Search Results (387)

Rapid global urbanization has exacerbated the conflict between land expansion and ecosystem carrying capacity, making the enhancement of urban land use efficiency (ULUE), a critical pathway for sustainable development. While the digital economy offers a new engine for green transition, its spatiotemporal mechanisms remain underexplored. Taking China, a representative emerging economy, as a case study, this paper investigates the impact of digital transformation on ULUE from 2013 to 2020. By integrating the Super-EBM model with GTWR, we reveal a dynamic evolution where national efficiency improves while regional polarization intensifies. A key finding challenges traditional agglomeration theory, that population density increasingly exerts a negative impact on ULUE, suggesting that congestion costs and ecological pressures are outweighing agglomeration benefits in the digital era. Furthermore, digital infrastructure demonstrates a consistent positive effect by overcoming geographical barriers, whereas environmental regulation exhibits a J-curve effect that is initially constraining but eventually boosts efficiency. These insights provide a roadmap for developing nations to leverage digital tools for balancing economic growth with ecological sustainability, emphasizing the need for spatially differentiated strategies to manage the digital divide and urban congestion. Full article

The continuous augmentation of greenhouse gas and pollution emissions has exerted a conspicuous and negative influence on social production, economic development, and human health. As the digital economy continues to penetrate into various fields of social development, whether the advancement of the digital economy can promote urban pollution and carbon dioxide emission reduction has emerged as a pivotal topic of interest across all sectors of society. This study adopts empirical research methods to delve into the direct static, dynamic effects, spatial effects, and spatial spillover effects of the digital economy on pollution and carbon dioxide emission reduction in the Yangtze River Delta Urban Agglomeration (YRDUA). As evidently suggested by the research findings, the digital economy has an inverted U-shaped impact on carbon dioxide and pollution emissions. As heterogeneity analysis reveals, this inverted U-shaped influence relationship exhibits heterogeneous effects in the high-level group and low-level group of digital economy development. The robustness of this conclusion was demonstrated through robustness testing. Mechanism analysis reveals that, in the early stage of digital development, infrastructure expansion serves as the primary channel driving emissions, whereas in the later stage, green technological progress becomes the key mechanism enabling emission reductions. Finally, the results confirms that digital economy has a significant negative spatial correlation effect on carbon dioxide and pollution emissions, and has an inverted U-shaped spatial spillover effect on neighboring regions. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Fine-scale accounting of land use carbon budgets and identification of their driving factors provides an essential scientific basis for constructing green and low-carbon territorial spatial systems. This is of great significance for optimizing territorial spatial structure and promoting low-carbon development in urban agglomerations. Taking the Central Guizhou Urban Agglomeration as the study area, this study employed a composite carbon coefficient method to construct a 30 m × 30 m grid-based carbon budget index and quantitatively assessed carbon budget changes induced by land use transitions from 2000 to 2024. POI data and a quantile regression model were further integrated to analyze the dominant spatial characteristics associated with carbon budgets, and a carbon budget monitoring and early-warning index was developed to delineate risk zones. The results show that: (1) From 2000 to 2024, the total area of land use change reached 0.95 × 10⁴ km² in the Central Guizhou Urban Agglomeration, accounting for 17.68% of the total land area, and leading to a net increase of 2.3821 million tons of carbon emissions. This increase was primarily associated with the conversion of cultivated land to construction land, with an accelerated growth rate observed in the later period. (2) The spatial patterns of carbon budgets and carbon emission risk levels exhibit a distinct “core–periphery” structure, with high carbon emission levels concentrated in built-up urban areas and lower levels observed in peripheral ecological land. (3) The expansion of construction land is the dominant contributor to the increase in net carbon emissions; industrial, transportation, and residential spaces exert significant positive driving effects, whereas commercial and service spaces show a negative association. (4) Carbon budget risk zoning based on dominant spatial characteristics identifies Guiyang and Anshun as extremely high-risk areas. The results further suggest that reducing carbon-increment spaces and increasing carbon-reduction spaces may play an important role in territorial carbon budget optimization. The integrated “accounting–driving–monitoring” analytical framework established in this study provides a scientific basis for territorial spatial optimization and carbon emission reduction in mountainous urban agglomerations. Full article

The plain river network region is faced with ecological and environmental challenges such as insufficient hydrological connectivity and degradation of ecosystem services under the influence of urbanization and human activities, and therefore attention needs to be paid to river network changes in this region and the synergistic benefits of natural–social–economic multidimensional factors. This study took the Lixiahe region, a typical plain river network region, as the research object, using Mann–Kendall, spatial autocorrelation analysis, random forest, multiple validation and Granger causality test of key drivers to analyze the spatiotemporal evolution of its river network from 2013 to 2025 and quantify driving mechanisms from natural, social and economic factors. The results showed that: (1) From 2013 to 2025, the Lixiahe Plain river network region tended to be trunk and artificial, with the number and connectivity of river networks showing an upward trend while the curvature of river network decreased significantly. (2) The Global Moran’s I index of the Lixiahe Plain river network decreased from 0.612 to 0.534, indicating a continued weakening of spatial agglomeration in the water area and exhibiting characteristics of edge fragmentation. (3) Random forest analysis showed that socioeconomic factors dominated recent river network change in the Lixiahe Plain. Economic factors mainly influenced quantity-related indicators, while social factors were more important for meander degree and connectivity in several ecologically sensitive counties. Multilevel validation demonstrated the robustness and generalization ability of the model. Granger causality analysis further indicated that GDP, road network density, freshwater aquaculture area, and agricultural output statistically preceded changes in key hydrological indicators. These findings suggest that river network management in plain river network regions should move beyond quantity-based engineering expansion and adopt a multi-indicator, spatially differentiated approach. Integrating river quantity, morphology, and connectivity into management can better support the balance between socioeconomic development and ecological protection and promote the sustainable optimization of river network. Full article

Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce the GCTP, a novel framework that integrates guided Geographical zoning, Convolutional Neural Networks (CNN)-Transformer, and the Patch-generating Land Use Simulation (PLUS) model. Initially, guided K-means clustering was employed for geographic zoning to characterize regional spatial non-stationarity. Then, a CNN-Transformer network leveraged self-attention mechanisms to capture multi-scale spatial correlations, obtaining pixel-level development probabilities. Finally, these probabilities were fused with PLUS- Land Expansion Analysis Strategy (LEAS) outputs to drive PLUS- Cellular Automata with multi-type Random Seeds (CARS) for patch-level simulation. The results demonstrate the following: (1) The embedding of guided zoning enabled the model to achieve an Overall Accuracy (OA) of 0.941, effectively mitigating global simulation bias. (2) The optimal simulation performance occurred at a fusion weight of 0.81, yielding a Kappa of 0.8917 and an Figure of Merit (FoM) of 0.3830, significantly exceeding a single model. (3) The 2030 simulation indicates that the GCTP model effectively reduces isolated pixels at urban fringes. The GCTP generates neighborhood patterns with high spatial compactness and geographic consistency. This study highlights the significant advantages of integrating long-range spatial perception with geographical heterogeneity constraints in the land expansion simulation of urban agglomerations. The findings support more precise territorial spatial planning practices. Full article

The rapid expansion of digitalization is reshaping factor mobility and income distribution between urban and rural areas, with important implications for inclusive and sustainable development. Using panel data for 277 prefecture-level cities in China from 2012 to 2022, this study examines how DE affects urban–rural income disparity from the perspectives of nonlinear effects, factor allocation, and spatial interdependence. Compared with existing studies based mainly on provincial data, this paper provides a more fine-grained analysis at the prefecture level and combines mediation, double-threshold, and spatial analysis within a unified framework. The results show that DE has a significant U-shaped effect on urban–rural income disparity, suggesting that digital development may initially narrow the gap but widen it after a certain stage. Urban–rural factor allocation acts as an important transmission channel, and its role exhibits a double-threshold characteristic. The effect of DE also varies across urban agglomeration types and stages of urbanization, with stronger impacts in more developed and urbanized regions. In addition, the direct effect of DE follows a U-shaped pattern, whereas its spatial spillover effect shows an inverted U-shape. These findings indicate that digitalization is not automatically equalizing and that its distributional consequences depend on factor allocation conditions, regional development stages, and spatial linkages. The study provides evidence for policies aimed at reducing urban–rural inequality and promoting more balanced and sustainable development. Full article

Urban agglomerations play a pivotal role in the economic and social progress of regions and countries. Substantial urban expansion, particularly in metropolitan areas, has been generally associated with economic and population growth. This study investigates the spatio-temporal urban expansion of Romania’s major metropolitan areas using Cellular Automata (CA). Focusing on eight metropolitan areas, the paper analyzes land cover dynamics from 2015 to 2020 and it develops a model of urban growth for the years 2025 and 2030. The novelty of the paper is represented by the combination of the CA algorithm and economic complexity for predicting the expansion of built-up areas. To our knowledge it is the first attempt to combine these two aspects in modelling urban growth. The analysis incorporates six variables such as land use, population density, distance to roads, slope, restricted areas and economic complexity to offer insights into future urbanization trends. Our study concluded that CA proved to be a valuable approach for modelling urban growth. The great added value of the paper is related to the integration of the economic complexity index into urban growth model. Doing so, our results not only summarize both economic development and demographic dynamics within major metropolitan areas, but they have provided the urban growth model with a novel and more robust basis for prediction. The results indicate variations in the growth rates and spatial patterns of urbanization, emphasizing the importance of informed urban planning for a sustainable urban development. A major conclusion of the paper is that the actual urban fabric will not suffer significant changes, as it is already compact. Only at the peripheries of the major urban centres there are free space reserves which can be densified by future constructions. Thus, the lack of free space in the city’s core areas and the expensive costs drive the expansion of the built-up areas towards the suburban localities located near the urban centres. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region. Full article

As the largest urban agglomeration and a critical grain production base in northern China, the Beijing–Tianjin–Hebei (BTH) region faces a sharp conflict between rapid urbanization and cropland conservation. Urban expansion inevitably leads to the loss of high-quality agricultural land, posing dual threats to food security and the terrestrial carbon cycle. To accurately assess the ecological costs of this process, this study integrates the CASA model with a time-weighted cumulative model to quantify the spatiotemporal impacts of urban expansion on cropland NPP in the BTH region from 2001 to 2020. Furthermore, a Geographically Weighted Regression (GWR) model was employed to examine the spatially varying effects of key driving factors on cropland NPP loss. The results indicate that urban land in the BTH region expanded by 45.2% over the past two decades, with 91.04% originating from cropland. Despite an overall upward trend in regional cropland NPP driven by climate change and agricultural intensification, the time-weighted cumulative cropland NPP loss attributable to urban encroachment over 2001–2020 reached 29.24 Tg C, which is equivalent to 0.751× the annual total cropland NPP in 2020 (used as a reference benchmark). Crucially, this expansion exhibits distinct ecological selectivity toward high-quality cropland, meaning that urban development has disproportionately encroached upon highly productive land with productivity levels exceeding the regional average. This selective occupation has led to a structural decline in the region’s potential agricultural production capacity. Additionally, GWR results reveal significant spatial non-stationarity in the relationships between cropland NPP loss and its drivers, revealing differentiated response patterns between plains and mountainous areas in terms of socio-economic drivers and physical constraints. These findings expose the hidden threats of urban expansion to food security, providing a crucial scientific basis for formulating differentiated land management policies and coordinating regional urbanization with cropland protection. Full article

Urbanization significantly reshapes urban form, affecting the spatial and quantitative dynamics of urban land use under carbon constraints. However, the role of macro-scale urban form in guiding low-carbon urban expansion remains underexplored. Our study introduces an integrated Cellular Automata (CA) model to simulate urban land use patterns with regard to the low-carbon goal, focusing on urban form optimization. The model employs a top-down strategy to adjust future urban land demand by balancing urban development needs with carbon emission (CE) reduction targets. The adjusted demand is then used to optimize urban form parameters (i.e., the inverse S-shaped function) to predict future urban land patterns and allocate land increments within concentric rings. Subsequently, a bottom-up strategy incorporating carbon sequestration (CS) conservation is applied to refine urban land conversion. The CA model integrates a maximum probability transformation rule to allocate urban land efficiently. We used the model to simulate urban land use patterns under four scenarios (i.e., Low-carbon Urban Development Scenario (L-UDS), Top-up Urban Development Scenario (T-UDS), Bottom-up Urban Development Scenario (B-UDS), and inverse S-shaped constraint Urban Development Scenario (S-UDS)) for the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration in 2035. Results show that the proposed model effectively reconciles the conflict between rapid urbanization and urban carbon management strategies, as evidenced by a 31.25% reduction in carbon emissions in the L-UDS and T-UDS relative to the S-UDS and B-UDS. Furthermore, urban form constraints promote the development of compact and dense urban structures, advancing sustainable urban development goals. This study not only proposes a simulation model capable of effectively promoting compact urban development at the theoretical level, but its findings also offer actionable policy insights for China to address urban sprawl and actively advance low-carbon urban development. Full article

Based on 2011–2022 panel data covering 278 Chinese cities, a panel fixed-effects model, a mediating effect model, and a threshold regression model are used to conduct an empirical analysis of the influence of the digital economy (DE) on urban carbon emission performance from the quantitative and efficiency perspectives. The key findings include the following: (1) An inverted U-relationship is observed between the DE development and urban per capita carbon emissions (PCE), while the nexus between the DE and carbon emission efficiency (CEE) follows a U-shaped pattern. (2) The DE yields a stronger carbon reduction effect once green technology innovation attains elevated levels; conversely, under conditions of nascent green innovation, its principal impact manifests through improvements in CEE. Only when green technology innovation surpasses a critical threshold does the DE begin to reduce carbon emissions. (3) Heterogeneity analysis indicates that, in optimization and upgrading agglomerations, carbon emissions are reduced by DE at a later time point. In growth and expansion agglomerations, the impact of DE on CEE is more evident. Moreover, policy priorities should include fostering innovation-driven digitalization, expanding green technology diffusion, and optimizing regional mechanisms for coordinated low-carbon growth. Full article

Rapid urban expansion poses growing challenges for balancing carbon emissions (CE), economic development, and ecological protection, particularly in coastal urban agglomerations. Although optimization–simulation approaches have been widely applied, explicit consideration of low-carbon objectives remains limited. To address this gap, this study proposes an integrated non-dominated sorting genetic algorithm III (NSGA-III)–patch-generating land use simulation (PLUS) framework that combines multi-objective optimization with spatially explicit land-use simulation. Using multi-temporal land-use datasets (2000–2020) from the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this research examined spatiotemporal land-use transitions and their co-evolution with CE, ecosystem services value (ESV), and GDP under five development scenarios. The results show that construction land expanded by 78% from 2000 to 2020, largely through cropland conversion, which pushed CE upward to 335.4 Mt. For 2030, the Low Carbon Emission scenario reduces CE by 11.8 Mt compared with the natural development scenario. The Balanced Development scenario maintains economic growth while limiting CE increases and stabilizing ESV. Spatially, scenario differences are limited in extent. Over 93% of areas remain unchanged, and variations are mainly concentrated in peri-urban corridors around the Guangzhou–Foshan core. Overall, the NSGA-III–PLUS framework provides a structured approach for coordinating carbon mitigation and land-use planning in rapidly urbanizing coastal areas. Full article