Search Results (617)

The rural collective property rights system reform (RCPRSR) is a pivotal institutional innovation for revitalizing rural resources, optimizing factor allocation, and advancing urban–rural integration—a core goal of sustainable land use planning. This study evaluates the reform’s impact on county-level urban–rural integration using panel data from 1106 Chinese county-level administrative units during 2013–2020. Treating the staggered rollout of reform pilots as a quasi-natural experiment, we employ a multi-period difference-in-differences approach. The results show that the RCPRSR significantly promotes urban–rural integration, a finding robust to a series of sensitivity checks. The policy effects exhibit marked heterogeneity: the dividends of narrowing the urban–rural development gap are more pronounced in poverty-stricken counties and areas with lower baseline integration levels. Mechanism analysis reveals two pathways—population agglomeration and industrial structure optimization—through which the reform operates, specifically manifested as enhanced county population carrying capacity, accelerated tertiary industry development, and deepened secondary–tertiary industrial integration. These findings provide empirical evidence for optimizing rural property rights reform and advancing sustainable urban–rural development. Full article

Achieving sustainable urban development and optimizing the urban scale structure are central priorities in global governance. However, the relentless population agglomeration in Chinese megacities, despite astronomical living costs, presents a prominent “China Paradox” that seemingly defies classical spatial equilibrium theories. This study decodes this paradox by endogenizing the strategic land supply behaviors of local governments. Utilizing a comprehensive panel dataset of 287 Chinese prefecture-level cities from 2006 to 2020, we construct a multi-dimensional mediation framework and a panel threshold model to investigate how the structural misallocation of land—specifically, the pro-industrial and anti-residential bias—reshapes urban migration dynamics. Empirical results reveal that this land allocation bias acts as the fundamental institutional engine driving urban polarization. Analysis of the transmission pathways reveals a complex push–pull dynamic at the core of this paradox. The artificial restriction of residential land drives up housing prices, generating a profound centrifugal “push” force. However, this dispersion effect is entirely neutralized by two formidable centripetal “pull” forces: industrial co-agglomeration fueled by subsidized manufacturing land, and premium public service capitalization financed through lucrative land revenues. Furthermore, this demographic pull effect exhibits a pronounced inverted U-shaped dynamic, peaking during the rapid growth phase but diminishing precipitously once cities cross the threshold into highly developed megacities (LnGDP > 11.525). These findings highlight the ultimate unsustainability of the land-driven urbanization model. We propose a paradigm shift towards sustainable urban governance, advocating for stage-specific land supply reforms and the transition from monopolistic land finance to a sustainable property tax system to foster a spatially just and resilient urban hierarchy. Full article

Urban fire risk in high-density cities is characterized by complex spatial heterogeneity and nonlinear relationships with the built environment, population distribution, and climatic conditions. However, most existing studies rely on linear assumptions and offer limited interpretability. To address this gap, we developed an interpretable analytical framework that integrates the CatBoost model with SHAP (SHapley Additive exPlanations), using Futian District in Shenzhen as a case study. We constructed a fire risk surface from historical fire incident data using kernel density estimation (KDE) and incorporated multiple urban environmental factors—including points of interest (POIs), road networks, and meteorological variables—as explanatory variables. The CatBoost model captured nonlinear relationships, while SHAP quantified feature importance and revealed interaction effects. The results show that urban fire risk is strongly associated with the spatial agglomeration of population-related facilities, especially high-density commercial and residential areas, as well as thermal conditions. Several variables exhibit clear nonlinear threshold effects, with their influence on fire risk varying markedly across different intensity ranges. Interaction analysis further indicates that combinations of built-environment characteristics and climatic factors jointly shape the spatial pattern of fire risk. These findings provide empirical insights into the spatial mechanisms underlying urban fire risk and highlight the value of interpretable machine learning in urban safety research. The proposed framework offers a practical tool for developing more targeted, evidence-based fire risk management strategies in high-density urban areas. Full article

Rapid urbanization in China has intensified pressure on regional ecological systems, necessitating quantitative assessment of urbanization–eco-environment interactions. This study investigates the coupling coordination between urbanization and eco-environmental quality (EQ) in five major Chinese urban agglomerations from 2001 to 2024 using multi-source remote sensing data. An improved eco-environmental index (AMEI) and an urbanization index (MCNLI) were constructed, and a dual peri-telecoupling-inspired coordination model (DPTCM) was developed to capture both local interactions and distance-decayed cross-regional spillover effects. Results show that, across the five urban agglomerations, the average urbanization level increased from 0.24 to 0.33 (38.9%), while EQ declined from 0.44 to 0.39 (11.3%). The Pearl River Delta exhibited an opposite trend, with EQ increasing by 18.7%. Despite this ecological decline, the overall coupling coordination degree increased from 0.55 to 0.65 (19.3%). Spatially, higher coordination levels are concentrated in the Pearl River Delta and Yangtze River Delta, whereas the Central Yangtze River and Chengdu–Chongqing regions exhibit relatively lower coordination. Although ecological conditions have generally declined, the coordination between urbanization and the eco-environment has improved across regions, with significant spatial heterogeneity. This study provides a comprehensive quantitative framework for assessing urban-ecological interactions and offers insights for guiding sustainable urban development in densely populated regions. Full article

The urban heat island effect, a typical rapid urbanization issue, arises from natural surfaces covered by impermeable layers via urban sprawl. To clarify its unclear response to urban expansion under human–land synergy, this paper proposes a multidimensional urban expansion model and a random forest–intelligence integrated method for high-precision large-region population mapping. Taking the Pearl River Delta urban agglomeration as a sample, its urban expansion is divided into five modes to explore thermal environment impacts. The results show: (1) The proposed random forest–intelligence method achieves 84% overall accuracy in 30 m resolution population mapping. (2) The Pearl River Delta urban agglomeration is dominated by vertical expansion, but all cities have population-shrinking regions, especially around Guangzhou and Shenzhen. (3) From 2010 to 2020, Pearl River Delta urban agglomeration impervious surface expansion and population growth were mismatched: impervious surface extended to fringes, while population grew in core areas. (4) The expansion of impervious surface does not always exacerbate the urban heat island effect; when the per-capita land area is less than 1.8 m², it can actually mitigate the effect. (5) Guangzhou–Foshan–Zhaoqing and Shenzhen–Dongguan–Huizhou integration reduces heat island intensity. Core cities driving surrounding areas via clustered, interconnected development alleviates this effect. Full article

China’s rapid economic growth is partly due to the productivity gains from agglomeration, whereby firms and workers in larger and denser cities benefit from proximity through knowledge spillovers, thicker labor markets, and shared infrastructure. This study examines the changing nature and location of agglomeration economies in China using resident-based measures of urban scale from the 2000, 2010, and 2020 population censuses. Chinese “cities” are administrative jurisdictions that contain both dense urban districts and lower-density counties, so the agglomeration elasticities are estimated separately for districts and counties for a balanced panel of 298 prefectural jurisdictions. Agglomeration economies occur only in urban districts, while coefficients on urban scale for counties and county-level cities are close to zero or significantly negative. Moreover, district-level elasticities decline over time, from 0.24 in 2000 to 0.15 in 2020, assuming no feedback from productivity to urban scale. Allowing for such feedback, the temporal decline is even greater, from 0.24 in 2000 to 0.08 in 2020. However, urban growth is shifting increasingly toward counties rather than districts, foregoing the potential agglomeration effects. Changes in location of construction workers also shows this dispersed urban growth. Hence, recent urban growth is increasingly in locations without agglomeration benefits. Full article

Under the background of the global “polycrisis”, enhancing ecological resilience has become a key pathway for achieving sustainable urban development, particularly in ecologically fragile regions. This study takes the Loess Plateau as the study area and constructs an evaluation framework for urban ecological resilience based on three dimensions: resistance, adaptability, and recovery. By integrating multi-source data from 1990 to 2020, the spatiotemporal evolution of urban ecological resilience in the region is analyzed, and the Geodetector is employed to identify the impacts of natural, economic, and social factors on urban ecological resilience. The main findings are as follows: (1) The urban ecological resilience index on the Loess Plateau shows an overall trend of initial increase followed by decline. Cities in both the western and eastern parts exhibit similar evolutionary patterns, while cities in the central region demonstrate a continuous upward trend, with the resilience index increasing from 0.3658 in 1990 to 0.3838 in 2020. (2) Urban ecological resilience in the study area exhibits a spatial gradient pattern characterized by a decrease from core cities toward peripheral areas. Resilience levels gradually weaken outward from urban centers. The spatial agglomeration effect is relatively weak, with most regions showing insignificant clustering and lacking contiguous high-resilience clusters. (3) Urban ecological resilience on the Loess Plateau is jointly influenced by multiple factors, with the effects of key drivers displaying clear stage-specific characteristics. Socioeconomic factors—particularly population density, per capita GDP, and industrial structure—serve as the primary driving forces, while natural conditions provide basic constraints. Interactions among factors are dominated by bivariate interactions and nonlinear synergistic effects. These findings deepen the understanding of the evolution and driving mechanisms of urban ecological resilience in ecologically fragile regions and provide scientific support for enhancing urban resilience and promoting sustainable development in the Loess Plateau. Full article

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

Northeast China’s rust-belt cities have faced persistent concerns about stagnating labor productivity amid structural change. This paper examines how the productivity payoff to urban density depends on local economic structure and ownership composition using an annual panel of prefecture-level cities. We estimate two-way fixed-effects models with city and year effects and city-clustered standard errors, complemented by dynamic specifications and additional robustness checks. The results show a robust positive within-city association between population density and labor productivity. This density premium is structure-conditioned: the productivity payoff to density is significantly larger in city-years that are more industry-oriented. Information-theoretic measures further show that sectoral and ownership composition matter in distinct ways. A normalized entropy measure based on 19 all-city sectoral employment categories is positively associated with labor productivity, while its interaction with density is negative and significant, indicating that the density premium is weaker in more sectorally balanced city-years. A normalized four-category ownership entropy measure, constructed from SOE, private/self-employed, collective, and other employment shares, is positively associated with labor productivity and interacts positively with density, indicating a stronger density–productivity association in city-years with a more balanced ownership composition. Collectively, the findings suggest that urban density is not a uniform engine of productivity: its payoff depends on whether dense city economies are organized around productive sectoral linkages and a sufficiently balanced ownership environment. Overall, the evidence supports a conditional agglomeration view in which productivity dynamics in Northeast China reflect the interaction of density, structural orientation, sectoral dispersion, and ownership mixing. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. 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

As China transitions from rapid urbanization to high-quality development, the competition for population among cities has intensified, characterized by a shift from labor-intensive migration to multi-dimensional lifestyle choices. However, traditional migration models often assume global linearity, failing to capture the complex non-linear thresholds and spatial non-stationarity inherent in migration decisions. This study employs a novel Geographically Weighted Random Forest (GWRF) model to analyze net migration flows across 278 Chinese cities using high-granularity mobile signaling data from the 2020 Spring Festival travel rush. The results reveal that GWRF significantly outperforms traditional OLS, GWR, and global Random Forest models, effectively handling spatial heterogeneity and non-linearity. Wage levels are the dominant global driver, exhibiting a distinct “S-curve” non-linear threshold, while population scale shows a significant U-shaped effect, highlighting the transition from agglomeration economies to congestion costs. Migration drivers exhibit profound spatial heterogeneity: western inland cities are “wage-driven,” the Pearl River Delta is “employment-structure driven,” and the northeastern “Rust Belt” is increasingly sensitive to “innovation investment” (technology expenditure). These findings challenge the “one-size-fits-all” approach to population policy, offering precise, spatially targeted strategies for urban planners to mitigate population shrinkage and enhance urban vitality. Full article

With the acceleration of the global energy transition, the photovoltaic industry has become a significant force in the promotion of green development, and photovoltaic raw materials play a crucial role in this process. In this paper, 177 countries during the period of 2001 to 2024 were taken as the research subjects, with a focus on polysilicon and silicon wafers as components of upstream photovoltaic raw materials. Through a combination of the evolutionary analysis of nodes, the overall structure, and the three-dimensional structure with an exponential random graph model, the evolution and dynamic mechanisms of the global photovoltaic raw material trade network are explored. The study reveals the following: (1) The global PV raw material trade volume tended to increase from 2001 to 2024. (2) The global photovoltaic raw material trade network showed a tendency towards the “enhanced dominance of core countries and denser trade connections,” with the trade volume between core countries continuously expanding and the network density, average clustering coefficient, and connection efficiency increasing annually, which is a reflection of the globalization and regional cooperation of the global photovoltaic industry. (3) From the weighted out-degree and in-degree ranking evolution of the global photovoltaic raw materials trade network, it can be seen that China consolidated its core position, while Southeast Asian countries tended to transfer their processing and manufacturing links. The status of the United States and traditional industrial powers gradually declined, which is a reflection of the restructuring of the global industrial chain along with regional geopolitical agglomeration effects. (4) Internal attributes such as the national economic level, population size, and urbanization rate, as well as external network effects such as common language and geographical proximity, significantly influence the formation path of the photovoltaic raw material trade network. Moreover, the network exhibits distinct heterogeneous complementarity mechanisms and path dependence characteristics, with a structural evolution that tends toward stability and cooperative relationships showing significant time inertia. Overall, the global trade volume of photovoltaic raw materials continues to grow, and the core positions of major countries such as China, the United States, and Germany remain prominent but show a transitional trend towards Southeast Asian countries. The strengthening of the level of coordination and cooperation among global photovoltaic raw material producers to ensure supply chain stability, promote resource sharing and technological progress, and achieve the sustainable development of green energy policies is necessary. 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