Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in th...Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.展开更多
The Chinese government’s common prosperity policy has inspired a series of rural development initiatives aimed at reducing income disparities between urban and rural areas.Zhejiang Province was designated as the firs...The Chinese government’s common prosperity policy has inspired a series of rural development initiatives aimed at reducing income disparities between urban and rural areas.Zhejiang Province was designated as the first demonstration zone in China’s pursuit of common prosperity.To explore the demonstration effect of rural development on narrowing the income gap between urban and rural areas,this study adopts the Theil index to measure urban-rural income disparity levels and explores spatial evolution patterns of the income disparity in 52 counties of Zhejiang Province,China from 2011 to 2020.The spatial Durbin model is also used to investigate the impact of rural development on the urban-rural income disparity.The results show that:1)Urban-rural income disparities have gradually narrowed.Moreover,income disparities across the province are spatially clustered from north to south.Narrowing the gap between mountainous and non-mountainous counties is imperative for resolving these spatial imbalances in the short term.2)Rural development reduces urban-rural income disparity levels and generates economic and technological spillover effects,as evidenced by the phenomenon where development in one county leads to reduced income disparities in neighboring counties.3)The technological innovation enhances the co-development of rural industries through technological diffusion effects,further strengthening rural development.Finally,enhancing the ecological environment synergizes with rural development to reduce urban-rural income disparities.The research results provide policy implications for narrowing the urban-rural income disparity and promoting common prosperity.展开更多
Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor...Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.展开更多
Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus pr...Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data.展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
Under the background of‘the Belt and Road’and‘China-Mongolia-Russia Economic Corridor’initiatives,this paper studied the urban accessibility level,regional accessibility pattern and regional spatial effects along ...Under the background of‘the Belt and Road’and‘China-Mongolia-Russia Economic Corridor’initiatives,this paper studied the urban accessibility level,regional accessibility pattern and regional spatial effects along the Primorsky No.1 and No.2 transportation corridors.First,the evaluation of urban accessibility level with and without Primorsky No.1 and No.2 high-speed rails(HSRs)opening was conducted with two indicators,i.e.,the weighted average travel time,and the economic potential.After the evaluation,the spatial differentiation pattern of the accessibility changes with and without Primorsky No.1 and No.2 HSRs opening was performed respectively using ArcGIS.On these bases,the regional spatial effects brought by Primorsky No.1 and No.2 HSRs opening were studied.The results are as following.First,the urban accessibility level will be greatly improved by the opening of Primorsky No.1 and No.2 HSRs.All adjacent cities will be integrated into‘1 h HSR communication circle’and the whole journey will be integrated into‘4 h HSR communication circle’along Primorsky No.1 and No.2 corridors,respectively.The HSR accessibility of Primorsky No.1 corridor is stronger than that of Primorsky No.2 corridor.But the HSR accessibility improvement degree of Primorsky No.1 corridor is weaker than that of Primorsky No.2 corridor.Second,spatially,along Primorsky No.1 and No.2 corridors,the HSR accessibility level of the cities which are located in China is stronger than those cities located in Russia,showing the‘High West,Low East’patterns.The HSR accessibility improvement degree of the cities which are located in Russia and Sino-Russian border is stronger than those cities located in China,showing the‘High East,Low West’patterns.Third,Primorsky No.1 and No.2 corridors could connect the China’s‘Heilongjiang Land Sea Silk Road Economic Belt’and‘Changchun-Jilin-Tumen Development Pilot Zone’respectively,gradually involving into the development of China’s Harbin-Changchun Megalopolis.Relying on Harbin(China)and Changchun(China),Primorsky No.1 and No.2 HSRs could connect Northeast China-Beijing HSR,accelerating the diffusion of population,economy and other flows from China’s Beijing-Tianjin-Hebei Urban Agglomeration to Northeast China,and then to Russia’s Far East Federal District.Relying on Suifenhe(China)and Hunchun(China),Primorsky No.1 and No.2 HSRs could be conducive to the development of the second largest sea channels for Northeast China,creating the Northeast Asian Urban Belt,and new sea-rail intermodal pattern among China,Russia,Democratic People’s Republic of Korea,Japan and Republic of Korea.Relying on Vladivostok(Russia)and Zarubino(Russia),Primorsky No.1 and No.2 corridors could connect the‘Ice Silk Road’,building the‘Sino-Russian Northern Maritime Corridor’and‘Sino-Russian Arctic Blue Economic Areas’.展开更多
Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfall...China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfalls under global change is critical for effective disaster risk management.This study utilizes future typhoon track data simulated by the regional climate model version 4(RegCM4),combined with projected population and Gross Domestic Product(GDP)data for China’s coastal regions under three Shared Socioeconomic Pathways(SSPs:SSP1,sustainability;SSP2,middle of the road;SSP5,fossil-fueled development).We analyze typhoon impact zones for future periods(2030s,2050s,and 2090s)under Representative Concentration Pathways(RCPs:RCP2.6,RCP4.5,and RCP8.5,representing low,medium,and high greenhouse gas emission scenarios,respectively).Exposure levels for 2030,2050,and 2100 are calculated based on the annual average frequency of typhoon impacts during these periods,aiming to quantify the distribution characteristics of typhoon-affected population and GDP in China’s coastal regions.Key findings reveal two high-frequency typhoon im-pact zones:the Taiwan Strait region and the northern Qiongzhou Strait region.Furthermore,under RCP2.6,typhoon impacts may ex-tend to Liaoning,while RCP4.5 and RCP8.5 scenarios indicate potential impacts reaching further north to Heilongjiang.Under RCP4.5,Northeast China will experience the largest typhoon-affected area(43.800×10^(4) km^(2))by the 2030s.High population and GDP exposure concentrates in the Yangtze River Delta,the Pearl River Delta,and the Taiwan Strait coastal areas.Notably,Liaoning’s cumulative ex-posed population may exceed 0.100×10^(8) by 2030 and 2050 under RCP4.5 and RCP8.5 due to typhoon track migration.Given China’s location within the Western Pacific typhoon high-incidence region,the northward expansion of impacts will substantially escalate socio-economic exposure in mid-latitude regions previously at lower risk.These findings underscore the imperative for enhanced disaster pre-vention,mitigation strategies and targeted countermeasure research.展开更多
The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation...The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation and evolution of Meizhou pomelo industry cluster in China,focusing on its role in restructuring rural socio-economic systems and integrating the whole value chains.Based on a case study employing qualitative methods such as in-depth interviews and participatory observation,the agricultural cluster evolution of Meizhou pomelo was categorized into three key phases of initial decentralization,self-organized scaling,and reorganized clustering.Geographical proximity and industrial agglomeration constitute the physical foundation,while vertical/horizontal linkages,technologic-al innovation,and policy support enhance competitiveness.Special mechanisms emerge through localized social networks,farmer co-operatives’activation,and cross-regional market expansion.The cluster’s impact is manifested in the shift from extensive to standard-ized and modernized production,diversified and flexible livelihood of farmers,and the integration of agriculture with industry and ser-vices.The development of the whole value chain based on agricultural cluster represents a critical pathway for achieving agricultural modernization,encompassing both internal and external value chain optimization.Through quality assurance systems,product diversi-fication strategies,operational efficiency improvements,and brand enhancement,these clusters amplify product value propositions and market competitiveness.This systemic approach facilitates supply-demand coordination,enables resource synergies,and optimizes eco-nomic returns across the horizontal and vertical value chain.This paper argues that agricultural clusters serve as strategic catalysts for sustainable rural development by reconstructing local production systems,fostering innovation ecosystems,and aligning agricultural modernization.It contributes to debates on rural vitalization by demonstrating how agricultural clustering can reconfigure rural areas as hubs of ecological modernization,rather than mere urban peripheries.展开更多
Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradien...Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.展开更多
Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis t...Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis to examine the grain size characteristics of major riparian dunes in the typical cold and arid deserts of China.The results indicate that major riparian dunes of deserts in study area can be classified into three types based on their grain size characteristics.The Bartlett test of sphericity and the Kaiser-Meyer-Olkin(KMO)test were also performed,and their significance values were found to be 0.000 and 0.584,respectively.The results of the principal component analysis revealed that the cumulative contribution rate of the total variance reached 85.9%for the two principal components with characteristic roots greater than 1.0.The primary principal component included medium sand,whereas the secondary principal component included fine sand.We conducted a cluster analysis and classified the samples into three major types.Type I rivers include the Keriya River,Langqu River,Tora River and Heihe River,which are characterized by by fine particle size,and well-sorted.Type II includes Mu Bulag River,Kuye River,and the Xar Moron River,Compared with type I,it has a relatively coarser mean grain size and relatively poor sorting for this type.Type III includes the Maquan River,which is characterized mainly by fine sand and medium sand,accounting for more than 90%,and the sorting coefficient(0.52)suggests relatively well sorting in this pattern.Moreover,principal component analysis was applied to determine the particle sizes of samples from different watersheds.Moreover,these sediments exhibit both hydromorphic and aeolian features.At the drainage basin scale,the mode and intensity of aeolian-fluvial interactions depend on climatic conditions.In arid and semi-arid climate regions,wind is the dominant force,and the grain size exhibits significant aeolian features.Conversely,in the semi-humid region,flowing water is the dominant force,and riparian dunes in this region are formed by aeolian-fluvial interaction.The angle between the wind direction and flow direction in different reaches influences both the supply of sediment sources and the development of riparian dunes.This study will provide a new perspective for evaluating aeolian-fluvial interactions on riparian dunes in the deserts of China’s cold and arid regions.展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most inte...Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most intense interaction between urban and rural areas,serving as a key zone for breaking down barriers and promoting urban-rural integration.Based on a systematic review of representative case studies and scholarly literature,this paper synthesizes the evolving research perspectives on the urban-rural fringe,with particular attention to how data-driven approaches that integrate official statistics,remote sensing imagery,points of interest,and mobile phone signaling data have advanced the characterization of fringe features,refined identification methods,and revealed emerging developmental trends through spatial clustering and machine learning classification.It proposes an integrated analytical framework encompassing administrative boundaries,economic metabolism,social activities,material infrastructure,and the ecological environment.The paper further examines the characteristics and emerging development trends of urban-rural fringe areas and advances a set of strategic directions to support urban-rural integration and more efficient resource allocation.These include expanding analytical dimensions,enhancing data integration,refining identification criteria,elucidating mechanisms of internal and external interactions,and strengthening interdisciplinary collaboration.Collectively,these efforts offer actionable insights for optimizing public service delivery,directing infrastructure investment in transportation and utilities,delineating ecological conservation boundaries,and implementing place-based socioeconomic revitalization strategies in the urban-rural fringe regions.展开更多
Understanding the dynamics of vegetation carbon sequestration(VCS)is essential for regional carbon neutrality strategies.This study revealed the spatiotemporal patterns of VCS and its relationship with anthropogenic c...Understanding the dynamics of vegetation carbon sequestration(VCS)is essential for regional carbon neutrality strategies.This study revealed the spatiotemporal patterns of VCS and its relationship with anthropogenic carbon emissions(ACEs)in Shandong Province,China during 2000-2020,and identified the sensitivity factors affecting VCS.The results show that:1)VCS increased consistently from 193.45 million t to 256.41 million t,with high values areas concentrated in the central,northeastern,and southeastern mountainous and hilly regions,while low values were found in water bodies and urban built-up areas.At the city level,Linyi,Yantai,Binzhou,and Jinan experienced the most significant rises-reaching up to 243000 t/yr.At the county level,Pingdu,Qixia,and Yiyuan also showed substantial growth,each exceeding 30400 t/yr.2)Digital Elevation Molde(DEM)was identified as the dominant natural factor influencing VCS distribution,while land use optimization measures,especially afforestation and farmland conversion in sloped terrain,were the primary human drivers of VCS increase.3)Urbanization and carbon neutrality were not mutually exclusive.While urban expansion locally reduced VCS,rural emigration enhanced carbon sinks in surrounding areas,partially offsetting urban losses.This compensatory mechanism supported VCS increases in nearly all cities and 90% of counties.Nevertheless,with ACEs continuing to rise and the offset ratio by VCS declining,achieving carbon neutrality requires regional strategies that integrate with accelerated energy conservation,emission reduction technologies,and energy transition.These findings provide a scientific basis for decomposing carbon neutrality targets across cities and counties in Shandong and a reference for developing localized land use policies in similar regions.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
The Yili River Basin in Northwest China is a crucial ecological security barrier,yet it faces frequent droughts amid global climate change,posing significant threats to food security and ecological stability.However,t...The Yili River Basin in Northwest China is a crucial ecological security barrier,yet it faces frequent droughts amid global climate change,posing significant threats to food security and ecological stability.However,the spatiotemporal variations and driving mechanisms of drought in the basin remain unclear.Based on the monthly Standardized Precipitation Evapotranspiration Index(SPEI),this study employed the Run-Length theory to identify drought events in the basin during 1980-2020,applied statistical and time-series analyses to investigate the spatiotemporal variations,trends,periodicity,and persistence of drought,and explored the underlying drivers associated with meteorological factors and large-scale atmospheric circulation patterns.The results showed that droughts in the Yili River Basin are more severe in spring and summer.Droughts in the central and southwestern regions exhibit greater severity,with shorter durations and stronger intensity.Drought conditions have generally worsened(Sen’s slope=−0.146/10 yr),with notable intensification since 2005,especially in the northwestern region.Spring droughts(Sen’s slope=−0.213/10 yr)and summer droughts(Sen’s slope=−0.169/10 yr)have intensified at most stations,while autumn and winter droughts have eased.In the future,droughts are expected to alleviate overall,but summer droughts may continue to intensify.The Yili River Basin exhibits two primary drought periods of 9 yr and 21 yr,with primary periods in autumn(20 yr)and winter(20 yr)being longer than those in spring(10 yr)and summer(17 yr).Finally,droughts are impacted by both meteorological factors and large-scale atmospheric circulation patterns.Rising temperatures and uneven precipitation have intensified droughts.The SPEI exhibits a co-phase relationship with the North Atlantic Oscillation and an antiphase relationship with the East Central Tropical Pacific Sea Surface Temperature.Therefore,close monitoring and mitigation are necessary for spring and summer droughts,with a focus on the central and southwestern areas in autumn and winter.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi...Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.展开更多
In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged ...In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged the role of governance quality in at-tracting Chinese OFDI,their explanatory capacity regarding human development has not received sufficient attention.This study ad-dresses this gap,investigating the relationship between Chinese OFDI and human development,emphasizing the governance quality’s moderating role.Using data from 122 BRI countries from 2004 to 2021,we employed the Panel Corrected Standard Error model through Prais-Winsten regression,complimented by robustness check via the Spatial Durbin Model.Our analysis revealed a significant positive relationship between Chinese OFDI and human development in BRI countries,indicating that Chinese investments act as cata-lysts for improved well-being.More importantly,governance quality plays a vital role in shaping this relationship,revealing that effect-ive governance enhanced the human development outcomes of OFDI.Post-hoc analysis of governance indicators,both as a composite index and individual dimensions,supported this finding.Regional heterogeneity analysis further showed that Chinese OFDI has stronger effects on Human Development Index(HDI)in Asia and Africa compared to Europe,Latin America,and Oceania.However,the moder-ating effect of governance quality seemed more prominent in Oceania and Latin America,indicating its role in fostering the benefits of Chinese investments in these regions.These findings hold implications for academia and policy,emphasizing the need for transparent,accountable,and effective governance to drive sustainable and inclusive human development within the BRI framework.展开更多
基金Under the auspices of the Hong Kong Research Grants Council(No.14605920,14606922,14603724,C4023-20GF,8601219,8601242,3110151)a Grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes of the Chinese University of Hong Kong(CUHK,No.3133235)the Vice-Chancellor’s One-off Discretionary Fund of CUHK(No.4930787)。
文摘Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.
基金Under the auspices of Project of Humanities and Social Sciences Research of the Ministry of Education(No.23YJC790127)Major projects of the National Social Science Fund(No.18ZDA045)。
文摘The Chinese government’s common prosperity policy has inspired a series of rural development initiatives aimed at reducing income disparities between urban and rural areas.Zhejiang Province was designated as the first demonstration zone in China’s pursuit of common prosperity.To explore the demonstration effect of rural development on narrowing the income gap between urban and rural areas,this study adopts the Theil index to measure urban-rural income disparity levels and explores spatial evolution patterns of the income disparity in 52 counties of Zhejiang Province,China from 2011 to 2020.The spatial Durbin model is also used to investigate the impact of rural development on the urban-rural income disparity.The results show that:1)Urban-rural income disparities have gradually narrowed.Moreover,income disparities across the province are spatially clustered from north to south.Narrowing the gap between mountainous and non-mountainous counties is imperative for resolving these spatial imbalances in the short term.2)Rural development reduces urban-rural income disparity levels and generates economic and technological spillover effects,as evidenced by the phenomenon where development in one county leads to reduced income disparities in neighboring counties.3)The technological innovation enhances the co-development of rural industries through technological diffusion effects,further strengthening rural development.Finally,enhancing the ecological environment synergizes with rural development to reduce urban-rural income disparities.The research results provide policy implications for narrowing the urban-rural income disparity and promoting common prosperity.
基金Under the auspices of the National Natural Science Foundation of China(No.42371222,41971167)Fundamental Scientific Research Funds of Central China Normal University(No.CCNU24ZZ120)。
文摘Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.
基金Under the auspices of National Key R&D Program of China(No.2024YFF1306405)。
文摘Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data.
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
基金Under the auspices of Heilongjiang Provincial Natural Science Foundation of China(No.YQ2024D012),National Natural Science Foundation of China(No.42071162,42101165,42501220)。
文摘Under the background of‘the Belt and Road’and‘China-Mongolia-Russia Economic Corridor’initiatives,this paper studied the urban accessibility level,regional accessibility pattern and regional spatial effects along the Primorsky No.1 and No.2 transportation corridors.First,the evaluation of urban accessibility level with and without Primorsky No.1 and No.2 high-speed rails(HSRs)opening was conducted with two indicators,i.e.,the weighted average travel time,and the economic potential.After the evaluation,the spatial differentiation pattern of the accessibility changes with and without Primorsky No.1 and No.2 HSRs opening was performed respectively using ArcGIS.On these bases,the regional spatial effects brought by Primorsky No.1 and No.2 HSRs opening were studied.The results are as following.First,the urban accessibility level will be greatly improved by the opening of Primorsky No.1 and No.2 HSRs.All adjacent cities will be integrated into‘1 h HSR communication circle’and the whole journey will be integrated into‘4 h HSR communication circle’along Primorsky No.1 and No.2 corridors,respectively.The HSR accessibility of Primorsky No.1 corridor is stronger than that of Primorsky No.2 corridor.But the HSR accessibility improvement degree of Primorsky No.1 corridor is weaker than that of Primorsky No.2 corridor.Second,spatially,along Primorsky No.1 and No.2 corridors,the HSR accessibility level of the cities which are located in China is stronger than those cities located in Russia,showing the‘High West,Low East’patterns.The HSR accessibility improvement degree of the cities which are located in Russia and Sino-Russian border is stronger than those cities located in China,showing the‘High East,Low West’patterns.Third,Primorsky No.1 and No.2 corridors could connect the China’s‘Heilongjiang Land Sea Silk Road Economic Belt’and‘Changchun-Jilin-Tumen Development Pilot Zone’respectively,gradually involving into the development of China’s Harbin-Changchun Megalopolis.Relying on Harbin(China)and Changchun(China),Primorsky No.1 and No.2 HSRs could connect Northeast China-Beijing HSR,accelerating the diffusion of population,economy and other flows from China’s Beijing-Tianjin-Hebei Urban Agglomeration to Northeast China,and then to Russia’s Far East Federal District.Relying on Suifenhe(China)and Hunchun(China),Primorsky No.1 and No.2 HSRs could be conducive to the development of the second largest sea channels for Northeast China,creating the Northeast Asian Urban Belt,and new sea-rail intermodal pattern among China,Russia,Democratic People’s Republic of Korea,Japan and Republic of Korea.Relying on Vladivostok(Russia)and Zarubino(Russia),Primorsky No.1 and No.2 corridors could connect the‘Ice Silk Road’,building the‘Sino-Russian Northern Maritime Corridor’and‘Sino-Russian Arctic Blue Economic Areas’.
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
基金Under the auspices of National Key Research and Development Program of China(No.2017YFA0604902,2024YFF1306802)Natural Science Foundation of Fujian Province(No.2022J01497)Open Project of the Strait Meteorology Laboratory(No.2025KF03)。
文摘China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfalls under global change is critical for effective disaster risk management.This study utilizes future typhoon track data simulated by the regional climate model version 4(RegCM4),combined with projected population and Gross Domestic Product(GDP)data for China’s coastal regions under three Shared Socioeconomic Pathways(SSPs:SSP1,sustainability;SSP2,middle of the road;SSP5,fossil-fueled development).We analyze typhoon impact zones for future periods(2030s,2050s,and 2090s)under Representative Concentration Pathways(RCPs:RCP2.6,RCP4.5,and RCP8.5,representing low,medium,and high greenhouse gas emission scenarios,respectively).Exposure levels for 2030,2050,and 2100 are calculated based on the annual average frequency of typhoon impacts during these periods,aiming to quantify the distribution characteristics of typhoon-affected population and GDP in China’s coastal regions.Key findings reveal two high-frequency typhoon im-pact zones:the Taiwan Strait region and the northern Qiongzhou Strait region.Furthermore,under RCP2.6,typhoon impacts may ex-tend to Liaoning,while RCP4.5 and RCP8.5 scenarios indicate potential impacts reaching further north to Heilongjiang.Under RCP4.5,Northeast China will experience the largest typhoon-affected area(43.800×10^(4) km^(2))by the 2030s.High population and GDP exposure concentrates in the Yangtze River Delta,the Pearl River Delta,and the Taiwan Strait coastal areas.Notably,Liaoning’s cumulative ex-posed population may exceed 0.100×10^(8) by 2030 and 2050 under RCP4.5 and RCP8.5 due to typhoon track migration.Given China’s location within the Western Pacific typhoon high-incidence region,the northward expansion of impacts will substantially escalate socio-economic exposure in mid-latitude regions previously at lower risk.These findings underscore the imperative for enhanced disaster pre-vention,mitigation strategies and targeted countermeasure research.
基金Under the auspices of the Key Projects of Philosophy and Social Sciences Research,Ministry of Education of China(No.23JZD008)National Natural Science Foundation of China(No.42171193)+2 种基金Key Project of Guangdong Provincial Philosophy and Social Sciences Planning(No.GD24ES013,GD25ZX04)2025 Guangzhou Basic and Applied Basic Research Special Project(No.2025A04J7127)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.24wkjc11)。
文摘The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation and evolution of Meizhou pomelo industry cluster in China,focusing on its role in restructuring rural socio-economic systems and integrating the whole value chains.Based on a case study employing qualitative methods such as in-depth interviews and participatory observation,the agricultural cluster evolution of Meizhou pomelo was categorized into three key phases of initial decentralization,self-organized scaling,and reorganized clustering.Geographical proximity and industrial agglomeration constitute the physical foundation,while vertical/horizontal linkages,technologic-al innovation,and policy support enhance competitiveness.Special mechanisms emerge through localized social networks,farmer co-operatives’activation,and cross-regional market expansion.The cluster’s impact is manifested in the shift from extensive to standard-ized and modernized production,diversified and flexible livelihood of farmers,and the integration of agriculture with industry and ser-vices.The development of the whole value chain based on agricultural cluster represents a critical pathway for achieving agricultural modernization,encompassing both internal and external value chain optimization.Through quality assurance systems,product diversi-fication strategies,operational efficiency improvements,and brand enhancement,these clusters amplify product value propositions and market competitiveness.This systemic approach facilitates supply-demand coordination,enables resource synergies,and optimizes eco-nomic returns across the horizontal and vertical value chain.This paper argues that agricultural clusters serve as strategic catalysts for sustainable rural development by reconstructing local production systems,fostering innovation ecosystems,and aligning agricultural modernization.It contributes to debates on rural vitalization by demonstrating how agricultural clustering can reconfigure rural areas as hubs of ecological modernization,rather than mere urban peripheries.
基金Under the auspices of the National Natural Science Foundation of China(No.42430511,U20A2083)the National Key Research and Development Program of China(No.2022YFF1300900)the Science and Technology Development Program of Jilin Province(No.20210509037RQ,20230101348JC)。
文摘Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.
基金Under the auspices of the General Project of Science and Technology Department of Shaanxi Province(No.2023-JCYB-264)General Program of National Natural Science Foundation of China(No.41801004,42371008,42471012)。
文摘Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis to examine the grain size characteristics of major riparian dunes in the typical cold and arid deserts of China.The results indicate that major riparian dunes of deserts in study area can be classified into three types based on their grain size characteristics.The Bartlett test of sphericity and the Kaiser-Meyer-Olkin(KMO)test were also performed,and their significance values were found to be 0.000 and 0.584,respectively.The results of the principal component analysis revealed that the cumulative contribution rate of the total variance reached 85.9%for the two principal components with characteristic roots greater than 1.0.The primary principal component included medium sand,whereas the secondary principal component included fine sand.We conducted a cluster analysis and classified the samples into three major types.Type I rivers include the Keriya River,Langqu River,Tora River and Heihe River,which are characterized by by fine particle size,and well-sorted.Type II includes Mu Bulag River,Kuye River,and the Xar Moron River,Compared with type I,it has a relatively coarser mean grain size and relatively poor sorting for this type.Type III includes the Maquan River,which is characterized mainly by fine sand and medium sand,accounting for more than 90%,and the sorting coefficient(0.52)suggests relatively well sorting in this pattern.Moreover,principal component analysis was applied to determine the particle sizes of samples from different watersheds.Moreover,these sediments exhibit both hydromorphic and aeolian features.At the drainage basin scale,the mode and intensity of aeolian-fluvial interactions depend on climatic conditions.In arid and semi-arid climate regions,wind is the dominant force,and the grain size exhibits significant aeolian features.Conversely,in the semi-humid region,flowing water is the dominant force,and riparian dunes in this region are formed by aeolian-fluvial interaction.The angle between the wind direction and flow direction in different reaches influences both the supply of sediment sources and the development of riparian dunes.This study will provide a new perspective for evaluating aeolian-fluvial interactions on riparian dunes in the deserts of China’s cold and arid regions.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
基金Under the auspices of the Funding Project of Northeast Geological S&T Innovation Center of China Geological Survey(No.QCJJ2024-11)Natural Science Foundation of Liaoning Province(No.2025-BS-0873)+1 种基金Liaoning Provincial Joint Science and Technology Program(No.2024-MSLH-507)National Social Science Foundation of China(No.23ATJ006)。
文摘Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most intense interaction between urban and rural areas,serving as a key zone for breaking down barriers and promoting urban-rural integration.Based on a systematic review of representative case studies and scholarly literature,this paper synthesizes the evolving research perspectives on the urban-rural fringe,with particular attention to how data-driven approaches that integrate official statistics,remote sensing imagery,points of interest,and mobile phone signaling data have advanced the characterization of fringe features,refined identification methods,and revealed emerging developmental trends through spatial clustering and machine learning classification.It proposes an integrated analytical framework encompassing administrative boundaries,economic metabolism,social activities,material infrastructure,and the ecological environment.The paper further examines the characteristics and emerging development trends of urban-rural fringe areas and advances a set of strategic directions to support urban-rural integration and more efficient resource allocation.These include expanding analytical dimensions,enhancing data integration,refining identification criteria,elucidating mechanisms of internal and external interactions,and strengthening interdisciplinary collaboration.Collectively,these efforts offer actionable insights for optimizing public service delivery,directing infrastructure investment in transportation and utilities,delineating ecological conservation boundaries,and implementing place-based socioeconomic revitalization strategies in the urban-rural fringe regions.
基金Under the auspices of the National Natural Science Foundation of China(No.42476247,42461015)the Open Research Fund of Key Laboratory of Coastal Science and Integrated Management,Ministry of Natural Resources(No.2024COSIM01)Guangxi Science and Technology Base and Talent Special Project(No.GuikeAD23026194)。
文摘Understanding the dynamics of vegetation carbon sequestration(VCS)is essential for regional carbon neutrality strategies.This study revealed the spatiotemporal patterns of VCS and its relationship with anthropogenic carbon emissions(ACEs)in Shandong Province,China during 2000-2020,and identified the sensitivity factors affecting VCS.The results show that:1)VCS increased consistently from 193.45 million t to 256.41 million t,with high values areas concentrated in the central,northeastern,and southeastern mountainous and hilly regions,while low values were found in water bodies and urban built-up areas.At the city level,Linyi,Yantai,Binzhou,and Jinan experienced the most significant rises-reaching up to 243000 t/yr.At the county level,Pingdu,Qixia,and Yiyuan also showed substantial growth,each exceeding 30400 t/yr.2)Digital Elevation Molde(DEM)was identified as the dominant natural factor influencing VCS distribution,while land use optimization measures,especially afforestation and farmland conversion in sloped terrain,were the primary human drivers of VCS increase.3)Urbanization and carbon neutrality were not mutually exclusive.While urban expansion locally reduced VCS,rural emigration enhanced carbon sinks in surrounding areas,partially offsetting urban losses.This compensatory mechanism supported VCS increases in nearly all cities and 90% of counties.Nevertheless,with ACEs continuing to rise and the offset ratio by VCS declining,achieving carbon neutrality requires regional strategies that integrate with accelerated energy conservation,emission reduction technologies,and energy transition.These findings provide a scientific basis for decomposing carbon neutrality targets across cities and counties in Shandong and a reference for developing localized land use policies in similar regions.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Under the auspices of the Third Xinjiang Scientific Expedition Program(No.2022xjkk0600)。
文摘The Yili River Basin in Northwest China is a crucial ecological security barrier,yet it faces frequent droughts amid global climate change,posing significant threats to food security and ecological stability.However,the spatiotemporal variations and driving mechanisms of drought in the basin remain unclear.Based on the monthly Standardized Precipitation Evapotranspiration Index(SPEI),this study employed the Run-Length theory to identify drought events in the basin during 1980-2020,applied statistical and time-series analyses to investigate the spatiotemporal variations,trends,periodicity,and persistence of drought,and explored the underlying drivers associated with meteorological factors and large-scale atmospheric circulation patterns.The results showed that droughts in the Yili River Basin are more severe in spring and summer.Droughts in the central and southwestern regions exhibit greater severity,with shorter durations and stronger intensity.Drought conditions have generally worsened(Sen’s slope=−0.146/10 yr),with notable intensification since 2005,especially in the northwestern region.Spring droughts(Sen’s slope=−0.213/10 yr)and summer droughts(Sen’s slope=−0.169/10 yr)have intensified at most stations,while autumn and winter droughts have eased.In the future,droughts are expected to alleviate overall,but summer droughts may continue to intensify.The Yili River Basin exhibits two primary drought periods of 9 yr and 21 yr,with primary periods in autumn(20 yr)and winter(20 yr)being longer than those in spring(10 yr)and summer(17 yr).Finally,droughts are impacted by both meteorological factors and large-scale atmospheric circulation patterns.Rising temperatures and uneven precipitation have intensified droughts.The SPEI exhibits a co-phase relationship with the North Atlantic Oscillation and an antiphase relationship with the East Central Tropical Pacific Sea Surface Temperature.Therefore,close monitoring and mitigation are necessary for spring and summer droughts,with a focus on the central and southwestern areas in autumn and winter.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金Under the auspices of the National Key Research and Development Program of China(No.2024YFC3713102)。
文摘Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.
基金Under the auspices of Fundamental Research Funds for the Central Universities,Zhongnan University of Economics and Law,China(No.2722021EG001)。
文摘In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged the role of governance quality in at-tracting Chinese OFDI,their explanatory capacity regarding human development has not received sufficient attention.This study ad-dresses this gap,investigating the relationship between Chinese OFDI and human development,emphasizing the governance quality’s moderating role.Using data from 122 BRI countries from 2004 to 2021,we employed the Panel Corrected Standard Error model through Prais-Winsten regression,complimented by robustness check via the Spatial Durbin Model.Our analysis revealed a significant positive relationship between Chinese OFDI and human development in BRI countries,indicating that Chinese investments act as cata-lysts for improved well-being.More importantly,governance quality plays a vital role in shaping this relationship,revealing that effect-ive governance enhanced the human development outcomes of OFDI.Post-hoc analysis of governance indicators,both as a composite index and individual dimensions,supported this finding.Regional heterogeneity analysis further showed that Chinese OFDI has stronger effects on Human Development Index(HDI)in Asia and Africa compared to Europe,Latin America,and Oceania.However,the moder-ating effect of governance quality seemed more prominent in Oceania and Latin America,indicating its role in fostering the benefits of Chinese investments in these regions.These findings hold implications for academia and policy,emphasizing the need for transparent,accountable,and effective governance to drive sustainable and inclusive human development within the BRI framework.
