The changing global environment and techno-economic paradigm exert remarkable influences on the economy-environment relationship. However, economic geographers across the world have primarily retreated from tackling environmental challenges at the same time. In response, Environmental Economic Geography becomes an emerging theme of Economic Geography since 2000, which makes efforts to reconsider the economy-environment relationship from the perspective of Economic Geography. The development of Economic Geography in China adheres to the tradition of human-environment interactions, which lays the foundation for developing Environmental Economic Geography. The rapid growth and transition of China’s economy also provide rich cases for conducting Environmental Economic Geography researches. Generally, Environmental Economic Geography does not necessarily require a fresh start. Instead, it can be derived from the theoretical thinking of Economic Geography. This study proposes three potential themes. First, Environmental Economic Geography can revisit the location of economic activities which are environment-relevant, leading to a better understanding of how the changing environment reshapes the global economic landscape. Second, Environmental Economic Geography can trace the flows of factors and the interactions between actors by network analysis, providing better chances to project the transfer of environmental risks at different geographical scales. Third, Environmental Economic Geography can look into how different actors interact with each other across different regions and at multiple levels of institutional contexts, unravelling the process of green innovation and green transition in different geographical contexts.
The big data of spatio-temporal behavior trajectory have multiple complex characteristics, such as sequential properties, spatio-temporal interaction, and multidimensionality. Facing these complexities, this paper explores a new pattern mining method of spatio-temporal behavior trajectories to provide a more flexible approach to pattern mining, particularly for the trajectory big datasets. This paper explores a new spatio-temporal behavior pattern mining method by three steps. First, based on Social Affiliation theory, we develop a conversion method from space-time paths to spatio-temporal networks by individuals, by incorporating the analytical framework of time geography and complex network. The temporal and spatial attributes of activities are saved in the spatio-temporal networks. Second, this study uses the Louvain Method to detect communities, that is, the clustering trajectories or behavior patterns in a behavioral network. This community-detection method is widely used in the field of network science, particularly for handling a large set network data. Third, relying on the visualization techniques from time geography, this study integrates the advantages of 2-dimentional and 3-dimentional charts to analyze and display the characteristics of spatio-temporal behavior patterns based on multiple perspectives. By mining similar or cohesive communities, this study further explores the characteristics of spatial heterogeneities in behavioral patterns and their day-to-day variabilities. By adopting a weekly-long trajectory data from a GPS-based individual activity-travel survey in 2012 Beijing, this study reveals three major findings. First, complex network analysis can effectively extract grouping patterns with similar behaviors, along with identifying representative behavior patterns from messy trajectories. Besides, the new approach provides a new perspective for further exploring the spatio-temporal interaction of human activities in time geography and social geography. It has the capacity to flexibly handle heterogeneous and multidimensional behavior trajectories and detect patterns from trajectory big data by varying narratives of activity-travel events, spatial interaction levels, and lengths of time series or sequences. Second, in the case study, considering the time allocation characteristics and sequence of activities, activity and sequence narrative are selected for analysis, and 6 major behavior patterns based on similarity mining are analyzed and compared. Results show that the residents have a typical behavior pattern in which they worked from 9 AM to 7 PM after sleeping on weekdays, and then conducted leisure, entertainment, housework and private affairs before 9 PM. Third, this study extends the complex network method to behavior pattern mining with multi-day spatio-temporal data. The effect of spatial interaction was considered when measuring the individual connections, and the spatial distribution characteristics of community were compared by adjusting the distance attenuation factor. This finding suggests significant spatial heterogeneities in behavioral patterns of surveyed residents in the suburb of Beijing. The residents also have significant day-to-day variabilities in spatio-temporal behavior patterns, mainly between weekday and weekend as well as between Saturday and Sunday.
With the building of sustainable cities being listed as one of the sustainable development goals (SDG 11) of 2030 agenda, it is more urgent to evaluate and simulate the level of sustainable development of cities. Urban sustainable development is a complex system, but the traditional evaluation method is difficult to cope with the feedback mechanism and dynamic evolution. In this article, an urban sustainability assessment model based on the System Dynamics is proposed, covering six urban subsystems of economy, livelihood, risk, environment, resources and pollution governance. Taking Beijing-Tianjin-Hebei region as a study case, this article used the model to evaluate and simulate the temporal and spatial trends of sustainable development level of each city from 2005 to 2035. The accuracy test was conducted by using the indicators of each city in 2017, and the relative error between the output value of the model and the actual value is less than 10%. The model can be used to evaluate and simulate the urban sustainability score accurately and the results show that: 1) Due to the dependence of resource endowment and development path, the sustainability in the subsystem of different cities varies. Complex and diverse interactive relationships among the urban subsystems in this region can be divided into three main types; 2) In recent years, the comprehensive sustainability of cities in the region shows fluctuating but rising trends. In terms of the historical trajectory and regional development policy, the level of sustainable development of all cities will be greatly improved in future, but the huge gap between the urban sustainability of Beijing and that of other cities exists objectively and is likely to be maintained for a long time. This article discusses the feedback mechanism among the variables in the urban system and the dynamic evolution of the sustainable development level of cities in the region, which is helpful to understand the complexity, dynamics and heterogeneity of urban sustainable development. In addition, the evaluation method proposed in this study can be applied to other regions easily to simulate the variations of different urban subsystems and comprehensive sustainability among cities, which is of great practical significance to formulate reasonable regional development policies and achieve the sustainable development goals for cities.
Creative talents are the new dynamics to urban and regional economic development. However, limited attention was paid to the spatial agglomeration mechanism and driving factors of creative talents. Based on the theory of urban amenities, the paper establishes an index system of urban amenities to the spatial agglomeration of creative talents in design sectors in China. An extensive dataset with over 3 million designers registered in the online platform zcool.com was adopted as the sample for this study. It aims to examine the spatiality of China’s creative talents and its association with urban amenities for cultural production. Using the quantitative methods such as location quotient, geographic concentration index, and the negative binomial regression method, we investigate the characteristics and mechanism of the spatial concentration of Chinese creative designers. The results suggest that: First, the distribution of creative designers shows a significant spatial agglomeration. First-tier cities like Beijing are the most dominated, but emerging cities such as Hangzhou, Changsha, Wuhan, and Zhengzhou are also well-performed, even better than some first-tier cities. Second, amenity-based approach has a strong explanatory power in explaining the agglomeration of Chinese design creative talents. The cultural environment, educational environment, and natural environment are the core factors that affect the agglomeration of design creative talents. Third, this study echoes with some conclusions of the creative class theory. Cultural infrastructures, cultural heritages, cultural tourism, high-quality human capital, warm climate, and good air quality were essential to enhance the city’s attractiveness to creative talents. Simultaneously, it is found that scientific research investment has a significant crowding-out effect on the accumulation of creative talents. The amenity factors of the living environment that were emphasized by Western scholars have a limited impact on agglomeration of creative talents. In conclusion, this research adopted a more inclusive urban amenity theory, and proved that urban amenities matter in the process of concertation of creative designers. It also provides a reference for local governments to promote their brain gain policy for creative talents.
The last decades have witnessed a steady increase in studying urban vitality. However, the information that could depict 3D space has not been included in studies. This research attempts to incorporate street view imagery data to elaborate the spatiotemporal variability of vibrancy in Futian District, Shenzhen. As for the street-level physical environment, attributes are extracted from street view images by deep machine-learning algorithms SegNet. 2D built environment indicators of streets were extracts from the Open Street Map and POI points. A multivariate econometric are framed to examine the association between urban environment and the vitality at the street level of commercial streets and residential streets. The results showed that: 1) The peak hours of activity on commercial streets are 18:00 to 20:00, and the peak hours of activity on living streets are 11:00 to 13:00. 2) Futian District Street Vitality Shows Polycentric Structure, whose distribution had shown obvious spatial differences with time. 3) Variations in the temporal and degree effects of different built environment indicators on street vitality. Increasing the mix of facilities contributes to the morning and nighttime vibrancy of commercial streets, as well as the afternoon vibrancy of living streets. More compact streets have a positive impact on commercial streets’ vitality, and safer pedestrian environments have a positive impact on both living and commercial streets’ vitality. Improving the building continuity will promote the vitality of commercial streets. Higher interface richness increases the vitality of living streets in afternoon and evening. An increase in the richness of the interface will promote the daytime vitality of the residential street.
Using the panel data of the Yangtze River Delta urban agglomeration to calculate the integration index, this article reveals the spatial spillover effect of integration process on tourism economy by means of spatial Durbin model (SDM). The main conclusions are as follows: Firstly, the spatial distribution of the integration index of the Yangtze River Delta urban agglomeration is unbalanced, showing a decreasing pattern from the provincial capital to the surrounding areas on the whole. Secondly, the SDM can fairly estimate the spillover effect of integration process. From the estimation results of the SDM, the integration process of the Yangtze River Delta urban agglomeration has a significant spillover effect on tourism economy. Every 1% increase in the process of local integration will promote the tourism economy of surrounding areas to increase by 0.575%. As far as the local effect is concerned, each dimension of integration has a significant positive impact on tourism economy, and infrastructure integration is one of the more important factors. In terms of the spillover effect, each dimension of integration on tourism economy is lower than local effect, and infrastructure integration has a certain negative impact on tourism economy of surrounding areas. Lastly, in the future, the high-quality development of tourism in the Yangtze River Delta urban agglomeration should not only attach importance to local integration, but also spatial spillover effect of integration, create a good surrounding environment, issue relevant policies, and strengthen regional coordination and cooperation.
With the continuous upgrading of the biomedical industry, a closer institutional connection and a more complete feedback mechanism have been gradually established between its innovation system and local assets. In this article, the coupling coordination characteristics and coupling modes of the flow of technology, capital and talents in different cities in the field of biomedicine from 2000 to 2018 were analyzed by constructing a directional coupling coordination degree model. We further used the mean square error-mutation progression method and the geographically weighted regression model to explore the evolution pattern of dual innovation cold and hot spots, the temporal and spatial heterogeneity of dual innovation driving factors, and their dominant effects in different coupling regions. The results show that: 1) Phenomenon of ‘decoupling’ is prominent when cities gather multiple innovation elements, and phenomenon of ‘geese effect’ is obvious when deploying multiple innovation elements. According to the coupling coordination of multi-elements flow, the geographical space can be divided into a conservative zone dominated by dependent coupling, a balanced zone dominated by reciprocal coupling, and a star zone dominated by absorption coupling; 2) Incremental innovation has spatio-temporal inertia, while breakthrough innovation has spatio-temporal intermittent. The two promote each other and have a spatio-temporal chain effect. Cities in conservative zone mainly are cold spots. The incremental innovation hotspots show a spatial growth trend of point-axis diffusion, while the breakthrough innovation hotspots randomly jump among important node cities in the balanced zone. The ambidextrous innovation hotspots of the Yangtze River Delta and the Pearl River Delta urban agglomeration show a circle distribution in the star zone; 3) Driving factors of the ambidextrous innovation have temporal instability and spatial heterogeneity, and the factors in different coupling divisions have obviously different spatio-temporal dominant effects on the ambidextrous innovation modes.
As an important part of urban recreation space, the layout of accommodation industry plays an important role in capacity, activating commercial service vitality and even expanding urban functional areas. In this article, the central model, the theory of spatial autocorrelation and the nuclear density method are used to analyze the spatial pattern characteristics of Shanghai’s accommodation industry and its spatial response relationship with the road network structure from the angle of urban road network form. Results show that the Shanghai accommodation industry has spatial agglomeration, type differentiation and distance attenuation, the agglomeration core is consistent with the network node density core, mainly around the central urban area to form a primary agglomeration core, star hotel agglomeration distribution in the central urban area, economic hotels and inns outside the primary agglomeration core to form a number of scattered agglomeration core, hotel agglomeration attachment is more obvious, homestay agglomeration degree is the highest. There is a significant spatial correlation between the distribution of different types of accommodation facilities, the dependence on the road network is pluralistic, the degree of influence by the road network is different, the regional traffic accessibility with higher road connection value is better, and the density of accommodation facilities is also higher. Economic hotels are most dependent on the relative accessibility of urban road network and linear traffic efficiency, and inns and homestay depend on the accessibility between road network nodes. The development of accommodation industry should take into account the urban road network structure and consumer demand for reasonable site selection under the premise of product preference design, so as to achieve the maximum comprehensive benefit.
The commuting efficiency of polycentric spatial structure is not only controversial for a long time, but also its test method needs to be improved. Based on the ideal commuting mode of polycentric spatial structure, this article constructs a theoretical model of commuting distance distribution, compares the measured results with the theoretical model, and analyzes the commuting efficiency. Taking Shanghai and Hangzhou as research objects, it is found that: 1) The polycentric spatial structure does help shorten the commuting distance, but with the increase of the distance from the job center, the positive effect of the job center on reducing the commuting distance gradually weakens; 2) Shanghai, with a higher level of social and economic development, has better implementation of commuting efficiency of its polycentric spatial structure than Hangzhou. The study discusses the impact of city size, implementation time of polycentric spatial structure, housing market, etc. On commuting efficiency: Shanghai has a more mature polycentric space structure and a higher proportion of rental housing. In order to maintain the normal operation of the city, it is necessary to have a more efficient transportation organization. And residents do have more experience and more housing options to adjust jobs-housing space to shorten commuting distance; The urban functions of Hangzhou is still in developing condition, so the commuting efficiency of polycentric spatial structure has not fully played its role.
Based on the data of population and city, this article studies the distribution and change of China’s economic center of gravity in the past 2 000 years (A.D.2-2015) by using historical temporal section method and GIS spatial analysis method. The results show that: 1) Over the past 2 000 years, China’s economic center of gravity has generally moved toward the southeast, but before the Tang Dynasty, it mainly moved to the southwest. After the Song Dynasty, it mainly moved to the southeast. It means that the spatial pattern of China's economy has shifted from North-South differences to East-West differences. And the economic development process and pattern of the southeastern half of “Shenyang-Lanzhou-Xishuangbanna Arc” in China, can represents the national economic development to a certain extent. 2) The “centers of gravity distribution area”, formed by the population, city and economic centers of gravity, which are closely related, is roughly the so-called “Central Plains” area in history. These centers of gravity are always in the east of China’s geometric center, which illustrates that the economic gradient difference among the East and the West of China has a long history and is quite stable. 3) The change of China’s economic center of gravity is constrained by the changes of population pattern and city pattern. As time goes by, economic equivalent of per a person tends to decrease, and the economic equivalent of per a city tends to increase, which indicates that the impact of city economic development on the overall economic pattern is increasingly significant.
Intangible cultural heritage is the product of cultural-ecological environment, and analyzing its spatial distribution characteristics and influencing factors will help to further improve the management and protection of intangible cultural heritage. Therefore, taking Jiangsu Province as an example, the nearest neighbor index method and nuclear density estimation method are used to visualize the temporal and spatial distribution of intangible cultural heritage at the provincial level and above, and the buffer analysis method and geodetector model are used to analyze the influencing factors of the distribution of intangible cultural heritage. The conclusions can be drawn as follows: 1) There are differences in the distribution of Jiangsu intangible cultural heritage in time and space dimensions. The intangible cultural heritage in different historical stages has obvious differences in quantity and type due to the economic, cultural and political influence of the time. At the same time, the intangible cultural heritage center of Jiangsu has shown a ‘circular southward’ development trajectory in the time dimension. From the perspective of spatial distribution, the overall distribution is in a cluster shape, forming six high-density core areas, two sub-high-density core areas and six low-density core areas. 2) The temporal and spatial distribution of intangible cultural heritage in Jiangsu is affected by natural, social and economic factors. First of all, natural factors have a significant impact on the distribution of intangible cultural heritage by river systems. Secondly, the policy-oriented factors among social factors have the greatest impact on the distribution of intangible cultural heritage. The influence of historical and cultural factors on intangible cultural heritage is mainly manifested in the number, type and artistic characteristics of intangible cultural heritage. Finally, economy has a direct relationship with the generation and inheritance of intangible cultural heritage, and the economic level directly affects the degree of attention to intangible cultural heritage. The countermeasures can be proposed as follows: 1) Construct the Grand Canal Cultural Belt (Jiangsu Section) to promote the protection and utilization of intangible cultural heritage. In the process of promoting the construction of the Grand Canal Cultural Belt (Jiangsu Section), the protection and development of intangible cultural heritage along the Grand Canal should be placed under the overall planning and overall coordination of the cultural belt. 2) Attention should be paid to the overall protection of the intangible cultural heritage ‘cultural ecosystem’. The protection of intangible cultural heritage should rely on the original cultural ecosystem to establish different cultural protection areas, and carry out effective inheritance and development of intangible cultural heritage, so as to ensure the subsequent survival of intangible cultural heritage and maintain the local cultural characteristics. 3) Take a correct view of the reasonable interaction between intangible cultural heritage and the economy. On the premise of ensuring that the core of intangible cultural heritage remains unchanged, giving full play to the economic role of intangible cultural heritage and promoting the integration of intangible cultural industry and tourism will not only benefit the development of local folk economy, but the resulting economic benefits can still feed back protection and inheritance of intangible cultural heritage.
Urban expansion and suburbanization sharply increase residents’ commute duration, reduce opportunities of community participation, and then reduce community social capital. Previous research has found that commute duration is negatively associated with social activity, social participation and social trust in western cites. However, existing research lacks the direct evidence on the mechanism of the influence of daily activity system on community social capital. Furthermore, few studies conducted in Chinese cities. During the fast urbanization and huge transition process from danwei to neighborhoods in China, similar problems of home-work separation, long commuting and decreased social capital have also appeared in Chinese communities. Therefore, based on the 2017 social cohesion and activity diary survey in 26 communities in Beijing, this study explored the influence mechanism of commute burden and community activities on community social capital through structure equation model (SEM). Community social capital was measured by community social network structure and social trust and mutual value. Community activities were measured by whether respondents had out-home non-work activities within community life circles (1 000 m buffer). They were further divided into two categories, personal and family-related affairs and social and recreational activities. SEM was employed to testify the assumption that commute duration has negative influence on community social capital, through the mediating effect of community activities. Socio-economic status, housing tenure and housing source, as well as community location, were used as exogenous variables to control the SEM. The results showed: 1) Long commute duration was found to negatively relate to activities conducted within community circles, and then negatively influence community social network and further perceived social trust and common value. 2) The role of different types of activities within community life circles is different: the mediating effect of social and recreational activities were significant while personal and family-related affairs had no significance. 3) Young, highly-educated and high-income residents, as well those with shorter length of residence and living in suburban communities have more commute burden, less community activities and lower community social capital. This study has proved the influence mechanism that commute burden has negative influence on community social capital through the mediating effect of community activities. The result reflects the social effects of individual daily activity system. Hence, jobs-housing balance can not only reduce vehicle travels and transportation carbon emissions, but also benefit community social construction through increasing community activities. In addition, to improve the quality of life of residents through community life circles should not be limited to the improvement of the built environment and service facilities, but should start from the residents’ daily activity-travel system and improve the overall activity arrangement, including jobs-housing relationships.
Considering the urban traffic connection and passenger travels behavior choices, the Beijing-Tianjin-Hebei urban agglomeration is used as the research object, and the prefecture-level cities and counties are used as the research unit to estimate the shortest travel time of cities or counties. Through the combination of weighted average travel time, economic potential and spatial autocorrelation methods, the accessibility of each stage of the high-speed rail network expansion in the Beijing-Tianjin-Hebei region is measured. Then, it further explores how accessibility changes affect the spatial distribution pattern of economic activities in cities or districts. The results show that: 1) With the gradual increase in cities connected to high-speed rail, the accessibility values ??of cities and counties in the Beijing-Tianjin-Hebei region are significantly reduced, and the level of accessibility has been significantly improved. Spatially, an irregular ring-shaped distribution pattern that expands outward upon the directions of Beijing-Tianjin, Beijing-Guang, and Beijing-Shanghai is formed, and the level of accessibility presents the characteristics of high in the south and low in the north. 2) The economic potential for high-speed rail in the core cities along the line is significantly higher than that in the peripheral cities, forming an ‘inverted V’ spatial pattern of Beijing as the apex and gradually decreasing from the center of the outer circle. The economic potential for the Beijing-Tianjin-Hebei counties presents a diminishing distribution feature of Beijing and Tianjin as the core areas along the opened high-speed rail lines. The emergence of high-speed rail clearly strengthens the impact on commuting time of economic potential. 3) With the expansion of the time range of accessibility, the spatial agglomeration of economic activities in the Beijing-Tianjin-Hebei districts and counties gradually weakened, and the optimal radiation range was within 3.5 h. The economic activities in the Beijing-Tianjin-Hebei region are not completely randomly distributed in space, and regions with similar economic development levels tend to be adjacent. Low-low agglomeration areas are mainly concentrated on the northern and southwestern margins of the Beijing-Tianjin-Hebei region.
Previous studies have shown a significant association between burglary and micro-environment, and burglary usually has a spatiotemporal phenomenon of near repeat occurrence. However, there is no literature on the comparative analysis of spatial influencing factors of the distribution of near repeat cases and non-near repeat cases in China. Combing with social disorganization theory, routine activities theory and crime patterns theory, this study taking YP District of a large city in the southern China as an example, and divides the burglary cases into two types by importing into near repeat caculator, namely, near repeat cases and non-near repeat cases (isolate cases). Using the data of burglary alarm, the sixth census data, and point of interest (POI), a binary logistic regression model was constructed to explore the influence of the micro characteristics of regional environment on the distribution of near repeat and non near repeat cases of burglary. The results show that the higher the spatial-temporal interaction strength between the criminal target and potential criminals, the more significant the pattern of near repeat. More specifically, the probability of near repeat cases in areas with more residents, banks & ATMs and parks is higher than that of isolate cases, while the communities with higher road density are difficult to have near repeat cases. We also find that the communities with higher proportion of youth population and more low-rent households will aggravate the concentration trend of vulnerable people and problem people, and the possibility of near repeat crimes will increase. The impact of the proportion of youth population is higher than that of low rent households. These findings can provide certain reference for the public security prevention & control and police policy making of burglary near repeat crime at the micro community level.
Supported by China Academic Journal Network Publishing Databas, this study collected 566 articles fromActa Geographica Sinica,Geographical Research, Scientia Geographica SinicaandProgressinGeography (four geography periodicals for short) of CNKI. The selected articles were all related to agricultural geography, and their publication dates covered from the establishment of the four geography periodicals to December 2020. Taking the 566 articles as the analysis object and using bibliometrics and literature summarization methods assisted by Citespace, this study identified the topic context, knowledge evolution, historical hotspots, development of research, institutions, and authors. The analyses reflected that the past development history, the current main research topics, and the future development trend of agricultural and rural geography in China. The results showed that agricultural geography, settlements and ancient villages, land use and development patterns, rural settlements and its spatial pattern, urban-rural integration, new rural construction, rural restructuring, rural revitalization, and urban-rural integration all played important leading roles in different periods. Although the research on these topics has been flourishing for a long time. However, it has experienced 3 stages: the agricultural geography stage, the agricultural geography and settlement geography stage, and the urban-rural integration and rural revitalization stage. The process has presented four major transformations. First, research perspective modified from single agricultural geography and rural settlements to integrated agricultural and rural geography. Second, research content transformed from agricultural and social elements to the process, mechanism, and influence of regional urban-rural integration. Third, research methods and techniques changed from survey statistics, the field observation and digital simulation to big data, multi-source spatiotemporal monitoring simulation and agricultural geoengineering practice. The path from rural transformation, urban-rural integration, rural revitalization, to rural high-quality development will become the general logic and new normal of future urban-rural development. It will be the focus and direction of agricultural and rural geography research in the future. The main research topics include rural areal types, rural functional transformation, rural spatial restructuring, rural settlements reconstruction, rural human settlements quality, new factors and mechanisms of rural development, rural elasticity and sustainable development, and rural industrial structure adjustment and upgrading, overall planning and construction of urban-rural infrastructure and services, and so on. To serve national rural revitalization strategy better, the comprehensive research should be emphasized in disciplinary development, the understanding of the new driving forces of agriculture-rural development should be deepened, and the theoretical and methodological innovation of rural geography research should be effectively strengthen.
As an important way of new urbanization construction, tourist town is a social and economic space integrating tourism industry and urbanization construction. The construction of a reasonable spatial layout of tourist towns is of great practical significance to promote the development of tourism industry and the construction of new urbanization in Fujian Province. This study divides the tourist towns in Fujian Province into four types: Ecological habitat type, tourism reception type, resource leading type and characteristic industry type. The spatial distribution characteristics and influencing factors of 155 tourist towns in Fujian Province were studied by using the spatial statistical analysis methods. The results show that: 1) The overall spatial distribution of tourist towns in Fujian tends to be uniform, and the quantity distribution of each city is relatively balanced. 2) The core density of tourist towns in Fujian province shows the distribution characteristics of “high in the east and low in the west”. The number of ecological residential tourist towns is the largest, and the overall distribution density is the largest; the core density of tourist reception towns and resource-based tourist towns is close, followed by; the overall distribution density of characteristic industrial tourist towns is the smallest. 3) The cold spots of Fujian tourist towns are Putian, Quanzhou and Xiamen; the sub cold spots are Nanping, Fuzhou and Zhangzhou; the sub hot spots are Sanming and Ningde; and the hot spots are Longyan. The development of tourist towns in Fujian Province is mainly in cold spot area and sub cold spot area. The spatial difference between cold spot area and hot spot area is obvious, with a significant “block” distribution characteristics. 4) Economic base, urbanization level, population density and tourist market are the main factors affecting the spatial distribution of tourist towns in Fujian Province, and the impact on each type of tourist towns is significant. In the future, the influence of geographical conditions, culture and other factors can be discussed more systematically, and the time evolution law of the spatial distribution of tourism towns in Fujian Province, the influence degree of various influencing factors and the interaction between influencing factors can be deeply explored.
In this article, we directly assess the degree of environmental decentralization according to the allocation of environmental managers among different levels of government. By incorporating fiscal decentralization indicators, the provincial panel data and dynamic spatial econometric model are used to empirically test the impact of environmental decentralization on carbon emissions from a spatial perspective. The study found that: 1) China’s provincial carbon emissions have significant inertia dependence and spatial path dependence. The increase (decrease) of provincial carbon emissions will lead to the increase (decrease) of carbon emissions in neighboring regions. 2) At the national level, environmental decentralization (ED), environmental administrative decentralization (EAD) and environmental monitoring decentralization (EMD) significantly reduced China’s carbon emissions, while environmental supervision decentralization (ESD) and fiscal decentralization (FD) significantly increased carbon emissions. Similarly, the interaction of ED and its decomposition indicators and FD also significantly promoted carbon emissions, and the impact was related to the types of environmental management decentralization. 3) The carbon emission effects of environmental decentralization in different regions were heterogeneous. The inhibition effect of ED, EAD and EMD on carbon emissions in the western region was significantly greater than that in the eastern and central regions, but the inhibitory effect of ED and its decomposition index and FD interaction on carbon emissions in the eastern region was significantly stronger than that in the central and western regions. The above results provide theoretical support for China to construct a differentiated carbon emission environmental management system from two aspects of regional differences and environmental management power categories.
Based on sea-land thermal contrast and coupling relationship in winter, a local zonal sea-land thermal difference (SLTD) index was constructed. From the perspective of atmospheric circulation and precipitation, this article reveals the influence of the SLTD on relative humidity (RH) in eastern China, and further discusses the climatological association with the formation, transformation and trend changes of winter fog and haze in South China. The results show that, in winter, the coupling region with significant sea-land thermal contrast is located in south China region and the eastern part of the Philippine Sea. The SLTD has a decadal change of about 17 years, and the trend shift from strengthening into weakening in the early 1980s was obvious. As the SLTD increases, the winter northeast wind in South China is abnormally enhanced, and the moisture transport from the east China seas and the Sea of Japan to South China and rainfall are also strengthened, which is conducive to the abnormal increase of RH in South China. Under the modulation of the SLTD, the wet period and dry period in South China alternated with cycle of about 17 years. In the wet period before 1980s, the RH in fog days in south China remained basically unchanged. The environmental RH tended to decrease at the beginning of the 1980s, which caused less fog days. Due to the background of RH in South China is more conducive to the formation of haze in the case of the pollutant discharge or migration accumulation increase, the reduction of RH inhibits the transformation of haze into fog, making fog days continue to decrease while haze days to increase with highest rate especially in the dry peak period after 2000. Since 2020s, South China has subsequently entered a dry period, the increase in saturation specific humidity caused by climate warming is more likely to cause extreme and dangerous characteristics such as stronger and longer duration of haze weather in the future.
In this article, four methods including current operational forest fire danger index, single-factor contribution model, logistic regression model and random forest model, are inter-compared for their prediction accuracies of forest fire probability at daily and monthly timescales in Guangdong Province. Excepting for the daily meteorological observations and MCD14DL Active Fire product, the microwave-based soil moisture dataset is also included in the latter two machine learning models, in order to evaluate their potential utilities in forest fire prediction. The results show that the logistic regression and random forest models significantly outperform the current forest fire danger index and the historical single-factor contribution model, increasing the accuracy by approximately 20%. The normalized forest fire probability from random forest model prediction are strongly correlated with the normalized active fire number (MCD14DL), showing correlation coefficient of 0.476. In addition, inclusion of soil moisture information in the meteorological factors-based model slightly increases model accuracy, which evidences the importance of surface soil moisture in forest fire prediction. The results of this study could provide reference for efficiently mining earth observations to improve forest fire prediction at different time scales, and therefore improve regional disaster preparedness measures.
