Search Results (137)

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Achieving the synergistic effect of pollution reduction and carbon mitigation (PRCM) is a core pathway for promoting green and low-carbon transition and realizing the “dual carbon” goals, as well as a crucial mechanism for coordinating ecological environment governance with climate action. Based on panel data from three major urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta) between 2008 and 2019, this study employs network centrality and structural holes to characterize urban network positions (UNP), and systematically investigates the impact mechanisms and spatial heterogeneity of urban network positions on PRCM synergy using a dual fixed-effects model. The findings reveal that (1) urban network positions exert significant inhibitory effects on the overall synergy of PRCM, meaning higher centrality and structural hole advantages hinder synergistic progress. This conclusion remains valid after robustness checks and endogeneity tests using instrumental variables. (2) Heterogeneity analysis shows the inhibitory effects are particularly pronounced in Type I large cities and southern urban agglomerations, attributable to environmental governance path dependence caused by complex industrial structures in metropolises and compounded pressures from export-oriented economies undertaking industrial transfers in southern regions. Northern cities demonstrate stronger environmental resilience due to first-mover advantages in heavy industry transformation. (3) Mechanism testing reveals that cities occupying advantageous network positions tend to reduce environmental regulation stringency and research and development investment levels. Conversely, cities at the network periphery demonstrate late-mover advantages by embedding environmental regulations and building stable technological cooperation partnerships. This study provides a theoretical foundation for optimizing urban network spatial configurations and implementing differentiated environmental governance policies. It emphasizes the necessity of holistically integrating network effects with ecological effects during new-type urbanization, advocating for the establishment of a multi-scale coordinated environmental governance system. Full article

The impact of urbanization on the spatial distribution of extreme precipitation has become a major topic in the field of urban hydrology. This study used an urban canopy model (UCM) coupled with a Weather Research and Forecasting model (WRF) to analyze two extreme precipitation events experienced by the Pearl River Delta on 12–13 June (monsoon rainstorm) and 16–17 September (typhoon rainstorm) in 2018. The results showed that both experiments, considering UCM and not considering UCM, can effectively simulate the spatial distribution of two precipitation events in Pearl River Delta urban agglomeration. The root mean square errors of simulation and observation data of the two precipitation events by the UCM scheme were 14.6 mm and 16.7 mm, respectively, indicating relatively high simulation accuracy. The simulated precipitation amounts for the two rainfall events were increased by 2.3 mm and 3.0 mm, respectively. The simulation results of the two precipitation events showed that compared to agricultural land, urban and built-up land have experienced temperature increases of approximately 0.7 °C and 1 °C, respectively. The air-specific humidity of the two precipitation events increased by approximately 0.5 g/kg and 1.2 g/kg, respectively. These differences between UCM and NON simulations confirm that the increase in near-surface air humidity and temperature significantly enhances the intensity of extreme precipitation in the Pearl River Delta urban agglomeration. Full article

The sustainable development of urban agglomerations depends on the effective coordination of water, energy, food, and ecology (WEFE) systems. However, disparities in resource endowments and socio-economic conditions create challenges for achieving a balanced WEFE system across urban regions. This study examines three urban agglomerations in China with distinct development gradients: the Pearl River Delta (PRD), the Hohhot–Baotou–Ordos–Yulin (HBOY) region, and the Central Jilin Province (CJP). A comprehensive evaluation index system is constructed to assess the coupling coordination degree (CCD) of the WEFE system from 2008 to 2022. Through the CCD model, spatiotemporal evolution trends are analyzed, while correlation analysis explores development patterns under varying gradient conditions. A back-propagation artificial neural network (BPANN) model identifies the primary driving factors influencing WEFE coordination. Key findings include the following: (1) the CCD of the PRD, HBOY, and CJP urban agglomerations has improved over time, with CCD values ranging between 0.4 and 0.6, 0.3 and 0.5, and 0.4 and 0.6, respectively. (2) The CCD exhibits a negative correlation with urbanization rates exceeding 70% and industrialization rates but shows a positive correlation with per capita GDP. (3) The dominant contributing subsystems vary; ecology in the PRD (28.76%), food in HBOY (28.83%), and food in CJP (29.32%). These findings underline the importance of tailored strategies for enhancing WEFE system coordination in urban agglomerations with diverse development gradients. Targeted policy recommendations are proposed based on regional characteristics and subsystem contributions. Full article

This study focuses on 67 counties in Guangdong Province, China, and investigates the spatial distribution patterns, regional differentiation characteristics, and functional zoning of rural areas based on the “Production–Living–Ecological” (PLE) functional synergy theoretical framework. Multiple quantitative methods, including the entropy method, spatial concentration degree, and functional identification, were employed. Key findings include: (1) Rural functions in Guangdong exhibit significant heterogeneous evolution. Production functions have generally weakened, showing a spatial pattern of “consolidation in the south and decline in the north”. Ecological functions demonstrate a U-shaped recovery trend, with high-value areas concentrating around the Pearl River Delta urban agglomeration, indicating effective ecological protection policies. Living functions continue to decline due to population mobility and imbalanced public services. (2) Structural transformation of rural function types occurred: Weakly integrated counties decreased (2010–2019), dual function type counties (production–ecological and living–ecological) significantly increased, and ecology-dominant counties predominated, highlighting ecological polarization under policy interventions. (3) Functional evolution is driven by terrain gradients, policy regulation, and industrial relocation. The research provides empirical evidence for optimizing territorial spatial governance and coordinating urban–rural development. Recommendations include promoting dynamic PLE balance through high-standard farmland construction, ecological industrialization cultivation, and cross-regional compensation mechanisms to facilitate rural revitalization and sustainable development. Full article

Satellite retrievals can capture the spatiotemporal variation of O₃ over a large area near the surface. However, due to the unstable functional relationships between variables across spatiotemporal scales, the outlier predictions will reduce the accuracy of the prediction model. Therefore, a validated residual constrained random forest model (RF-RVC) is proposed to estimate the monthly and annual O₃ concentration datasets of 0.1° in China from 2005 to 2020 using O₃ precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O₃ concentrations in China and the four urban agglomerations (Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan–Chongqing (SC)) were calculated. The results show that the annual R² and RMSE of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m³. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual R² increasing from 0.59 to 0.8, and the RMSE decreasing from 17.24 μg/m³ to 10.74 μg/m³, which is significantly better than that of the RF model. The North China Plain is the focus of ozone pollution. Summer is the season of a high incidence of ozone pollution in China, YRD, PYD, and SC, while pollution in the PRD is delayed to October due to the monsoon. In addition, the trend of the O₃ and its excess proportion in China and the four urban agglomerations is not satisfactory; targeted measures should be taken to reduce the risk of environmental ozone. The research findings confirm the effectiveness of the residual constraint approach in long-term time-series modeling. In the future, it can be further extended to the modeling of other pollutants, providing more accurate data support for health risk assessments. Full article

Analyzing the electric power consumption (EPC) patterns of China’s mega urban agglomerations is crucial for promoting sustainable development both domestically and globally. Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based on the K-Means clustering algorithm combined with multiple indicators integrated with socio-economic factors. Combining IPAT theory, regional GDP and population density, the final EPC prediction models were developed. Using these models, the EPC distributions for Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations in 2017–2021 were generated at both the administrative district level and the 1 km × 1 km grid scale. The spatio-temporal dynamics of the EPC distribution in these urban agglomerations during this period were then analyzed, followed by EPC predictions for 2022. The models showed a significant improvement in prediction accuracy, with the average MARE decreasing from 30.52% to 7.60%, 25.61% to 11.08% and 18.24% to 12.85% for the three urban agglomerations, respectively; EPC clusters were identified in these areas, mainly concentrated in Langfang and Chengde, Shanghai and Suzhou, and Dongguan; from 2017 to 2021, the EPC values of the three urban agglomerations show a growth trend and the distribution patterns were consistent with their economic development and population density; the R² values and the statistical values for the 2022 EPC predictions using the improved classification EPC models reached 0.9692, 0.9903 and 0.9677, respectively, confirming that the proposed method can effectively predict the EPC of urban agglomerations and is applicable in various scenarios. This method provides a timely and accurate spatial update of EPC dynamics, offering fine-scale characterization of urban EPC patterns using night light images. Full article

New quality productive forces serve as a catalyst for high-quality development and act as a critical driver of Chinese-style modernization. This study evaluated the degree of new quality productive force in China’s five major urban agglomerations between 2013 and 2022 using the entropy approach. Additionally, it utilized kernel density estimation, the Dagum Gini coefficient, and Markov chain analysis to explore the spatial and temporal dynamics of these forces and their evolutionary trends. The findings revealed the following: (1) Overall, the new quality productive forces in China’s five major urban agglomerations have exhibited a steady upward trend, although the overall level remains relatively low. Among these regions, the Pearl River Delta ranks the highest, followed by the Yangtze River Delta, Beijing–Tianjin–Hebei, Chengdu–Chongqing, and the Urban Cluster in the Middle Reaches of the Yangtze River. Nevertheless, significant potential for improvement persists. (2) The traditional Markov probability transfer matrix suggests that the new quality productive forces in these urban agglomerations are relatively stable, with evidence of “club convergence”. Meanwhile, the spatial Markov transfer probability matrix indicates that transfer probabilities are influenced by neighborhood contexts. (3) Over time, the new quality productive forces in Chinese urban agglomerations show a tendency to concentrate at higher levels, reflecting gradual improvement. The developmental state and evolutionary patterns of new quality productive forces in Chinese urban agglomerations are thoroughly evaluated in this paper, along with advice for accelerating their growth to promote Chinese-style modernization. Full article

Urban agglomerations, as hubs of population, economic activity, and energy consumption, significantly contribute to greenhouse gas emissions. The interconnected infrastructure, energy networks, and shared economic systems of these regions create complex emission dynamics that cannot be effectively managed through isolated city-level strategies. However, these regions also present unique opportunities for innovation, policy implementation, and resource optimization, making them crucial focal points in efforts to reduce carbon emissions. This study examines China’s three major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. Utilizing data from 2005 to 2020 and a comprehensive evaluation model (BCPCAM), the research offers more profound insights into the socio-economic factors and collaborative mechanisms influencing emission trends, facilitating the development of targeted strategies for sustainable development and carbon neutrality. The findings indicate that (1) economic development and carbon control can progress synergistically to some extent, as economically advanced cities like Beijing and Shanghai have achieved their carbon peaks earlier; (2) natural resource endowment significantly affects urban carbon emissions, with resource-rich cities such as Tangshan and Handan, where fossil fuels dominate the energy mix, facing considerable challenges in reducing emissions; and (3) notable differences exist in the growth patterns of carbon emissions between urban and rural buildings, underscoring the need for tailored carbon reduction policies. Full article

Social and economic growth in developing countries has heightened the awareness of environmental challenges, with carbon emissions emerging as a particularly pressing concern. However, the impact of economic development on carbon emission intensity has rarely been considered from the perspective of economic agglomeration, and the relationships and mechanisms between the two remain poorly understood. We analyzed the impact of economic agglomeration on carbon emission intensity and its spatial spillover effect in Guangdong Province, the most economically advantaged province of China, based on a spatial weight matrix generated using geographic proximity, exploratory spatial data analysis (ESDA), and the spatial Durbin model. Between 2000 and 2019, economic agglomeration and carbon emission intensity in Guangdong Province exhibited persistent upward trajectories, whereas between 2016 and 2019, carbon emission intensity gradually approached zero. Further, 80% of the province’s economic output was concentrated in the Pearl River Delta region. Strong spatial autocorrelation was observed between economic agglomeration and carbon emission intensity in the cities, and the economic agglomeration of the province had a parabolic influence on carbon emission intensity. Carbon emission intensity peaked at an economic agglomeration level of 1.2416 × 10⁹ yuan/km² and then gradually decreased. The spatial spillover effect of the openness degree on carbon emission intensity was positive, while GDP per capita and industrial structure had negative effects. Further, the economic agglomeration effects of Guangdong Province increased the carbon emission intensity of major cities and smaller neighboring cities. The stacking effect of economic agglomeration between cities also affected the carbon emission intensity of neighboring cities in the region. During the period of rapid urban development, industrial development and population agglomeration increased resource and energy consumption, and positive externalities such as the scale effect and knowledge spillover were not well reflected, resulting in greater overall negative environmental externalities relative to positive environmental externalities. Full article

Accurate identification of urban agglomeration spatial range is essential for scientific regional planning, optimal resource allocation, and sustainable development, forming the basis for regional development policy. To improve the accuracy of identifying urban agglomeration boundaries, this study fuses nighttime light data, which reflects urban economic levels, with LandScan data representing population distribution and heatmap data indicating population mobility. This fusion allows for identification from a differentiated perspective of population distribution and mobility. We propose a new method for identifying the dynamic boundaries of urban agglomerations through multi-source data fusion. This method not only provides technical support for scientific regional planning but also effectively guides the functional positioning of edge cities and the optimization of resource allocation. The results show that the spatial range identified by NTL_LS has an accuracy of 80.37% and a kappa coefficient of 0.5225, while NTL_HM achieves an accuracy of 89.17% with a kappa coefficient of 0.7342, indicating that the fusion of economic level with population mobility data more accurately reflects the spatial range of urban agglomerations in line with real development patterns. By adopting a differentiated perspective on population distribution and mobility, we propose a new approach to identifying urban agglomeration spatial range. The research results based on this method provide more comprehensive and dynamic decision-making support for optimizing transportation layouts, allocating public resources rationally, and defining the functional positioning of edge cities. Full article

Rapid urbanization in China has profoundly transformed its urban systems, bringing about considerable ecological challenges and significant imbalances between urban growth and ecological health. The Pearl River Delta (PRD) urban agglomeration, as one of China’s most economically dynamic regions, exemplifies the complex interactions between rapid urbanization and environmental sustainability. This study examined these dynamics using statistical yearbook and geographic information data from 1999 to 2018. Through a multi-scale approach integrating panel entropy, coupled coordination analysis, and FLUS models, we evaluated the relationship between urbanization and ecology at both the agglomeration and city levels. The findings revealed that while the overall coordination between urbanization and ecology in the PRD has improved, it remains at a moderate level with pronounced core-periphery disparities. Core cities face increasing ecological pressures and inefficient land use patterns. Simulation results, under three distinct policy scenarios—“unconstrained”, “growth machine”, and “compact and intensive usage/urban renewal”—and validated through field research, indicate that urban renewal presents a viable strategy for optimizing land use and mitigating ecological pressures. The study provides both a comprehensive diagnostic framework for assessing urban health and sustainability and practical intervention pathways, particularly for regions experiencing similar rapid urbanization challenges. The insights gained are especially relevant to other developing countries, offering strategies to enhance urban resilience and ecological sustainability while addressing persistent regional inequalities. Full article

Air pollution is influenced by a combination of pollutant emissions and meteorological conditions. Anthropogenic emissions and meteorological conditions are the two main causes of atmospheric pollution, and the contribution of meteorology and emissions to the reduction of PM_2.5 concentrations across the country has not yet been comprehensively examined. This study used the Kolmogorov–Zurbenko (KZ) filter and random forest (RF) model to decompose and reconstruct PM_2.5 time series in five major urban agglomerations in China, analyzing the impact of meteorological factors on PM_2.5 concentrations. From 2015 to 2021, PM_2.5 concentrations significantly decreased in all urban agglomerations, with annual averages dropping by approximately 50% in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Central Plain (CP), and Chengdu–Chongqing (CC). This reduction was due to both favorable meteorological conditions and emission reductions. The KZ filter effectively separated the PM_2.5 time series, and the RF model achieved high squared correlation coefficient (R²) values between predicted and observed values, ranging from 0.94 to 0.98. Initially, meteorological factors had a positive contribution to PM_2.5 reduction, indicating unfavorable conditions, but this gradually turned negative, indicating favorable conditions. By 2021, the rates of meteorological contribution to PM_2.5 reduction in BTH, YRD, PRD, CP, and CC changed from 14.3%, 16.9%, 7.2%, 12.2%, and 11.5% to −36.5%, −31.5%, −26.9%, −30.3%, and −23.5%, respectively. Temperature and atmospheric pressure had the most significant effects on PM_2.5 concentrations. The significant decline in PM_2.5 concentrations in BTH and CP after 2017 indicated that emission control measures were gradually taking effect. This study confirms that effective pollution control measures combined with favorable meteorological conditions jointly contributed to the improvement in air quality. Full article

The mismatch between urban livability and population distribution can result in overcrowding and excessive pressure on ecosystem services if population growth surpasses urban capacity. Conversely, if urban expansion outpaces population needs, it can lead to underutilized infrastructure and inefficient land use. This study aims to assess the coordination between urban livability and population distribution in five major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Mid-Yangtze River (MYR), and Chengdu–Chongqing (CC). A comprehensive index for urban livability is established, from the aspects of social–economic development and ecosystem service. Additionally, a Coordination Distance Index (CDI) is developed to measure the relationship between urban livability and population distribution. Data from 2010, 2015, and 2020 are analyzed to evaluate the coordination levels and trends across the five urban agglomerations. The results show that from 2010 to 2020, most cities within these urban agglomerations experience improvements in their coordination levels, with the most notable advancements in the PRD and YRD regions. By 2020, the PRD and YRD were classified as having “high coordination”, while BTH, MYR, and CC were categorized as having “moderate coordination”. However, certain cities, such as Chengde in BTH, Shanghai in YRD, Ya’an in CC, and Zhuhai in PRD, still exhibited “low coordination”, highlighting areas requiring spatial planning adjustments. This study introduces a method for quantitatively assessing the coordination between urban livability and population distribution, providing essential insights for policymakers and urban planners to refine urbanization development strategies and population regulation policies in China’s major urban agglomerations. Full article

SDG 9.1.1 and SDG 11.2.1 are significant evaluation indicators of the United Nations Sustainable Development Goals related to transportation accessibility and are used to measure the proportion of the population facilitating the use of road services in rural areas and the proportion of the population facilitating the use of public transportation services in urban areas, respectively. However, there are currently challenges related to incomplete data and the inadequate interpretation of the indicators. In this study, we therefore evaluate the spatiotemporal patterns of the indicators and the number of disadvantaged groups in 337 Chinese cities from 2015 to 2022 based on multi-source data, and explore the spatial aggregation of the indicators and the driving factors. The results demonstrate that the indicator values of SDG 9.1.1 and SDG 11.2.1 reached 99.36% and 90.00%, respectively, in 2022, and the number of vulnerable groups decreased to approximately 1.89 million and 2.82 million. The indicator values of SDG 9.1.1 are high in the eastern part of China and low in the western part of the country, whereas the indicator values of SDG 11.2.1 exhibit spatial agglomeration in regions such as the Pearl River Delta. The average rural elevation and the density of urban public transportation stops are the most influential factors for these two indicators, respectively. The insights and data from this study provide support for improving transportation infrastructure and inequality in China, contributing to the achievement of the 2030 Sustainable Development Goals. Full article