indicated that the effects of various drivers on ΔAOD were interconnected. When interactions among these factors were considered, their influences on ΔAOD were found to be either amplified or altered, highlighting the importance of accounting for interactions within the human-nature-meteorological system in formulation of atmospheric pollution policies. These findings contribute to a deeper understanding of ΔAOD variation mechanisms and provide valuable insights for the development of air pollution reduction strategies at the UA scale.

Simulating the Impact of Urban Expansion on Ecological Security Pattern from a Multi-Scenario Perspective: A Case Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China

Rapid urbanization has further expanded the scale of construction land in urban agglomerations. The encroachment of urban land on ecological land has led to severe ecological problems and threatened the stability of ecological security in urban agglomerations. Analyzing the characteristics of future urban multi-scenario expansion and its impacts on ecological security patterns (ESP) can provide guidance for formulating ecologically sustainable management and control Policies. Our study focuses on Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration as the study area and establishes an ESP. Additionally, a cellular automata (CA) was used to simulate future urban expansion patterns under three scenarios (i.e., natural development scenario, urban development scenario, and ecological conservation scenario). The subsequent analysis evaluates their impact on the ESP. The simulation results indicate that from 2020 to 2030, the CZX urban agglomeration will undergo rapid urban expansion under the natural development scenario and urban development scenario, characterized by outward growth surrounding the existing construction land. In the natural development scenario, urban expansion is primarily concentrated in the northwest and south directions of construction land, the proportion of construction land increased by 2.78%; in the urban development scenario, it is concentrated in the southeast direction of construction land, the proportion of construction land increased by 3.24%. Ecological conflicts in the aforementioned development scenarios primarily arise in the southwestern region of Changsha County, as well as the southern areas of Kaifu District and Furong District. Conversely, under the ecological conservation scenario, the rate of urban expansion has significantly decreased, environmental preservation is upheld at its highest level, and the proportion of construction land only increased by 0.04%. Based on the simulation results, we present targeted recommendations for urban land planning and growth management, as well as the protection, restoration, monitoring, and development of ecological land. These suggestions provide effective guidance for improving the stability of ESP in urban agglomerations and promoting high-quality development in Chinese urban agglomerations.

Quantitative simulation and validation of energy carbon emission efficiency changes in Chinese urban agglomerations

In the context of the global energy dilemma, enhancing energy efficiency has inevitably become a pivotal strategy for mitigating energy waste and promoting sustainable development. Urban agglomerations, as growth poles leading high-quality regional economic development, play a significant role in this process. Within these urban agglomerations, the aggregation of various factors and the substitution of energy resources substantially influence the energy carbon emission efficiency (ECEE) of surrounding cities during their development and maturation phases. This study initially employs a three-stage Data Envelopment Analysis (DEA) to quantitatively analyze the ECEE values of 19 urban agglomerations from 2006 to 2020. It then establishes a methodology for calculating the joint intensity (JI) and joint threshold (JT) of ECEE within these urban agglomerations. Finally, a validation process is executed using MATLAB simulation and verification methodology to fit and authenticate the evolutionary patterns of ECEE in Chinese urban agglomerations. The primary objective of this research is to model the evolution of ECEE, thereby exploring the sustainable development pathways for urban agglomerations in China. The research findings indicate that: (1) The results from the three-stage DEA demonstrate that, after removing the influences of external environmental factors and random errors, the comprehensive technical efficiency (TE) of energy carbon emissions for the 19 urban agglomerations in China experienced a wave-like increase, rising from 0.410 to 0.506 between 2006 and 2020. (2) The calculation results of the constructed JI model indicate that the JT values of ECEE for national-level, regional-level, and local-level urban agglomerations are 1006.92, 226.60, and 160.14, respectively. The national-level urban agglomerations have significantly higher JT values and JI than the other two levels of urban agglomerations. (3) The results of the Matlab simulation verification show that 16 urban agglomerations fit well with the wave-like ascending evolutionary pattern. However, four urban agglomerations exhibit an opposite fitting effect due to the influence of their pillar industries or the small number of cities within these accumulations, making them unrepresentative. Consequently, the evolutionary curve of ECEE in Chinese urban agglomerations generally exhibits a wave-like upward trend over time.

Conflict or Coordination? Ecosystem Services Supply and Demand in Chinese Urban Agglomerations

Conflict or Coordination? Ecosystem Services Supply and Demand in Chinese Urban Agglomerations

Towards a Low-Carbon Target: How the High-Speed Rail and Its Expansion Affects Industrial Concentration and Macroeconomic Conditions: Evidence from Chinese Urban Agglomerations

High-speed rail is a high-standard railway system, which allows trains to operate at high speed. The railway play a crucial role in connecting urban agglomerations, which represents the highest form of spatial organization in the mature stage of urban development, bringing together cities of various natures, types, and scales in specific regions. This paper explores the impacts of high-speed rail and its expansion on industrial concentration and macroeconomic conditions in the period of 2000 to 2019. We use a well-known transportation policy as a natural experiment, utilizing geographic distance data to study the effects of high-speed rail and its expansion on industrial concentration and macroeconomic conditions in urban agglomerations. The results show that high-speed rail increases industrial concentration but leads to a reduction in macroeconomic conditions. Unlike previous studies in this field, we use distance variables to analyze how the expansion of high-speed rail affects macroeconomic conditions and industrial concentration through location advantages. The impacts of high-speed rails vary across urban and non-urban agglomeration cities, resource-based and non-resource-based cities, large and small cities, and eastern, central, and western regions. Our results are robust to the shocks from the global financial crisis, time lags, different distance dummy variables, dependent variables, and endogeneity issues. This study regards the opening up of high-speed rail as both improving air quality and reducing carbon emissions through substituting for urban and aviation transport. Compared to traditional transport methods such as urban and air travel, the efficiency and environmental benefits of high-speed rail make it an important method for reducing greenhouse gas emissions. Consequently, the expansion of high-speed rail could support both economic development and environmental concerns, and it is playing a crucial role in transportation selection for advancing low-carbon economic goals.

Research on the correlation network of carbon emissions and economic between Chinese urban agglomerations

Urban agglomerations are currently at the center of China's efforts to reduce carbon emission and sustainable economic development. It is critical to investigate the carbon emissions and economic correlation between urban agglomerations in order to assist their carbon emission reduction and economic coordinated development. This paper is based on an environmentally extended multi-regional input-output model, and studies the embodied carbon emissions and economic two-dimensional correlation network between major urban agglomerations in China from 2012 to 2017. The research results indicate that the production-based carbon emissions of the production and supply of electricity and heat industry in the Yangtze River Delta urban agglomeration were higher than the consumption-based carbon emissions, with a significant gap. However, the construction and service industries were the opposite. The Hohhot-Baotou-Ordos-Yulin urban agglomeration experienced carbon emission losses and economic benefits. The Yangtze River Delta urban agglomeration achieved a win-win situation for carbon emissions and economy. The Pearl River Delta urban agglomeration benefited from carbon emissions but suffered economic losses. The Central Plains urban agglomeration faced a loss-loss situation for carbon emissions and economy. Major urban agglomerations in China should implement differentiated emission reduction measures based on their own characteristics. For example, the Hohhot-Baotou-Ordos-Yulin urban agglomeration should utilize natural resources to develop green industries and high value added industries.

Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations

Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations

Comprehensive evaluation and optimal management of extreme disaster risk in Chinese urban agglomerations by integrating resilience risk elements and set pair analysis

This study comprehensively assesses the extreme disaster risk faced by Chinese urban agglomerations and optimizes disaster risk management strategies to enhance their disaster resilience and sustainable development. The focus is specifically on droughts, floods, and earthquakes. The integration of resilience risk elements and set pair analysis (SPA) is employed to obtain risk assessment results for different urban agglomerations, including hazard, exposure, vulnerability, and disaster prevention and mitigation capabilities. The study explores the contributions of important indicators, such as exposure, vulnerability, and disaster prevention and mitigation capabilities, in disaster risk assessment, as well as their performance in various risk-prone urban areas. Comparative analysis of the assessment results for different urban agglomerations under various disaster types reveals varying levels of risk in droughts, floods, and earthquakes across different regions. Some areas exhibit higher hazard, exposure, or vulnerability levels, necessitating strengthened disaster prevention and mitigation measures. Regions with high exposure, low vulnerability, and strong disaster prevention and mitigation capabilities may face lower earthquake disaster risk, while regions with low exposure, high vulnerability, and weak disaster prevention and mitigation capabilities may face higher earthquake disaster risk. Moreover, the Comprehensive SPA model and Interval-valued SPA model outperform traditional SPA methods in assessing the existing flood risk level in Chinese urban agglomerations. The integrated model plays a significant role in predicting earthquake disasters, demonstrating the best predictive performance. This study provides essential reference data for urban disaster prevention and mitigation efforts, aiming to enhance urban resilience and disaster resilience capabilities.

An Improved Method to Identify Built-Up Areas of Urban Agglomerations in Eastern and Western China Based on Multi-Source Data Fusion

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

The impact of human capital misallocation on green economy efficiency: Empirical analysis of Chinese urban agglomerations

This paper uses panel data of 260 prefecture-level cities from 2000 to 2019 to explore spatial characteristics such as spatiotemporal divergence and dynamic convergence based on measuring the level of human capital misallocation in Chinese cities and empirically tests the green development effect of human capital misallocation. The study finds that: ① the human capital misallocation levels of the country and the eight major urban agglomerations show a fluctuating downward trend. ② Divergences in human capital misallocation continue to narrow across the country and urban agglomerations, and the difference between inter-urban agglomerations is the primary source of regional difference. ③ The YRD, PRD, MYR, HC, and CP have significant σ-convergence characteristics of human capital misallocation. Meanwhile, the country and each urban agglomeration show significant spatial absolute β-convergence and conditional β-convergence trends. ④ Human capital misallocation significantly negatively affects green economic efficiency, inhibiting green economy efficiency. Therefore, in the future, it is necessary to improve the match between regional industrial structure and human capital allocation through a combination of targeted policy guidance and market mechanisms tailored to local conditions to enhance the efficiency of the green economy. The significance of the study lies in accelerating the accumulation of human capital while realizing the appropriate matching of human capital, releasing the human capital dividend to the maximum extent, and boosting the structural reform of the labor market to realize the transformation of the green economy.

The Relationship between Farmland Abandonment and Urbanization Processes: A Case Study in Four Chinese Urban Agglomerations

Clarifying the relationship between urbanization and farmland abandonment in urban agglomerations (UAs) is crucial to guide the formulation of arable land management policies and strategies for sustainable urban development. Despite numerous studies confirming the correlation between farmland abandonment and certain urbanization factors, the exploration of the patterns and underlying mechanisms of farmland abandonment in China’s UAs remains worthy of systematic investigation. In this study, we conducted an analysis of the spatiotemporal trends in farmland abandonment and examined the key drivers of farmland abandonment in four representative Chinese UAs—Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing (CC), Pearl River Delta (PRD), and Yangtze River Delta (YRD). Our findings reveal that farmland abandonment has been intensified with increasing fragmentation and aggregation patches across these UAs. Abandonment experience was the main driver of continuous abandonment. Moreover, natural conditions persistently influenced farmland abandonment in the BTH, while land urbanization and economic urbanization were predominant drivers in the CC. The abandonment in the PRD was mainly driven by population urbanization, while the abandonment in the YRD was primarily driven by economic urbanization and land urbanization. The research findings provide data support and scientific explanation for land policy-making in these typical UAs under different development strategies.

Identifying building function using multisource data: A case study of China's three major urban agglomerations

As crucial geographical components shaping urban landscapes, defining the functions of buildings plays one of the pivotal roles in planning urban development and facilitating socio-economic human activities. Existing studies in large-scale mapping mainly ignore the environment around buildings, leading to insufficient meeting the demands of large-scale building-level mapping in certain disciplines. To abridge this gap, this article calculates the geometric characteristics of building footprint, the distances from buildings to adjacent objects, and the kernel densities of Point-of-Interest (POIs) to identify building main functions in three Chinese urban agglomerations (Beijing-Tianjin-Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and Pearl River Delta Urban Agglomeration). Leveraging XGBoost, our models attained accuracies of 0.936, 0.934, and 0.940 in the three urban agglomerations, accompanied by kappa coefficients of 0.883, 0.868, and 0.891 respectively, suggesting their capability of identifying building functions in large-scale mapping. Furthermore, the conducted experiments on varying feature combinations and transferability highlight the significance of the built environment in enhancing classification accuracy. Additionally, the analysis of the classification results of buildings in the three urban agglomerations shows harmonized development strategies for Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration, while cities within the Beijing-Tianjin-Hebei Urban Agglomeration do not show the same development strategies. All results demonstrate the feasibility of employing multi-source accessible data to efficiently categorize building functions over large areas in a cost-effective manner. This methodology can be replicated in China and even on a larger scale to offer insights into complex urban spaces and to help urban planners provide policy support such as old city renovations and new city planning.

How Urban Greening Policy Affects Urban Ecological Resilience: Quasi-Natural Experimental Evidence from Three Megacity Clusters in China

This study examines how urban greening policies affect urban ecological resilience through a quasi-natural experimental analysis of various cities in China. Leveraging the country's large-scale promotion of forest city development, we assess the effectiveness of these policies using differences-in-differences modeling across multiple time periods. The results affirm the positive impact of urban greening on resilience, as designated forest cities demonstrated enhanced resilience levels compared to control cities. Notable disparities were also found among the three major Chinese urban agglomerations - the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei regions - in terms of policy effects. While short-term resilience increased under central government oversight, long-term gains relied more on intercity cooperation within agglomerations. It highlights the limitations inherent within China's "experiment under hierarchy" paradigm but also benefits from regional integration. This study contributes novel insights regarding the dynamic relationship between governance structures and urban ecosystem resilience under authoritarian contexts. Innovatively, we explore how intercity cooperation among urban agglomerations affects the long-term effectiveness of urban ecological governance, thereby contributing to a deeper understanding of the complexities involved in fostering resilient urban ecosystems under such governance models.

How to Realize Synergistic Emission Reduction in Future Urban Agglomerations: Spatial Planning Approaches to Reducing Carbon Emissions from Land Use: A Case Study of the Beijing–Tianjin–Hebei Region

Land use changes in rapidly urbanizing regions around the world constitute a principal anthropogenic element fueling the surge in carbon emissions. Here, land use patterns within the Beijing–Tianjin–Hebei (BTH) urban agglomeration under low-carbon development (LCD) scenarios were simulated. Additionally, social network analysis was employed to formulate carbon balance planning guidelines for various administrative regions. (1) In the ecological protection scenario, carbon emissions from land use were 643.42 × 104 tons lower compared to the natural development scenario. Counties with high ecological support coefficients accounted for 22%, making them better suited for predicting outcomes related to low-carbon-oriented land use. (2) The spatial connections of carbon emissions in BTH were closely related, forming the three main carbon emission spatial linkage areas. (3) A carbon balance zoning plan for the BTH in 2035 under the LCD scenario was formulated. Furthermore, key areas for the implementation of carbon peak and carbon neutrality projects were delineated, and targeted measures for carbon reduction and sink increase were proposed. This study provides a new perspective for implementing territorial spatial planning in Chinese urban agglomerations and can aid the government in formulating a reasonable low-carbon-oriented regional planning policy.

Spatiotemporal distribution, sources, and ecological risk of soil polycyclic aromatic hydrocarbons in Chinese urban agglomerations from 2000 to 2020

To investigate the spatial and temporal distribution, sources, and ecological risk of soil polycyclic aromatic hydrocarbons (PAHs) in China’s urban agglomerations from 2000 to 2020, a comprehensive search strategy was employed using the keywords “soil”, “PAHs”, and “city”. A total of 122 relevant studies that provided information on individual PAH content during the specified time period were collected. These studies encompassed 20 urban agglomerations in China, which were further categorized into two distinct periods: 2000 to 2010 and 2011 to 2020. The diagnostic ratio method and principal component analysis were employed to identify the sources of PAHs, and a risk quotient model was used to evaluate the soil ecological risk. The results revealed the average PAH content in the 20 urban agglomerations in China from 2011 to 2020 was 2,439 ± 4,633 ng·g-1, which exceeded the severe pollution level cut-off (> 1,000 ng·g-1). The soil PAH content in the period from 2011 to 2020 decreased by 28% compared to the soil PAH content in the period from 2000 to 2010. Soil PAH pollution was more severe in the northern urban agglomerations than in the southern urban agglomerations. Diagnostic ratios and principal component analysis demonstrated that the principal sources in most urban agglomerations in China were traffic and coal combustion. GeoDetector found that coal and fuel oil consumption were the main factors affecting the spatial differentiation of PAHs. The ecological risk quotient showed that approximately 80% of the urban agglomerations were at a medium-high ecological risk from 2000 to 2010, compared with 72% from 2011 to 2020. Thus, it is necessary to deepen energy structure reform to alleviate the threat of serious pollution caused by coal and fuel oil in urban agglomerations.

Measuring the synergy of air pollution and CO2 emission in Chinese urban agglomerations: an evaluation from the aggregate impact and correlation perspectives

Measuring the synergy of air pollution and CO2 emission in Chinese urban agglomerations: an evaluation from the aggregate impact and correlation perspectives

Assessment the urbanization sustainability and its driving factors in Chinese urban agglomerations: An urban land expansion - Urban population dynamics perspective

Achieving urbanization sustainability is considered to be both an important component of the Sustainable Development Goals and a key way to address the conflict between limited resource constraints and urban continued growth. Regarding the urbanization sustainability, the impact of ecological environment and resource consumption has been widely discussed, while the holistic effect of urban land expansion and urban population dynamics is under-researched. This paper evaluated the urbanization sustainability of 15 urban agglomerations in China from 2000 to 2020, and applied the Geodetector model to investigate its impact mechanism. The results show that at the national level, the level of urbanization sustainability exhibited a trend of first increasing and then decreasing, accompanied by significant spatial differentiation. Spatially, the level of urbanization sustainability in urban agglomerations located in the northeast regions had strong variability during the study period. In contrast, urban agglomerations located in the central region had a lower level of urbanization sustainability due to the unrestricted expansion of urban land. In addition, urbanization sustainability varied according to the development stage of urban agglomerations. Specifically, urbanization sustainability showed a downward trend in the initial development stage, early growth stage and later growth stage of urban agglomeration, and upward trend in mature stage. Economic development level, foreign direct investment, industrial structure and urbanization level were the primary determinants of differences in urbanization sustainability at both national and regional levels. These findings may help to formulate the urban sustainable development planning and differentiated land use policies.

A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.

Spatiotemporal evolutionary characteristics and driving forces of carbon emissions in three Chinese urban agglomerations

Assessing carbon emissions in urban agglomerations has emerged as a pivotal step towards achieving carbon neutrality. In this study, panel data from 75 cities within the three largest urban agglomerations in China—the Yangtze River Delta (YRD), Beijing-Tianjin-Hebei (BTH) region, and Pearl River Delta (PRD)—were constructed to analyse the spatiotemporal evolution of carbon emissions from 2005 to 2020. An indicator system was established, and the Spatial Durbin Model (SDM) was used to investigate the driving forces of carbon emissions. The findings revealed a gradual increase in carbon emissions within these agglomerations, with a notable shift in their spatial distribution from metropolitan areas to broader urban agglomerations, exhibiting significant spatial correlation. Additionally, variations in carbon emissions were observed across different development stages. The SDM indicated that several factors, such as economic conditions, industrialization levels, and population size, influenced the carbon emissions of urban agglomerations. Nevertheless, the impact of these driving factors on carbon emissions was not constant and could be altered by other elements, including green technologies and governmental policies, indicating a decoupling effect between economic growth and carbon emissions. The insights from this study can assist policymakers in formulating strategies to curb carbon emissions and foster sustainable development.

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent.