How Unique is ′China Model′: A Review of Theoretical Perspectives on China′s Urbanization in Anglophone Literature

As a major aspect of the ′China Model′, urbanization in China is being heatedly discussed in the Anglophone literature in the last two decades. By considering China′s urbanization as a ′testing ground′ of ′western′ theories, these studies are important for the understanding of China′s ongoing urban restructuring and exchanging knowledge with ′western′ insights. Based on a survey of existing studies, this paper identifies various endogenous and exogenous concepts and explanations of China′s urbanization. It is suggested that China′s urbanization is mainly driven by a combination of political decentralization and neoliberalization with various forms of social stratification and mobility. However, it is argued that the distinctness of the ′China Model′, which is usually used to support the legitimacy of studies of China′s urbanization, has led to many controversial arguments and diminished the due contributions of Chinese scholars in the knowledge production of China′s urbanization. To consolidate the legitimacy of studies on China′s urbanization and enhance the roles of Chinese scholars, we suggest future studies to pay more attentions to the fundamental theories with looser constraints and ′true questions′ on China′s urbanization.

3-D observations of a red tide event in the offshore water along the western Guangdong coast

From November 24 to 26, 2014, a red tide event occurred in the offshore water off the Hailing Island located at the western Guangdong coast. The red tide appeared as pink strips distributed within 3 km in the offshore water and extended for about 10 km along the shoreline. During the flood tide, the pink seawater rushed to the beach with breaking waves, forming foam strips on the beach. Guangdong Province Key Laboratory for Coastal Ocean Variation and Disaster Prediction Technologies, Guangdong Ocean University, emergently responded to the event and organized three-dimensional observations from the air, onboard and on beach. The preliminary analyses of the cruise data and water samples indicate that the event was induced by non- toxic Noctiluca scintillans, of which the concentration reaches as high as 4 200 cells/L near the surface and 2600 cells/L at the bottom.

Spatial Analysis of Commuting Mode Choice in Guangzhou, China

Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distribution on road networks has enabled us to examine the factors that give rise to the discrepancies and the fundamental spatial causes of traffic congestion. In recent years, mi- cro-perspective, individual, and behavior-based spatial analysis have mushroomed and been facilitated with effective tools such as tem- poral geographic information systems （T-GIS）. It is difficult to study the interrelations between transport and space on the basis of commuting mode choice since the mode choice data are invisible in a specific space such as a particular road network. Therefore, in the field of transport, the classical origin destination （OD） four-stage model （FSM） is usually employed to calculate data when studying commuting mode choice. Based on the relative principles of T-GIS and the platform of ArcGIS, this paper considers Guangzhou as a case study and develops a spatio-temporal tool to examine the daily activities of residents. Meanwhile, the traffic volume distribution in rush hours, which was analyzed according to commuting modes and how they were reflected in the road network, was scrutinized with data extracted from travel diaries. Moreover, efforts were made to explain the relationship between traffic demand and urban spatial structure. Based on the investigation, this research indicates that traffic volumes in divergent groups and on the road networks is driven by： l） the socio-economie characteristics of travelers; 2） a jobs-housing imbalance under suburbanization; 3） differences in the spatial supply of transport modes; 4） the remains of the Danwei （work unit） system and market development in China; and 5） the transition of urban spatial structure and other factors.

Temporal and Spatial Variability of Dissolved Organic Carbon Concentration in the Xijiang River, South China

A whole year analysis of riverine dissolved organic carbon (DOC) concentrations in the Xijiang River (XJR), South China, showed that the mean riverine DOC concentration (1.24 mg L-1) in the XJR was notably lower than the averaged value (5.75 mg L-1) of the global riverine DOC concentration in several major rivers. There is an inconspicuous monthly fluctuation of the DOC signal in the XJR, but on a semi-yearly time scale, however, the riverine DOC concentration had significant difference between hydrological seasons. The DOC level during the flood season (1.18 mg L-1) was less than that during the non-flood season (1.40 mg L-1). Owing to the concomitance of the flushing and dilution effects of the runoff during the high-water period, the variation of riverine DOC concentration with discharge in the XJR differed from that reported in many other major rivers. The DOC export flux above the city of Wuzhou was about 0.62× 106 g C km-2 yr-1. The DOC transported during the "056" Massive Flood period comprised 30.35% of the annual total, while the discharge accounted for 36.32% of the total annual flow. The characteristics in riverine DOC concentration in the XJR were attributed to the combined effect of the geomorphologic, monsoon climatic and hydrological processes as well as land-use within the drainage basin.

The Environmental Impacts of Informal Economies in China: Inverted U-shaped Relationship and Regional Variances

This paper aims to the debate on the nexus between informal economies and the environment by investigating the long-term dynamic impacts of China’s informal economies on pollution and considering regional differences in informal economies’ pollution.This paper uses the Multiple Indicators Multiple Causes(MIMIC) model to estimate the size of informal economies and employs econometric models to examine their relationships to pollution based on provincial-level panel data from 2000 to 2017. The results indicate that informal economies’ effects on environmental pollution are not purely positive or negative. Rather, our model indicates that there is an inverted U-shaped relationship between informal economies and pollution in the long run in China;this means that the level of environmental pollution increases at first and then decreases with the growth of informal economies. Further analysis shows that while this inverted, U-shaped relationship is significant in different regions of China, it is affected by different environmental impact factors. The paper concludes by discussing the policy implications for environmental protection and sustainable development.

A Review of Assessment and Adaptation Strategy to Climate Change Impacts on the Coastal Areas in South China

This paper reviews assessment of climate change impacts on economy, society and ecological environment in the coastal areas of South China based on published literatures; it also proposes suitable adaptation strategies and counter- measures. Review shows that climate change has resulted in sea level rise in the coastal areas of South China, increasing the occurrence and intensity of storm surges, aggravating the influence of saltwater intrusion, coastal erosion, urban drainage and flood control, threatening the coastal facility and infrastructures, inundating lowland areas, offsetting mudflat silting, and degrading mangroves and corm reef ecosystem. Therefore, in order to reduce the adverse effects of climate change and to support the sustainable development in the coastal areas of South China, it is critical to improve the monitoring and early warning system, enhance prevention criteria, fortify coastal protection engineering, strengthen saltwater prevention, and reinforce the ecological restoration and protection.

Geography of Talent in China During 2000-2015:An Eigenvector Spatial Filtering Negative Binomial Approach

The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service allocation.Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China.Yet,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations.Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China.Using four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time.Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015.It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent.An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution.Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of talent.These results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.

Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging(Li DAR) provides a new way to characterize three-dimensional(3 D) plant structure, there is a need to develop robust algorithms for extracting 3 D phenotypic traits from Li DAR data to assist in gene identification and selection. Accurate 3 D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial Li DAR data. We describe a two-stage method that combines both convolutional neural networks(CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the Point CNN model and obtains stem instances by fitting 3 D cylinders to the points. It then segments the field Li DAR point cloud into individual plants using local point densities and 3 D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs(F-score =0.8207) and plants(Fscore =0.9909). The effectiveness of terrestrial Li DAR for phenotyping at organ(including leaf area and stem position) and individual plant(including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position(position error =0.0141 m), plant height(R^(2)>0.99), crown width(R^(2)>0.90), and leaf area(R^(2)>0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

Impacts of Climate Change on Human Health and Adaptation Strategies in South China

This study reviews the impacts of climate change on human health and presents corresponding adaptation strategies in South China. The daily mean surface air temperatures above or below 26.4~C increase the death risk for people in Guangzhou, especially the elderly are vulnerable to variations in temperature. Heat waves can cause insomnia, fatigue, clinical exacerbation, or death from heatstroke etc., while cold spells show increases in patients with fractures. During a cold spell period, the rates of both on-site emergency rescues and non-implementable rescues increase, and the risk of non-accidental deaths and respiratory disease deaths significantly rise as well. Both time series of hazy days and ozone concentrations have significant positive correlations with the number of patients with cardiovascular diseases. Both malaria and dengue fever reach higher altitudes and mountainous areas due to climate warming. Climate change is likely to bring stronger heat waves in the future, thereby increasing heat wave-related illnesses and deaths, particularly in the metropolitan areas of the Pearl River Delta. The projected increase of consecutive cold days in Guangdong province and parts of northern Guangxi province will affect residents＇ health in the future. The rising temperature exaggerates ozone pollution, but it is not clear whether climate change is aggravating or mitigating haze pollution. The transmission potential of malaria in South China will increase by 39%-140% and the transmission season will extend by 1 2 months with an air temperature increase of 1-2~C. By 2050, most areas in Hainan province are projected to convert from non-endemic dengue into endemic dengue areas. The aging population will cause more vulnerable people. To mitigate the adverse impacts of climate change on human health, sound and scientific adaptation strategies must be adopted in advance, such as strengthening the surveillance of epidemic diseases in potential transmission areas, conducting timely weather forecasting for human health, evaluating health vulnerability to climate change, improving environmental and health education, and strengthening hazard management and the cooperation between meteorological and health departments.

Changes in Climate Factors and Extreme Climate Events in South China during 1961-2010

Daily climate data at 110 stations during 1961-2010 were selected to examine the changing characteristics of climate factors and extreme climate events in South China. The annual mean surface air temperature has increased significantly by 0.16℃ per decade, most notably in the Pearl River Delta and in winter. The increase rate of the annual extreme minimum temperature (0.48℃ per decade) is over twice that of the annual extreme maximum temperature (0.20℃ per decade), and the increase of the mean temperature is mainly the result of the increase of the extreme minimum temperature. The increase rate of high-temperature days (1.1 d per decade) is close to the decrease rate of low-temperature days (-1.3 d per decade). The rainfall has not shown any significant trend, but the number of rainy days has decreased and the rain intensity has increased. The regional mean sunshine duration has a significant decreasing trend of -40.9 h per decade, and the number of hazy days has a significant increasing trend of 6.3 d per decade. The decrease of sunshine duration is mainly caused by the increase of total cloud, not by the increase of hazy days in South China. Both the regional mean pan evaporation and mean wind speed have significant decreasing trends of -65.9 mm per decade and -0.11 m s-1 per decade, respectively. The decrease of both sunshine duration and mean wind speed plays an important role in the decrease of pan evaporation. The number of landing tropical cyclones has an insignificant decreasing trend of -0.6 per decade, but their intensities show a weak increasing trend. The formation location of tropical cyclones landing in South China has converged towards 10-19°N, and the landing position has shown a northward trend. The date of the first landfall tropical cyclone postpones 1.8 d per decade, and the date of the last landfall advances 3.6 d per decade, resulting in reduction of the typhoon season by 5.4 d per decade.

Regional Evaluations of the Meteorological Drought Characteristics across the Pearl River Basin, China

Regional evaluation of drought characteristics provides critical information for water resource management. In this case, this study attempts to analyze the probability behaviors of drought events of a given severity in the Pearl River Basin and to construct severity-area-frequency curves of drought events. Due to possible impacts of complicated topographical properties and diverse climate types within the Pearl River Basin, the entire Pearl River Basin is subdivided into different homogeneous regions. In general, the Pearl River Basin can be categorized into four homogeneous regions, and the severity-area-frequency analysis results for the short-, medium- and long-term drought episodes within each homogeneous regions indicate stressful challenge for the water resource management in the Pearl River Basin due to the fact that severe droughts usually occur over the entire Pearl River Basin. Meanwhile, the Pearl River Delta will face a higher risk of drought when compared to other regions of the Pearl River Basin in terms of the medium-term drought. The Pearl River Basin is highly economically development and is heavily populated, thus impacts of droughts and related resilience resolutions or policies should be taken into account in the formulation of regional sustainable development of water resources and socio-economy within the Pearl River Basin, China.

Recent progress of coastal aeolian research in China

Recent progress of coastal aeolian research in China is reviewed and summarized. Topics considered include coastal dune type, distribution, formation and evolution, characteristics of modem and ancient sediments, and observations of sand transport of coastal aeolian dunes in China. Following international trends in aeolian research, research directions for the near future in China are suggested which focus on observation and modeling of coastal aeolian transport processes, the relationship between coastal aeolian processes and global change, a surfzone-beach-dune interaction model, and the problem of coastal aeolian damages in China.

Change of probability density distributions of summer temperatures in different climate zones

Extreme events have become increasingly frequent worldwide which are reflected in diverse changes in the shape of the temperature probability density function.However,few studies have paid attention to the heterogeneity of temperature at the scale of climate zones.Here,we use the ERA5-land data set to explore interdecadal summer temperature changes and the distribution across different climate zones from 1981 to 2019.Comparing the minimum(Tmin)and maximum(Tmax)temperature of 1982–1991 and 2010–2019,the results imply that Tmin and Tmax in summer maintained a notable upward trend over the past 40 years,especially Tmin.The effects of a simple shift toward a warmer climate contributed most to all climate zones,while the standard deviation,skewness and kurtosis had minor effects on extreme temperature except for tropics.Quantile analysis shows that the probability of extreme events in all climate zones is increasing in frequency and intensity,especially Tmin and Tmax in temperate climate zone.Understanding diverse reasons for climate change can assist us with taking different measures to address extreme climate in distinct climate zones.

Simulation of urban expansion based on cellular automata and maximum entropy model

Urban expansion is a hot topic in land use/land cover change(LUCC)researches.In this paper,maximum entropy model and cellular automata(CA)model are coupled into a new CA model(Maxent-CA)for urban expansion.This model can help to obtain transition rules from single-period dataset.Moreover,it can be constructed and calibrated easily with several steps.Firstly,Maxent-CA model was built by using remote sensing data of China in 2000(basic data)and spatial variables(such as population density and Euclidean distance to cities).Secondly,the proposed model was calibrated by analyzing training samples,neighborhood structure and spatial scale.Finally,this model was verified by comparing logistic regression CA model and their simulation results.Experiments showed that suitable sampling ratio(sampling ratio equals the proportion of urban land in the whole region)and von Neumann neighborhood structure will help to yield better results.Spatial structure of simulation results becomes simple as spatial resolution decreases.Besides,simulation accuracy is significantly affected by spatial resolution.Compared to simulation results of logistic regression CA model,Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation.It is found that the proposed model performs well in simulating urban expansion of China.It will be helpful for simulating even larger study area in the background of global environment changes.

30m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019

Using more than three million Landsat satellite images,this research developed the first global impervious surface area(GISA)dataset from 1972 to 2019.Based on 120,777 independent and random reference sites from 270 cities all over the world,the omission error,commission error,and F-score of GISA are 5.16%,0.82%,and 0.954,respectively.Compared to the existing global datasets,the merits of GISA include:(1)It provided the global ISA maps before the year of 1985,and showed the longest time span(1972–2019)and the highest accuracy(in terms of a large number of randomly selected and third-party validation sample sets);(2)it presented a new global ISA mapping method including a semi-automatic global sample collection,a locally adaptive classification strategy,and a spatio-temporal post-processing procedure;and(3)it extracted ISA from the whole global land area(not from an urban mask)and hence reduced the underestimation.Moreover,on the basis of GISA,the long time series global urban expansion pattern(GUEP)has been calculated for the first time,and the pattern of continents and representative countries were analyzed.The two new datasets(GISA and GUEP)produced in this study can contribute to further understanding on the human’s utilization and reformation to nature during the past half century,and can be freely download from http://irsip.whu.edu.cn/resources/dataweb.php.

Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

Urban spatial structure is an important feature for assessing the effects of urban planning.Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments.Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures.However,these methods cannot efficiently reflect the influence of human activities.With the wide application of big data,analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning.In this study,we constructed a human-activity space network using the taxi trip big data.Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning.This method was applied to a case study based on one-month taxi trip data of Dongguan City.Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020,which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan.We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure.The analysis demonstrated that the taxi trip data are important big data on social spatial perception,and taxi data should be used for evaluating spatial structures in future urban planning.

Assessing the degree of impact from iceberg activities on penguin colonies of Clarence Island

During August and September 2023,three giant icebergs,each bigger than Paris,successively grazed Clarence Island in the northeast of the Antarctic Peninsula,a home to a population of over 100000 penguins.This incident may serve as a clarion call for the increasing iceberg calving due to global warming and its subsequent impact on the Antarctic ecosystem.Here we investigate this unexpected event and employ historical records and probabilistic analyses of iceberg grounding to assess the degree of impact on penguin colonies of Clarence Island.Among the eleven colonies,there is one with low impact,eight with medium impact,and two with high impact.The low-impact colony,Cape Lloyd,is located in the northern part of the island,while the high-impact colonies,False Ridge and Pink Pool,are in the southeast.The eight medium-impact colonies are distributed along both the eastern and western coasts of the island.This study provides essential support for evaluating the impact of iceberg activity on penguin colonies.We argue that penguin colonies located in areas prone to iceberg drift,such as Clarence Island,may become more vulnerable to the heightened risk of iceberg collisions or groundings in the warming future.Therefore,we hope the public will become more aware of the grave impacts of climate change on penguins and underscore the urgent need for effective conservation strategies.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata:a case study on city of Toronto

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes.Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy,leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types.To overcome these limita-tions,an Accessibility-interacted Vector-based Cellular Automata(A-VCA)model was proposed for the better simulation of realistic land use change among different urban functional types.The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process.The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto,Canada,during 2012-2016.A vector-based CA without considering the driving factor of accessibility(VCA)and a popular grid-based CA model(Future Land Use Simulation,FLUS)were also implemented for compar-isons.The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature(kappa=0.907,figure of merit=0.283,and cumulative producer’s accuracy=72.83%±1.535%).The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models,suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy.The proposed model provides new tools for a better simula-tion of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

Citizenization of Native Villagers after Redeveloped Urban Village:A Case Study of Liede Community in Guangzhou

Based on qualitative data from in-depth interviews,participative observation,and literature study,this paper aims to get insights into the patterns and process of the citizenization of native villagers living in Liede Community,which is the first redeveloped urban village in Guangzhou.Investigation on villagers' citizenization level is carried out to examine how the redevelopment project affects villagers' citizenization.Our finding reveals that the redevelopment project has improved villagers' living conditions and income levels remarkably,has facilitated the mixed housing patterns of diversified social groups,and has increased their chances to be integrated into urban social life.Nevertheless,as villagers kept their traditional mode of thinking,behavioral habits,and lifestyles,the citizenization process made little progress in terms of land ownership,management mode,mode of livelihood,and citizen identity.Therefore,the Liede redevelopment project has turned out to be a "passive citizenization" process,i.e.,villagers merely received socio-economic benefits from the redevelopment passively but remained isolated from the socio-economic and cultural system of the city.Finally,this paper proposes some policy suggestions to promote the citizenization from both conceptual and practical perspectives.

Globe230k:A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping

Global land cover map provides fundamental information for understanding the relationship between global environmental change and human settlement.With the development of data-driven deep learning theory,semantic segmentation network has largely facilitated the global land cover mapping activity.However,the performance of semantic segmentation network is closely related to the number and quality of training data,and the existing annotation data are usually insufficient in quantity,quality,and spatial resolution,and are usually sampled at local region and lack diversity and variability,making data-driven model difficult to extend to global scale.Therefore,we proposed a large-scale annotation dataset(Globe230k)for semantic segmentation of remote sensing image,which has 3 superiorities:(a)large scale:the Globe230k dataset includes 232,819 annotated images with a size of 512×512 and a spatial resolution of 1 m,including 10 firstlevel categories;(b)rich diversity:the annotated images are sampled from worldwide regions,with coverage area of over 60,000 km^(2),indicating a high variability and diversity;(c)multimodal:the Globe230k dataset not only contains RGB bands but also includes other important features for Earth system research,such as normalized differential vegetation index(NDVI),digital elevation model(DEM),vertical-vertical polarization(VV)bands,and vertical-horizontal polarization(VH)bands,which can facilitate the multimodal data fusion research.We used the Globe230k dataset to test several state-of-the-art semantic segmentation algorithms and found that it is able to evaluate algorithms in multiple aspects that are crucial for characterizing land covers,including multiscale modeling,detail reconstruction,and generalization ability.The dataset has been made public and can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.