The assessment of the carrying capacity and suitability of spatial resources and the environment can provide effective guidance for regional planning and make vital contributions to the high-quality advancement of society and the economy. Additionally, this scientific evaluation of the spatial carrying capacity and suitability of urban production–living–ecological space (PLES) has important scientific value as well as practical significance for territorial spatial planning. This paper takes the cities along the Yellow River Basin (YRB) as the research object; establishes the PLES resource and environment carrying capacity evaluation index system; uses the multi-indicator superposition method and entropy weight method to evaluate the ecological importance, production and life carrying capacity of 78 cities in the YRB from 2010 to 2020; obtains the final ecological importance, production and life suitability levels based on the carrying capacity combined with the location conditions; and uses exploratory spatial data analysis (ESDA), the barrier degree model and other methods to determine the cities’ spatial and temporal patterns and influencing factors. The findings indicate that: (1) Ecological importance is characterized by “high upstream and low downstream”; the suitability for production is higher mainly in the eastern coastal area; the suitability of life as a whole is increasing, and the higher grade is in some provincial capitals and surrounding cities. (2) The local Moran’s I all passed the test, and the relationship between PLES showed a significant positive spatial correlation. The clustering characteristics of ecological importance and production suitability are strong, while the clustering characteristics of suitability for living functions are relatively weak. (3) Biodiversity, significance of water conservation and significance of wind and grit control functions are the main barrier factors affecting the ecological importance of the YRB; industrial value-added output per unit of industrial land, number of industrial enterprises above the scale and GDP per capita are the dominant factors affecting the production suitability of the study area; total water-resource utilization per capita, total sewage treatment per capita and residential land area per capita are the main barrier factors of living suitability. The national land space is a valuable natural resource of the state and the nation, and is the spatial carrier for the nation to carry out production, living and ecological civilization construction. However, rural populations have generally declined in countries around the world, and the hollowing out of people first emerged with the urbanization and industrialization process of recent years. Over the past forty years or so, China’s rate of urbanization has increased from 17.92% in 1978 to 64.72% in 2021, and the rapid development of urbanization and rapid industrialization has caused a strong squeeze on ecological space and agricultural production space [PLES was developed from agriculture’s versatility, which was initially proposed in the Uruguay Round Agreement on Agriculture (URAA) , and acaPLES focuses on identification and classification, spatial conflict, and simulation and prediction. In terms of identification methods, the identification methods of PLES are mainly divided into the top-down indicator measurement method and the bottom-up land-use-type method, with the former evaluating the land use function by constructing an indicator system using the coupled coordination degree model , the entThe evaluation of PLES is a crucial foundation for improving the spatial development of national land , and it In summary, many researchers have researched the PLES from different perspectives and using different methods, which shows the diversification of research themes, rationalization of index construction and maturation of research methods, but there are still areas that need in-depth research. First, most of the studies on the evaluation of the PLES focus solely on the carrying-capacity evaluation of resources and the environment or the suitability of spatial resources and the environment; another key fact to remember is that there are few studies that combine the two. Second, most researchers adopt static evaluation in the process of evaluation, but resource and environment carrying capacity and suitability level are not constant, but experience dynamic change, and dynamic research is the inevitable way to scientifically recognize the evolution of a regional development pattern. Hence, this study takes the YRB as the study area, establishes a multilevel evaluation system based on the idea of PLES based on the traditional evaluation levels of ecology, agriculture and towns in the “double evaluation” system, and implements the relatively conceptualized concept of PLES into the “double evaluation” process through the screening of evaluation indices. From the perspective of PLES, examining the current situation of the spatial development of national land, analyzing its carrying function and the suitability of development, the concept of PLES is no longer a slogan but is actually reflected in the basic analysis of planning.The Yellow River is located at latitude 31°31′ N~43°31′ N, longitude 89°19′ E~119°39′ E. The YRB originates in China’s Bayan Har Mountains of Qinghai Province, from west to east, flowing through Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong, 9 provinces, and the Yellow River runs through the eastern, western and eastern regions of China. According to natural background conditions, the YRB is relatively rich in land resources, containing 15% of the country’s arable land, but the largest contradiction in the YRB is water shortage, meaning that water resources cannot meet the demand. Although the Yellow River is the fifth longest river in the world, the amount of river runoff is only 2.2% of the national amount of river runoff. The population in the YRB and its downstream irrigation areas mean that 12% of the country’s population use water from the Yellow River. Additionally, 67% of water resources have been developed and used in the YRB. This land for production and living is constrained by topography, water resources and other natural conditions. In terms of the social economy, the YRB’s entire advancements lag behind those of other regions, and there are some problems in regional development that are not balanced; conflicts between environmental protection and economic development are more severe. Generally, the YRB is limited by poor resources and the environment and unreasonable ways of utilizing territorial space, resulting in many problems, such as a fragile ecological environment, a lack of water resources in local areas, and the urgent need to enhance the status of land development. The study area is shown in With reference to national regulations and existing academic research results, combined with the principles of regional specificity, data accessibility and index representativeness, this text used the model of single factor-integrated evaluation to evaluate the carrying capacity of resources and environment in ES, PS and LS around the elements of resources of the land as well as water, climate, natural disasters and socioeconomic factors. The current utilization level based on the shortcomings of the elements is constructed in this text, and the carrying capacity of the PLES is evaluated by measuring the carrying capacity for territory resources, water resources and nature. If development or construction activities in excess of the carrying capacity exceed the bottom line of resources and the environment, then the area is inevitably an unsuitable zone for production and life. The bottom-line restraint of resources and environment carrying capacity as suitability reflects “bottom line thinking” and the goal of forcing the transformation to high-quality development . TherefoThis study takes the YRB as an example, and selects evaluation indexes from soil and water, climate, environment, socio-economic and location conditions from production, life and ecological perspectives to investigate the ecological importance and the maximum scale of production and life, and to analyze and evaluate the importance of ecological protection, the suitability of agricultural production and the suitability of urban construction, respectively. The technical roadmap is as follows .ESDA is a more ideal data-driven analysis method. This method takes spatial correlation as the central concept and uses descriptions and illustrations of the spatial phenomena of objects to discover spatial anomalies and spatial clustering research objects intuitively to explore and study the interaction mechanism of spatial elements. ESDA analysis involves measurement and testing of global spatial autocorrelation and identification of local spatial association.(1)Global spatial autocorrelationGlobal spatial autocorrelation reflects the overall trend of the spatial correlation of observed variables in the whole study area, and the commonly used measurement index is global Moran’s I, which is used to explain the whole spatial correlation of the suitability of the land use of PLES.In Equation (1), (2)Cold- and hot-spot analysis, In Equation (2), In this paper, the obstacle degree model is used to analyse the factors that hinder the suitability of PLES and is calculated from the factor contribution ” issued by the Ministry of Natural Resources of China in June 2019 and January 2020 and related research ,37,38. FThe spatial classification of the YRB is based on land use types, with vector data on administrative regions, DEM, NPP (net primary productivity), NDVI , population density, spatial distribution of terrestrial ecosystem types and land use data. The data were collected from the Data Centre for Resource and Environmental Sciences of the Chinese Academy of Sciences. Meteorological data were collected from the National Center for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA) and soil texture-type data from the Harmonized World Soil Database (HWSD). Economic and social statistics are collected from the China Urban Statistical Yearbook 2010–2020 and the National Economic and Social Development Bulletin of each municipality, of which water resources data are taken from the water resources bulletin of each province and municipality.Data were processed based on ArcGIS 10.2 software, and the higher level of the significance of ecosystem service function and sensitivity of ecology was taken as the initial result of the ecological protection importance ranking. According to the criteria of the “Technical Guide for Carrying Capacity Evaluation of Resources and Environment and for Suitability of Territorial Area Development”, the importance of ecological protection will be divided into three parts: the importance of the general level, the importance of the higher level, and the importance of the highest level, and the spatial and temporal patterns of ecological-protection importance levels will be mapped for 2010, 2015 and 2020 .First, ecological patch concentration can portray the scale and spatial differentiation of ecological service functions and ecologically sensitive areas , so the In 2010, the level of ecological importance protection was very high, accounting for 57.75% of the entire region of the study area. The most ecologically crucial places were located in Tianshui, Baoji, Hanzhong, Ankang, Shangluo, Nanyang, Zhumadian, Xinyang, Zhoukou, Shangqiu, Qingdao, Weihai, Yantai and Ulanqab. The areas of high ecological-protection importance were mainly distributed in Bayannur, Ordos, Wuzhong, Yulin, Xinzhou, Anyang, Puyang, Dezhou, Jinan, Binzhou and Dongying. General important areas were mainly distributed in Bayannur, Ordos and Hohhot, but there were generally important areas scattered in a large number of cities. The ecological importance level was 3.28% of the entire region of the study area.In 2015, highly ranked locations were scattered throughout the eastern and western halves of the research area, accounting for 55.74% of the entire region. The cities in the middle reaches of the YRB and cities such as Baoyannur, Baotou, and Ulanqab had an obvious decreasing trend. The generally important areas were scattered primarily in the north and northeast, the proportion reached 40.71%, and the overall trend gradually increased from northwest to southeast. The generally graded areas were mainly scattered in the north. Compared with 2010, there are fewer areas of this class scattered across cities. Many cities have reached the level of high ecological importance and above in all parts of the city, and the area of this level accounts for 3.55% of the entire study area.In 2020, the areas of the ecological importance protection of the highest level were significantly reduced, and they were intensively concentrated in the southeast of the study area, accounting for 47.75% of the total area; important protected areas were mainly concentrated in the Inner Mongolia Plateau and scattered in Jining, Dezhou, Binzhou and other cities, accounting for 46.94% of the study area; areas with an importance of higher levels were mainly concentrated in Ordos, Yulin, Bayannaoer and other cities, accounting for 5.31% of the area. Overall, it shows a trend of increasing grade from the northwest to southeast of the YRB, and the extremely important protected areas appeared to agglomerate.(1)Evaluation of production capacityAccording to the entropy method, the production-carrying-capacity index of each city was calculated. Using ArcGIS 10.2 software, Natural Breaks  was used to divide it into Parts I, II, III, IV, and V, from low to high. The higher the level is, the higher the carrying capacity. For the higher level, the temporal and spatial pattern of production carrying capacity of prefecture-level cities in 2010, 2015, and 2020 was drawn .The figure demonstrates that in 2010, the top-level and upper-middle-level urban production-carrying-capacity areas were mostly concentrated in the lower reaches of the YRB. The top-level production-carrying-capacity areas mainly included Ordos, Yulin, Yanan, Zhengzhou, Jining, Dezhou, Linyi, Weifang, Qingdao, and Yantai; the upper-middle-level areas mainly included Baotou, Luliang, Linfen, Pingdingshan, Zhoukou, Liaocheng, Jinan, Liaocheng, Tai’an, Dongying, and Zaozhuang; the intermediate-level production-carrying-capacity areas mainly included Baoyannur, Ulanqab, Hohhot, Taiyuan, Xi’an, Luoyang, Jiaozuo, Xinxiang, Kaifeng, Shangqiu, and Anyang; the lower production-carrying-capacity and lowest-level areas were primarily found in the northeast and southwest of China, where the research region is located; and the lower and lowest-level cities included Xining, Lanzhou, Baiyin, Guyuan, Xinzhou, and Datong. The overall level showed a gradual upwards trend from west to east.In 2015, the upper middle level and top level areas were concentrated in the north-eastern and southern regions of the entire study region, accounting for 38.67% of the area. Top-level cities included Ordos, Shangqiu and Qingdao, the proportion of which was significantly reduced, the upper middle level areas showed expansion, and the urban carrying-capacity level of the lower YRB decreased significantly; the intermediate level was mainly distributed in the northeast, the Central Plains, and the northwest, and, compared with 2010, it showed an expansion trend; the places at the lower and lowest levels were mostly distributed in the upper YRB. The lower level urban areas gradually moved to the north, and the number of lower level urban areas was decreasing.In 2020, the top-level and upper-middle-level areas accounted for 29.39%, and the area proportion decreased significantly. The top-level areas were mainly localized in Ordos, Yulin, and Shangqiu. The distribution of upper-middle-level areas was scattered and decreased; the south-eastern part of the study region was mostly where the intermediate level area was located. Around the middle reaches and downstream of the YRB showed a decreasing trend. The lower and lowest-level areas spread when compared to 2015, and the lower and lowest-level areas expanded. The carrying capacity of urban areas in the YRB’s lower reaches is significantly weakened.(2)Production suitability evaluationThe suitability of production reflects the degree of the suitability of productive activities in territorial space. The evaluation results are divided into grades I, II, and III. The higher the grade is, the higher the suitability.Alternative areas for different levels of suitability were determined according to the level of carrying capacity: an area suitable for the distribution of production space should first have comprehensive resources and environmental conditions for carrying production activities; the higher the carrying capacity of the environmental foundation and the lower the limitation of disaster risk, the higher the suitability of production. According to the production-carrying-capacity grade, the production suitable area and general suitable area of the alternative area were determined. Ensuring that the level of hazard is sufficient to meet basic production needs, areas suitable for the distribution of production space should have a certain level of flatness, and the greater the relief is, the greater the hazard; therefore, the relief was chosen as the correction factor, and the evaluation result is shown in In 2010, the grade-III-production-suitable regions of the study area were concentrated in several cities in the eastern, central, and northern parts, mainly showing agglomeration distribution, accounting for 28.68% of the area. The second-level-production-suitable areas were mainly found in the central and northern regions, showing that the area around the cities of Ordos, Yulin and Yanan accounts for 62.3%. The majority of cities with Grade-I-acceptable areas are located in the western and northern regions, accounting for 11.02% of the area.In 2015, the level-III-production-suitable area included Ordos in the central and northern parts and expanded to the southwest in the south, including Zhengzhou, Kaifeng, Heze, Jinan, Qingdao, Weihai and other cities. The distribution of the level-II-suitable-production areas expanded, and its area proportion was 68.04%. The area expansion was mainly due to the lower suitability grades of Yulin and Yanan and the higher suitability grades of Datong and Weinan. The level-I-suitable areas decreased, and their area accounted for 7.36%, including the cities of Dingxi, Baiyin, Guyuan, and Pingliang.In 2020, the areas suitable for level-III production decreased in the central and southern regions and expanded in the northern regions, accounting for 19.74%. The areas suitable for level-II production expanded to the southeast, while the level of suitability decreased in the south-eastern regions. The areas suitable for level-I production expanded, and the proportion was 11.78%, with some cities, including Datong and Tongchuan, downgrading their suitability for production .(1)Evaluation of living carrying capacityIn 2010, the areas with higher living capacity levels and above were scattered, among which the level-V-carrying-capacity areas included Hanzhong, Ankang, Zhengzhou and Zhoukou, accounting for 5.80%. The level-IV-carrying-capacity cities included Baotou, Erdos, Wuhai, Shizuishan, Yinchuan, Xianyang, Shangluo, Jinan, Dongying, Qingdao, and Yantai, and the area accounted for 19.11%. The concentrated distribution area of level-III living capacity was mainly in the southeast and western Central Plains, including Ulanqab, Hohhot, Yulin, Xining, Xian, Baoji, Tongchuan, Nanyang, Luoyang, Jining, Weifang, Rizhao and other cities, accounting for 29.18%. The lower living capacity and lowest regions were mainly found in the west and around the Central Plains, showing a rise from west to east, including Lanzhou, Baiyin, Zhongwei, Linfen, Jincheng, Anyang, Puyang, Liaocheng, Dezhou, Binzhou and other cities.In 2015, the level-V-capacity-region distribution was scattered, including Yinchuan, Xianyang, Zhengzhou, Zhoukou, and Ulanqab. The level-IV-carrying-capacity area expanded, including Ordos, Wuhai, Ankang, Yantai and other cities. Compared with 2010, the area of level-III living capacity was significantly reduced, accounting for 26.71%. The carrying-capacity zones of level I and level II were mainly found in the western and central plains regions, with the expansion of the lower level area and a reduction in the lowest level area.In 2020, most regions were found in the west and around the Central Plains, with level-V carrying capacity increasing significantly, accounting for 25.75%, and the southwest and east regions included Bayannur, Ordos, Yinchuan, Hanzhong, Ankang, Xianyang, Zhengzhou, Zhoukou, Jinan, Qingdao and Yantai. The level-IV carrying capacity decreased, and was mainly distributed in some cities in Shandong Province. The cities with level-III carrying capacity were further reduced, and the eastern and northern regions are where they are most prevalent. The carrying capacity of level I and level II is reduced. The carrying capacity of level I is not significantly different to that of 2015, while the carrying capacity of grade II is reduced in the southwest .(2)Life suitability evaluationThe evaluation results of living suitability were classified into three main categories: suitable area, general suitable area and unsuitable area .The degree of transportation convenience has a great influence on life suitability, which mainly represents the support and guarantee ability of road transportation facilities for life activities. Therefore, combined with data representativeness and availability, the highway mileage data of 2010, 2015 and 2020 were selected to represent the degree of transportation convenience. The larger the urban highway mileage data are, the higher the degree of transportation convenience.In 2010, the distribution of level-III suitable areas was relatively scattered, including Erdos, Xianyang, Hanzhong, Ankang, Zhengzhou, Zhoukou, Qingdao and Yantai, occupying 23.08% of the research area. The middle and lower parts of the YRB, as well as the northern section, are where most of the level-II-appropriate regions are located, including Hohhot, Baotou, Taiyuan, Xian, Luliang, Linfen, Luoyang, Xinxiang, Heze, Jinan and other cities, accounting for 59.60% of the total area. The level-I-suitable areas are concentrated in the west and scattered in the central and eastern regions, including Xining, Lanzhou, Baiyin, Qingyang, Yanan, Xinzhou, Changzhi, Puyang, Shangqiu, Zibo and other cities, accounting for 23.08%.In 2015, the spatial pattern did not change significantly. The level-III-suitability area increased, and the level of suitability in Ulanqab increased from level-II suitability in 2010 to level-III suitability. The suitable area for level II showed a decreasing trend, accounting for 58.80% of the area. The suitable area of level I shrank to the west as a whole, accounting for 18.24%.In 2020, the level-III-suitability areas were further expanded, with a westward expansion in the Shandong Peninsula urban agglomeration, accounting for 29.16%. The suitable area for level II was further reduced, accounting for 52.60%. The spatial pattern of the level-I-appropriate area remained unchanged.The exploratory spatial data analysis method was used to measure the spatial agglomeration degree of PLES functions with ArcGIS 10.2. The findings revealed that the Moran’s I of ecological function importance was 0.2321, 0.2682 and 0.2042 in 2010, 2015 and 2020, respectively. The Moran’s I of production function suitability was 0.2824, 0.4411 and 0.2824 in 2010, 2015 and 2020, respectively. The ely weak .Due to the possible defects of global autocorrelation in terms of local instability, the Gi* index of urban functional suitability in the YRB was further divided into four types of areas, specifically including hot spots, second hot spots, cold spots, and second cold spots, using the natural breakpoint method through local spatial autocorrelation analysis . The hotThe obstacle degree model was utilized to identify some contributing aspects that influence the suitability of PLES in the YRB territorial space. The calculation results of the obstacle degree of a single indicator of regional ecological importance are shown in In 2010, the average obstacle degrees of biodiversity, significance of water-source conservation, and significance of windbreaks and sand fixation were 26.28%, 20.83% and 14.29%, respectively. In 2015, the average obstacle degrees were 26.53%, 25.01% and 14.31%, respectively. The average obstacle degree in 2020 was 25.84%, 20.29% and 13.94%, respectively, showing a rough trend of decreasing obstacle degree. The obstacle degree of land-desertification sensitivity was 7.82% in 2010, 8.01% in 2015 and 8.2% in 2020, showing a trend of a gradual increase. In the context of global efforts to promote biodiversity conservation, China has achieved remarkable results after decades of efforts. Chinese efforts to protect the environment and the ecological system have improved collaboration, strengthened the protection of grassland and wetland areas in water-conservation ecological function zones, and promoted the improvement of ecological and environmental quality of the YRB. The YRB’s biological surroundings have shown remarkable improvement, but they still face some problems at present. Since 2010, China’s weather and climate have been unusual: precipitation has increased; high temperatures, drought and waterlogging disasters have been connected; cold and hot space and time alternations have been obvious; extreme weather and climate events have occurred; and heavy rainfall has resulted in multiple river-basin floods and frequent geological disasters. Thus, the land-desertification problem in the YRB continues to be serious.The obstacle factors of PLES suitability of each city in the study area in 2010, 2015 and 2020 were calculated using Equations (3) and (4), and the top three obstacle factors were listed, as shown in Among the primary impediment factors affecting the production suitability of cities along the YRB, the frequency of the output of industrial added value per unit of industrial land showed a decreasing trend . The urbanization rate  and the output value of secondary and tertiary industries per unit of construction land  are gradually increasing. The pattern of economic development has also changed from extensive, low-quality and high-quantity to concentrate on the quality of development, continuous improvement of the economic structure, and fast growth of tertiary and secondary industries. The urbanization rate is conducive to guiding the flow of production components that are two-way, such as land, capital and labour, producing an agglomeration scale effect and resource-allocation wealth effect, and then promoting the transformation, development, enhancement and improvement of production capacity, industrial structure and employment structure, which can effectively improve the suitability of production.Through the analysis of occurrence frequency and average obstacle degree, it is discovered that the most typical barrier factors affecting living suitability mainly include total water use per capita , total sewage treatment per capita , and per-capita residential land area . First, the outstanding total water-resource utilization per capita is a barrier factor because the index levels of Xian, Jinan, Qingdao, Zhengzhou, and Yinchuan are significantly better than those of other cities. These cities have a higher level of economic development and need more water resources than other cities. With the expansion of ecological civilization construction, the urban demand for sewage treatment is increasing daily. Especially given the swift economic growth, the increase in sewage-treatment volume can satisfy urban inhabitants’ needs for a green lifestyle. The appearance of per-capita residential land area highlights that the cities along the YRB have a comparatively modest level of urbanization. Shandong and Henan, as provinces with large populations, still need to solve the outstanding problem of the man–land contradiction.The interannual variation in the main obstacle variables was considered, and almost all the top three major obstacle variables of urban living suitability along the YRB in 2010 included the total water use per capita, total sewage treatment per capita, and per-capita residential land area. By 2020, the main barriers remained the same across cities. However, the frequency of total water use per capita and the total amount of sewage treatment per capita decreased, while the frequency of the number of medical practitioners per 1000 population increased rapidly. The country strengthened the conceptualisation of a water-saving and anti-pollution society, which strengthened the Chinese people’s understanding of water conservation and reduces water pollution. However, improving the quality of medical treatment is the main means to maintain the health of all people. The society’s requirement for life is not only to have enough food and clothing but also to seek a high-quality and healthy lifestyle.The ecological importance level of cities along the YRB has increased overall from 2010 to 2015 and established more obvious zoning in geographic space in 2020, mainly because China’s biodiversity conservation work is effective. However, because the YRB spans a large area, there are large differences in resources and the environment, resulting in the formation of obvious zones of ecological importance; from 2010 to 2020, the cities with higher production suitability are mainly distributed in the downstream, mainly because the Guanzhong-Tianshui region, the Hubao-Egyu region, the Lanzhou-Xining region, and the Central Plains Economic Zone have certain scientific and technological innovation capacities and industrial development potential. The optimized development zones, mainly in the Jiaodong Peninsula, the Yellow River Delta region and the provincial central cities in the basin, have strong comprehensive strength and high production suitability; the suitability of life as a whole continually improved from 2010 to 2020, and the higher suitability level is for some provincial capitals and their surrounding cities. The main reason is that these cities are at a higher level than other cities in terms of transportation and economy, and their infrastructure is relatively complete, which greatly improves the suitability for living.In this paper, the technical framework of PLES delineation in the carrying-capacity evaluation of resources and the environment and suitability evaluation of the spatial planning of the YRB was constructed using precise and simple evaluation indexes and quantitative and modelling evaluation methods. The method has the following advantages: (1) In the evaluation method, the traditional method of the carrying-capacity evaluation of resources and environment and suitability evaluation is optimized and quantitative models such as the entropy weight method are introduced to carry out the comprehensive evaluation of multiple elements, which makes the evaluation results more scientific. (2) In the traditional carrying-capacity evaluation of resources and the environment and the suitability evaluation system of ecology, agriculture and towns, a multi-level evaluation system based on the concept of PLE is established, and the relatively defined concept of PLE is implemented into this process through the screening of evaluation indexes. (3) In terms of evaluation indicators, the number of socio-economic indicators are increased, this improves the authenticity and scientificity of evaluation results, and plays a better supporting role in serving the preparation of territorial spatial planning. Limitations are, mainly: (1) the model evaluation method is relatively accurate, but the data processing and quality requirements are high due to the vector or raster data of soil and water resources, environment, temperature and rainfall included in the indicators measuring ecological importance. (2) Some data are not easy to obtain and the accuracy of the data is not high, which makes the evaluation results of average accuracy, and we will try to obtain more accurate data in the future.(1) From 2010 to 2015, overall, the ecological importance of the cities along the YRB rose, but the magnitude was small. In 2020, cities near the YRB experienced a significant change in their ecological importance level, forming a relatively obvious partition in geographical space. From 2010 to 2020, most cities with higher production suitability were spread downstream, and the maximum number of cities with higher production suitability were present in 2015. From 2010 to 2020, the overall living suitability continued to improve, and the higher level of suitability was in some provincial capitals and surrounding cities. (2) Positive spatial autocorrelation of PLES. The agglomeration characteristics of ecological importance and production suitability are strong, while the agglomeration characteristics of life-function suitability are relatively weak. The distribution areas of cold hot spots of the PLES function overlap in space, to some extent. Most of the cold spots are located in the YRB’s higher reaches, and most of the hot spots or second hot spots were spread in the middle and lower reaches in the YRB. (3) From 2010 to 2020, biodiversity, water conservation and windbreak and sand fixation were the primary impediments to the ecological relevance of the YRB. The output of industrial added value per unit of industrial land, the number of industrial enterprises and GDP per capita were the dominant factors affecting the production suitability of the study area. Among the top three major obstacles to urban-living suitability along the YRB, almost all cities experience the per-capita total water-resource utilization, total sewage treatment per capita, and per-capita residential land area. However, the first three barriers are becoming increasingly less obstructive. Based on the above findings, the following recommendations are made:(1) By defining key functional domains, the balanced growth of various functions can be achieved. With the basic idea of ecological civilization running through the whole process, all departments, provinces, and autonomous areas in the YRB must break through administrative restrictions, plan spatial distribution from a strategic level, optimize spatial structure, and achieve spatial balance.According to the different regional features of the different watershed segments, the protection of the main flow corridors and migration channels should be coordinated in all regions. National parks will be established in the Yellow River Delta and other priority areas for biodiversity conservation. In the YRB’s middle and lower reaches, new nature reserves, ecological protection red lines or protected areas will be established, and the connectivity of existing nature reserves will be strengthened.To implement water-source protection using watersheds, the upstream should closely follow the functional orientation of water conservation and strictly follow the principles of sustainability and efficiency to increase water-resource efficiency. Water and soil conservation should be placed in a prominent position in the middle reaches, and high-energy and high-pollution enterprises should be eliminated. Downstream the river ecosystem’s healthy and green development should be enhanced and the sensitivity of the ecological environment reduced.Major projects should be implemented concerning important-ecosystem protection and restoration, establishing ecological space layouts, matching production and living constantly to improve the quality of sandy plantations. Continuing the implementation of major projects, these should focus on regional ecological protection and restoration, accelerating land desertification, salinization, and vegetation restoration in mining management, and strive to build a new development pattern for combating desertification.(2) Upstream key ecological-function areas and main agricultural product-producing areas should focus on environmental protection, develop an ecological economy and industries that can be sustained by resources and the environment according to local conditions, and should link their own characteristics and advantages with the overall industrial layout of the watershed according to the layout of the main functional areas upstream, thus improving the level of upstream production suitability.In view of the different stages of economic development, differentiated development strategies should be adopted. Industrialization in the eastern coastal area started early, and most cities are at an intermediate and advanced degree of industrialization. However, the central and western regions have been responsible for the country’s main agricultural production, and the industrial base is weak. Therefore, we should pay attention to the interaction between regions, promote the coordinated development between regions, and stimulate positive spatial interaction. According to the original industrial characteristics and their own resource advantages, each region should reasonably plan its industrial layout to form a reasonable division of labour and specialization among regions and to form an industrial cooperation mechanism among regions. The eastern region has a relatively higher suitability level and can provide more funds for the central and western regions, advanced technology and high-level talented people. The central and western regions are rich in resources and can deliver more clean energy to the eastern region. The overall suitability level of the YRB will be improved under the joint action of the west and east regions. This should actively guide the reasonable distribution of the population in cities and surrounding areas and actively develop industries in surrounding areas to promote industrial transfer in core areas.The whole area should promote the transformation of the original resource-based industry into the direction of being fine, high-end and clustered, and promote the integration of manufacturing and service industries; large enterprises should be encouraged as the leaders of industrial development, the direction of industrial development should be clarified, and a scale industrial system formed with small- and medium-sized businesses; To strengthen the planning of the clustering of SMEs in the industrial sector, an industrial cluster system should be formed with a reasonable layout, industrial coordination, complete categories and international competitive advantages.(3) The distribution and control of water resources in the upper reaches of the YRB, where there are few water resources, should be performed in accordance with the overall interests of the basin. The overall notion of “reducing agricultural water use, saving water for life, improving ecological water use and ensuring industrial water use” should be adhered to and the limited and scarce water resources exploited. In the middle and lower sections of the YRB, where there are plenty of water resources, within an appropriate range of the resource and ecosystem carrying capacity, water resources should be produced and used. The development philosophy of determining city, land, people and production should be adhered to using water, by refining the total water-use control index in the basin, by improving the water-resources control index system of YRB, and promoting the pattern of allocating water resources. This will continuously strengthen the control of the minimum flow required to maintain the river ecology and environment and implement the unified water-resources management of the main and tributaries of the YRB. To carry out pollution prevention and control, water conservation and control should be comprehensively implemented.In the future, the government should prioritize raising the standard of cheap homes, to achieve a balance between employment and housing, and achieve the goal of “little but comfortable” to ensure the security, peace and comfort of inhabitants. Although the number of medical practitioners per 1,000 population in China is on the rise, there are obvious regional differences. Therefore, to reduce geographical variations in the availability of medical professionals, attention should be paid to the allocation of doctors in remote areas and to improving medical services in remote areas and mountain villages. In recent years, the demand gap of doctors in certain places has been eased to some extent, but it is a long way from the intended goal, which makes it challenging to meet the need, and the supply–demand disparity will continue for a very long time. In the future, it is necessary to implement the Opinions, strengthen the education of general-practice personnel, and actually succeed in the aim of “full coverage, filling up the weak points, improving the level and ensuring the quality”.


Emergency information release during public health emergencies is a governance measure to slow down the spread of the epidemic and guide the public in scientific protection. Because of the uncertainty and life-cycle characteristics of public health emergencies, emergency information release represents the process of time dynamics. At present, it is an inevitable trend to establish a collaborative mechanism for emergency information release of public health emergencies to improve the release efficiency and respond to public demand. To determine time evolution characteristics of organizational collaboration in emergency information release, this study took the response to COVID-19 from the central government of China as an example and conducted research based on social network analysis. Based on information from COVID-19-related press conferences held by China's central government, the emergency information release collaborative networks (EIRCNs), and Emergency Organizations-Emergency Information Release Matters (EOs-EIRMs) 2-mode network were constructed. With the time evolution, the tightness, convergence, stability, and connectivity of EIRCNs in public health emergencies presented the process of lowering and then raising. At different stages, the core emergency organization (EO) nodes in EIRCNs continued to maintain a certain degree of activity. Their dynamic processes showed the characteristics of diversification rather than homogeneity. The time evolution of emergency information release matters (EIRMs) reflected the dynamic adjustment of the government's prevention and control measures and responded to the diversification of the public's understanding and protection needs during different stages of the COVID-19 pandemic. The study further examined the driving factors and implementation mechanism of the time evolution characteristics of the collaborative mode of emergency information release. The implementation of EIRMs at different stages had different resource requirements, which were usually achieved by introducing new EOs (Adding resource increment) or increasing the collaborative frequencies among EOs (Activating resource stock). In addition, further research prospects and feasibility interpretation were proposed. The COVID-19 pandemic is the most extensive pandemic afflicting humanity in a century, which is a typical public health emergency , 3. PublDue to the coupling and evolution of the social environment and risk types, the emergency management of public health emergencies also presents complexity and systematicness , 10. CorAt the present stage, to clearly explain and describe the interaction among various functional organizations in emergency management, the study of emergency management networks has been widely concerned by the academic community . NetworkThe remainder of this study is organized as follows: Section Literature review discusses and summarizes the existing studies on emergency information release and emergency management networks and puts forward the feasibility and necessity of this study. Section Methodology and data introduces the research framework and introduces the data source, the proposed analysis method, and key indicators. Section Results presents the main analysis results of this study, including the stage characteristics of the structures of EIRCNs, the attribute characteristics of EO nodes, and the affiliation relations between EOs and EIRMs. In Section Discussion, time evolution characteristics of the collaborative mode of emergency information release are summarized from a system perspective, and influencing factors of network evolution are further interpreted. Section Conclusions and recommendations summarizes the conclusions, contributions, and limitations of the study, as well as discusses the research implications.As its main aim is to support disaster emergency response, maintain social and public stability, and mitigate the loss of disasters, emergency information release has always been a vital issue in academic and governance practice. Meanwhile, with the rapid development of mobile communication, internet, social media, and other emerging media , 18, socEmergency information can be divided into data information about emergencies, emergency response measures taken by the government and relevant departments, emergency protection measures taken by the public, and public service information related to emergencies . The emeResearch on emergency information release belongs to the interdisciplinary research content, involving journalism and communication science, management science, and security science. In the research of emergency information release strategy, existing studies have discussed the effect of implementing emergency information release relying on different release channels and media types, such as press conferences, social media, and information release platforms. It is also essential to pay attention to the difference between the subject and object of emergency information release and consider the particularity of public behavior, such as public response and public adaptability. The mechanism of emergency information release is the core foundation to support the implementation of emergency information release. In the research of emergency information release mechanisms, relevant research is usually conducted from the practical and theoretical levels. On the one hand, the characteristics and practical problems of the current mechanism are discussed and summarized around the system design and policy arrangement of emergency information release. On the other hand, the theoretical frameworks of emergency information release mechanisms are designed based on governance theory, integral government theory, and collaborative theory. There are also studies on the mechanism of emergency information release in different types of emergencies and levels of government departments.To more specifically identify the types and scale of organizations involved in emergency management and explain the interaction relationships of EOs during emergency response, scholars have published several theoretical and case study results about the emergency management network , 27. A nFor the types of research objects, the research of emergency management networks can be divided and summarized according to the types of emergencies, such as earthquakes, floods, hurricanes, forest fires, explosion accidents, public health emergencies, and terrorist attacks. Du et al.  analyzedFor the construction methods of network construction, the academic circles primarily use texts of emergency response plans, actual case reports, and policy texts as data sources to identify types of organizations participating in emergency management. They construct interaction relationships between emergency response organizations to analyze constructing emergency management networks. By analyzing the interaction between EOs, the emergency management network can be constructed. Fan et al.  took govFor the research design, the network structure and node attributes have always been critical analysis perspectives of the emergency management network. Among them, network structure characteristics can effectively reflect the scale of cooperation and evaluate the cooperation performance of EOs , 35, wheIn addition, based on life cycle characteristics of emergency management, the emergency management network shows certain differences in different stages, which are regarded as the dynamic characteristics of the network . On the Emergency collaboration is essential to improve the performance of emergency management . As a viFighting COVID-19 China in Action White Paper and divided the collaboration process of emergency information release of COVID-19 into five stages  Network density refers to the ratio of the number of connections between nodes in the network to the maximum number of connections. The more the connections between nodes, the higher is the network density in the same network scale. When the number of links among nodes is consistent, increasing the network scale will lead to a decrease in network density. For EIRCNs, the higher the network density was, the closer were the collaborative relationships between EOs. In contrast, when the network density is lower, the connections among EOs are less and the network structure is closer.(2) By combing the data of collaborative relationships among EOs obtained in this study, the collaborative relationships between EOs were affected by association frequency. Therefore, EIRCNs were the multi-valued network. The previous concept of network density could not consider the scale of communication and the actual frequencies of connections. The concepts of collaborative depth and relative network density were introduced to further identify the actual connection strength and tightness among EOs in the network. The collaborative depth is defined as the ratio of the actual collaborative frequencies and the number of connections among nodes. Relative network density is defined as the ratio of the actual collaborative frequencies and the maximum number of network connections. In other words, the value of relative network density is equal to the network density multiplied by the collaborative depth.(3) Network centralization is the measure to verify whether there are core nodes in the network, which can measure the clustering trend of EIRCNs to one or more core EO nodes. In this study, degree centralization was chosen as the representative for analysis, which was equal in value to the ratio of the total of the actual difference and the total of the maximum difference of the network node degree centrality. When EIRCNs have high network centralization, the network structure generally has core-periphery characteristics. In contrast, when the network centralization of EIRCNs is low, there is no obvious core EO node, and the network structure shows balanced characteristics on the whole.(4) Network cohesion could describe the dependence of the overall network on core nodes and the balance of network structure. When the network cohesion of EIRCN was low, there was inequality among EOs in the network. The network stability was poor and tended to the dispatching structure. Meanwhile, the power and information of the whole network were relatively concentrated. In contrast, when the network cohesion of EIRCN was high, the network had a uniform structure and was not affected by individual nodes. Resources and information in EIRCN were relatively scattered.(5) Average path length refers to the average associated distance among the EOs in EIRCN, which can express the strength of network connectivity. When the average path length of EIRCN is higher, the communication distance among EOs in the network is longer and the cost of resource transmission is higher. In contrast, when the average path length of EIRCN is lower, the connectivity of the overall network is better. The communication among EOs are not easily affected by other factors, and the costs of forming cooperative relationship are low.Node attribute analysis was used to describe the role of EOs in EIRCNs. In this study, degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality were selected to analyze EO nodes in different stages. The study intuitively and quantitatively expressed the position and measured the importance of EOs in EIRCNs in terms of numerical size and ranking. When the network scale is different, the centrality in various graphs is not comparable. Therefore, this study took normalized centrality as the analysis index.Degree centrality refers to the number of other nodes directly connected to the EO node. In a directed network, degree centrality can be divided into in-degree and out-degree centrality. When the degree centrality of the EO node is larger, it indicates that the node is in the center of the EIRCN and has more network power.Betweenness centrality is a measure of the extent to which the EO node is located in the middle of other pairs of nodes. Betweenness centrality can express the control of the EO node on the network resources and the interaction among nodes in EIRCN. When the betweenness centrality of the EO node is higher, it indicates that the EO is on more geodesic pairs of nodes and has mastered the transmission mode of information and resources of EIRCN.Closeness centrality is the indicator to measure to what extent the EO node is free from the control of other nodes. Closeness centrality is the reciprocal of the average distance between the EO node and all other nodes in the EIRCN. Numerically, when the closeness centrality of the EO node is smaller, it indicates that the node is not the core point of EIRCN. When the closeness centrality of EO node is larger, the node is stronger regarding information resources, power, prestige, and influence.Eigenvector centrality measures the importance of the EO node by comparing the centrality of adjacent nodes. When the EO node has neighboring nodes with strong influence, the eigenvector centrality of the EO node is larger. The eigenvector centrality focuses on the analysis of connection quality rather than the connection quantity of the EO node. The EO node with a larger eigenvector centrality has a higher potential value.To analyze time evolution characteristics of network structure, the EIRCNs of Stage II-Stage V were plotted in sequence see . Table 3From the perspective of basic network attributes, the EIRCNs of Stages II to V contained 31, 55, 45, and 63 EO nodes, respectively. Meanwhile, links between EO nodes in EIRCNs were 101, 167, 132, and 223 in sequence. Accordingly, the network densities of the EIRCNs of Stages II to V were 21.72%, 11.25%, 13.33%, and 11.42%, respectively. From the perspective of the actual collaborative frequencies, 175, 210, 226, and 502 pairs of collaborative relationships were formed among the EOs in Stages II to V, respectively. Correspondingly, both the relative network density and the collaborative depth tended to decline and then rise. This was because in the early stages of the COVID-19 pandemic prevention and control, EOs needed to make immediate emergency response measures to contain the development of the epidemic. Furthermore, because of the lack of familiarity with the characteristics of COVID-19, various EOs often participated in the emergency information release together, forming relatively intensive relationships. Therefore, EIRCN had the highest relative network density (37.63%) at Stage II. When entering Stage III, the number of EOs implementing emergency information release increased, and the network scale of EIRCN expanded rapidly. This reduced the relative network density and collaborative depth of EIRCN to a certain extent but also provided climbing space for developing collaboration between EOs in EIRCN. In Stages IV and V, the relative network density and collaborative depth of EIRCN rebounded as the function division of EOs became clearer and the communication between EOs became closer. The EIRCN of Stage V had the closest connection among EOs and the largest collaborative depth (2.251).From Stage IV to V, the network centralization of the EIRCN increased gradually. Network morphology tended to have a star structure. In the early stage of COVID-19 prevention and control, the distribution of emergency information released by EOs was not clear, and the core nodes of the EIRCN were not exact. With the advance in COVID-19 prevention and control, the types and locations of core nodes in EIRCN became gradually clear, and the network as a whole had a more significant clustering trend toward core nodes. In Stage V, EIRCN had the largest network centralization, which was 91.49%. There was little difference in network cohesion at different stages, indicating that the EIRCN structure and resource allocation tended to be stable. Specifically, network cohesion decreased slightly after the transition from Stage II to III, which was related to the increase in the types and number of EOs. However, over time, the network cohesion of the EIRCN returned to a more balanced state. Contrary to the trend of relative network density and collaborative depth, the average path length of EIRCN showed the evolution path of ascending and then descending. In Stage III, the average path length of EIRCN was the largest (2.013). This indicated that with more EOs, the communication distance and the coordination costs in EIRCN increased. Furthermore, the collaborative efficiency of EOs recovered significantly in Stages IV and V.To sum up, with the increasing tasks for COVID-19 prevention and control, the need for EIRCN for emergency resources also deepened. The network size and the collaborative frequencies of EIRCNs showed an increasing trend. Correspondingly, EIRCN had lower relative network density, collaborative depth, network cohesion, and higher average path length at Stage III. However, with the time evolution, the division of functions was gradually clarified, and the collaborative relationships were deepened. As a result, the network tightness, convergence, stability, and connectivity of EIRCNs gradually enhanced.In Stage II, EO nodes with a high degree of centrality mainly included epidemic prevention and control departments , news and publicity department (SCIO), transportation departments , and policy support departments . In the face of the urgency and uncertainty of the COVID-19 pandemic, the priority of the emergency tasks in the early stage of epidemic prevention and control was stability. Consequently, the types of core EO nodes in EIRCN were relatively concentrated. In Stage III, NHC, SCIO, and MT were still the top 3 EO nodes with degree centrality. Meanwhile, market management and financial service departments , science technology and medical material departments , and public safety department (MPS) became the core organizations in this stage because of the needs of businesses returning to work, vaccine research and development, and social order maintenance. In Stage IV, the epidemic situation in China was under control. In contrast, the pandemic abroad spread rapidly, and, accordingly, the customs, immigration management departments , and civil aviation transportation departments  had a high degree of centrality. The Chinese government promoted the resumption of work and production to improve the speed and expand the scope. The functional departments such as commerce, finance, and development  were at the core of the network. In Stage V, the prevention and control of key time points and critical locations became a vital issue of concern to the government. Therefore, the tourism departments (MCT), education departments (ME), and transportation departments  were closer to the core of the network. SRGJRCM conducted multiple reports on COVID-19 vaccine research and public vaccination, which had a high degree of centrality.Among EIRCNs at different stages, the top 10 EO nodes with normalized betweenness centrality are shown in In Stage II, NHC, SCIO, and MT were the top 3 EO nodes with betweenness centrality, indicating that they had more power and control f more resources in EIRCN. Moreover, the types of EOs with high betweenness centrality were mainly livelihood guarantee departments  and financial support departments . This indicated that the implementation of various emergency tasks in the early stage of epidemic prevention and control could be separated from social and financial resources. In Stage III, NHC, SCIO, and MT still had the highest betweenness centrality, which has a similar trend with degree centrality analysis. During this stage, as the governments needed to take various measures to support the resumption of work, production, and industry, it had great demand and dependence on social order and market order. Therefore, security and legal protection departments  and marketing and industry management departments  occupied more geodesic pairs of nodes in the network. In Stage IV, the spread of the epidemic in China was basically blocked. Consolidating the effectiveness of epidemic prevention and control and orderly recovery of economic and social development became the main theme of emergency information release during this stage. Therefore, in addition to finance  and development (NDRC) departments, MARA, MC, and MIIT also provided corresponding governance resources for industrial production, employment security, and rural epidemic prevention and control. This provided the mediating effect for communication and coordination between EOs in EIRCN. In Stage V, the national epidemic prevention and control entered Ongoing Prevention and Control. The epidemic data, the introduction of experience, and emergency science popularization were vital contents of emergency information release. Therefore, NHC, CCDCP, and SCIO had a high betweenness centrality. Furthermore, departments in charge of key prevention and control locations, such as transportation, tourism, education, and customs, had more resources on the network. This also indicated that the emergency information release in this stage needed more support from heterogeneous EO and diversified governance resources.In Stage II, the top 10 EO nodes with closeness centrality and betweenness centrality were identical in type. However, the top 10 EO nodes with closeness centrality were different in order. This indicated that EO nodes on the geodesic pair of nodes had a higher likelihood of establishing connections with other nodes. More precisely, EO nodes with more network resources in EIRCN are often not controlled by other nodes. Compared with Stage II, in Stage III and Stage IV EIRCNs, six new types of EO nodes were added to the top 10 EO sets, respectively. EIRCN of Stage III corresponded to expanding the EO nodes, including MPS, SAMR, MST, MJ, NMPA, and ME. In the EIRCN of Stage IV, MC, MARA, GAC, MEE, SPB, and SAMR were the newly added top 10 EO nodes. This showed that with the abundance of EIRMs and the expansion of EIRCN, the EO sets that were not easily controlled by other nodes in the network vary greatly. In Stage V, different from the previous two stages, the top 10 EO nodes did not increase significantly. However, it showed similar integration characteristics with previous core EOs, including NHC, SCIO, MT, CCDCP, MIIT, SAMR, ME, MC, GAC, and MCT. NHC, SCIO, MT, CCDCP, and MIIT are the core EOs formed by EIRCN of Stage II. SAMR and ME were the key EOs formed by EIRCN of Stage III. MC and GAC were highly influential EOs formed by EIRCN of Stage IV. This indicated that with the advance in epidemic prevention and control and the development of time, types of EO nodes with high reputation and core influence tend to be stable with the network layout of EIRCN.The top 10 EO nodes of normalized eigenvector centrality in EIRCNs at different stages are identified, as shown in Comparing To analyze differences of EIRMs at different stages and study time evolution characteristics of affiliation relations, the EOs-EIRMs 2-mode networks of Stage II-Stage V were drawn see . In addiMoreover, the same EIRM was issued by different types and numbers of EOs at different stages. With the development of COVID-19 prevention and control, emergency information release had different core issues and EIRMs. In Stage II, the contents of emergency information release mainly included diversified safeguard measures, production of emergency and medical supplies, and epidemic prevention and control of key population groups. Measures for social services and security, including transport services and daily supplies, were the most important, effectively addressing and ensuring people's livelihood during the COVID-19 outbreak. At this stage, the government introduced several financial support, tax relief, and other policies. EIRM7, EIRM10, EIRM5, EIRM12, and EIRM14 (EIRM15 tied) are the top 5 EIRMs with a degree centrality of Stage II.In Stage III, EIRM7 is still the core topic of emergency information release. Specifically, the top 5 EIRMs are EIRM7, EIRM9, EIRM3, EIRM6, and EIRM8. EIRM17 appeared for the first time in EOs–EIRMs 2-mode networks, indicating that COVID-19 became a global pandemic. At this stage, the Chinese government issued several policies on epidemic prevention and control while ensuring economic and social development. Policy publicity and implementation was the key link of emergency information release during this period. Meanwhile, the EOs held press conferences on maintaining market order, resuming work and production, and strengthening employment security. During this period, the heterogeneity of EOs increased. This was related to emergency information release focusing on the contents of emergency science popularization.After the transition from Stage III to IV, the core EIRMs in EOs–EIRMs 2-mode networks were EIRM7, EIRM8, EIRM9, EIRM3, and EIRM14. During this period, as the spread of COVID-19 was effectively controlled in China, the central government shifted the focus of COVID-19 prevention and control to key places, units, and groups, such as rural areas schools, and the aged. The Chinese government determined the strategy of preventing input from outside and rebounding from inside and held several press conferences focusing on the theme of prevention and control measures for imported cases. Meanwhile, the EOs repeatedly briefed the public on the progress of research and development of COVID-19 vaccines. Moreover, the Chinese government made active arrangements for international medical cooperation, export of medical supplies, and international cargo transportation and showed substantial care and concern for Chinese citizens abroad.In Stage V, cases in all parts of China were under control, and the emergency response level was successively lowered. This indicated that COVID-19 prevention and control entered a phase of Ongoing Prevention and Control. The core focus of the emergency information release shifted to EIRM4, EIRM1, EIRM7, EIRM3, and EIRM16. During this period, the Joint Epidemic Prevention and Control Mechanism of The State Council invited EOs of different levels, types, and fields to share their experience and knowledge of epidemic prevention and control. On the eve of every holiday and other key time points, the epidemic prevention and control work was arranged around transportation, tourism, and other issues. During the period of Ongoing Prevention and Control, scientific research departments, medical management departments, and pharmaceutical companies regularly introduced COVID-19 vaccination to the society. In addition, in the face of the COVID-19 global pandemic, customs departments, immigration departments, civil aviation departments, and logistics departments focused on risk prevention and cold chain supervision of imported goods.The above results describe the network structure of EIRCNs, the attributes of EOs, and the affiliation characteristics of EOs-EIRMs in different stages of COVID-19 prevention and control. From a system perspective, it is necessary to further discuss the time evolution characteristics of EIRCNs and EOs–EIRMs 2-mode networks.(1) The network structure of EIRCNs was time evolution because of the differences in network size and the number of collaborative relationships between EO nodes in different stages. First, the relative network density and collaborative depth of EIRCNs showed the trend of decreasing first and then rising, which was formed by the expansion of the EIRCN's network scale and the gradual tightening of the collaborative relationships. Second, the type and location of core nodes in EIRCN were gradually observable because of the clear division of functions of EOs in the EIRMs. Therefore, the network centralization of EIRCNs showed the process of increasing over time. Third, there was little difference in EIRCN network cohesion at different stages. In comparison, Stage III had the lowest network cohesion. Fourth, the average path length of EIRCNs followed the evolution path of increasing first and then decreasing because the network expansion led to an increase in coordination and communication distance, which raised the collaborative costs. Furthermore, network connectivity recovered with the continuous improvement of identity among EOs. From Stage II to III, the relative network density, collaborative depth, and network cohesion decreased, and the average path length increased. With the advance in epidemic prevention and control, the network tightness, convergence, stability, and connectivity of EIRCN were gradually enhanced after Stage III.(2) Different stages of emergency information release of public health emergencies had different governance resource requirements. Therefore, the types and locations of EOs in EIRCN evolved over time. On the one hand, with the deepening of the understanding of COVID-19 prevention and control, many industry and functional support departments joined. Meanwhile, different from the trend of gradual withdrawal of core organizations in the emergency management network, the core EOs in EIRCN continued to maintain a certain degree of activity and played a key role in all stages of epidemic prevention and control. Although the spread of COVID-19 was under overall control, it still showed the trend of sporadic outbreaks. Therefore, the core departments needed to undertake certain emergency management responsibilities and emergency information release at different times. On the other hand, because of the differences between emergency information release and emergency disposal, emergency rescue, and emergency response, core EOs in EIRCN did not show a homogenization trend compared with previous studies. This is because the emergency information in a certain field or direction can only be published by a single EO rather than by multiple EOs in the same field. Therefore, in the EIRCN of public health emergencies, the core EO nodes showed a diversified evolution trend of diversification.(3) As the key issues and focus of emergency information release were closely related to the focus of epidemic prevention and control, the types of core EO and key EIRM nodes in the “Eos-EIRMs” 2-mode network at different stages evolved dynamically. In Stage II, the emergency information release mainly focused on epidemic prevention and control, social security, a guarantee of medical supplies, and key population groups prevention. Among them, social security included transportation, financial, and livelihood support. The guarantee of medical supplies included drug research and the production and transportation of medical supplies. In Stage III, with the initial control of the spread of the epidemic, the measures for businesses returning to work based on maintaining social stability were the vital issue concerned by emergency information release. It included the measures related to maintaining market order and stability, industrial chain recovery, industry support, employment of key groups, and fiscal and tax support. In Stage IV, concentrating on possible routes of transmission of COVID-19 was the focus of emergency management. Correspondingly, the emergency information release focused on prevention and control measures for key locations, units, and population groups. Meanwhile, because of the global spread of COVID-19, focusing on preventing and controlling imported cases also became the core content of emergency information release. In Stage V, with the ongoing prevention and control in China, the emergency information release focused on holidays, school opening, travel, transportation, and other issues. The development and vaccination of COVID-19 vaccines, imported cases, imported cold chain, and other conditions also received corresponding attention. To sum up, the dynamic adjustment of emergency information release reflected not only the government's prevention and control measures but also an intuitive response to the public's safety and protection needs.We obtained the time dynamic law of EIRCNs and EOs–EIRMs 2-mode networks. We now further discuss the factors that influence network evolution in different stages. In the emergency information release, all EOs needed to pay related network resources to realize the dissemination of EIRMs. By analyzing the interaction between different EIRM nodes, resource sharing in emergency information release can be concluded from the resource and information flow perspectives. Based on the affiliation data of EOs–EIRMs 2-mode networks, the minimums method was adopted to transform 2-mode networks to obtain the resource flow between EIRM nodes. On this basis, the chord diagrams of the resource sharing relationship of EIRMs in Stages II to V were drawn see . The widComparing Stages II and III, we find that although Stage III had many EOs, the resource sharing amount of EIRMs nodes is similar. This indicated that the high collaborative frequencies and the adequacy of interaction between EOs are also crucial ways to meet the resource requirements of EIRMs. In conclusion, different types of EIRMs at different stages and the different resource requirements of EIRMs for EOs promoted network evolution to a certain extent. Similarly, the driving forces for the time evolution of EIRCNs were similar, characterized by introducing new EOs or increasing the collaborative frequencies. Among them, introducing new EOs could improve the resource increment of EIRCNs, and the increase of the collaborative frequencies could tap the resource stock of EIRCNs.Emergency information release during public health emergencies is a vital part of emergency management, which involves every stage of the emergency management life cycle. As the responsible public health emergency management subject, the government connected various types of EOs in emergency information release to integrate the emergency resources of different fields and attributes. The contents of emergency information were different in different stages of public health emergencies. Therefore, various EOs also formed differentiated collaborative relationships around different emergency information issues. EIRCNs and EOs–EIRMs 2-mode networks were constructed in this study by taking the response to COVID-19 from China's central government as an example. We aimed to systematically and clearly describe the coordination and interaction between EOs in emergency information release of public health emergencies. The network structure of EIRCNs and the interaction among Eos, the attributes of Eos in EIRCNs, and the relations among affiliation nodes in Eos–EIRMs 2-mode networks at different stages were analyzed. Moreover, the time evolution characteristics of the EIRCNs in public health emergencies were summarized. The research concludes the following:(1) Comparing EIRCNs at different stages, we conclude that from Stage II to III, relative network density, collaborative depth, and network cohesion increased. In Stages IV and V, the network tightness, convergence, stability, and connectivity of EIRCNs are gradually enhanced.(2) With the advance in COVID-19 prevention and control, the network scale of EIRCN expanded, and the types of core organizations in the network have changed constantly because of the expanding demand for emergency resources. Over time, the identified core EOs was expected to continue to maintain a certain degree of activity in the next stage rather than withdraw from the network. Moreover, the time evolution of the core EO nodes in EIRCNs showed the trend of diversification rather than homogeneity.(3) The key issues and focus of emergency information release were closely related to the focus of epidemic prevention and control. The types of EIRM nodes in the network and their association with core EO nodes dynamically evolved. The time evolution of EIRMs in public health emergencies reflected the dynamic adjustment of the government's epidemic prevention and control measures. This also responded to the diversification of the public's understanding and protection against COVID-19 at different stages.(4) From the time evolution characteristics of the total amount of shared resources between EIRM nodes, the driving force of the time evolution of EIRCNs was the introduction of new EOs or the increase of the collaborative frequencies between EOs. The introduction of new EOs could improve the resource increment of EIRCNs, and the increased collaborative frequencies among EOs could activate the resource stock of EIRCNs.This study presents the following implications from two aspects: improving the collaborative mechanism of emergency information release and dynamically adjusting the key issues of emergency information release.(1) Improve and optimize the collaborative mechanism of emergency information release from two aspects of the overall structure and local arrangement. Regarding the overall structure, institutional arrangements such as the compilation of emergency plans, function allocation, and coordination mechanism should be strengthened to improve the standardization and feasibility of organizational collaboration in emergency information release. The emergency information release policy should be updated and improved promptly. Regarding local arrangements, heterogeneous EOs should be introduced to provide diversified governance resources according to the development law of public health emergencies and the characteristics of emergency management needs. The organization foundation and trust foundation formed in emergency information release should be fully explored to avoid or reduce the repeated allocation of resources.(2) Key topics of emergency information release should match epidemic prevention, control practices, and public safety needs. In the emergency information release of public health emergencies, EIRMs showed the time evolution feature, which revealed a high correlation with epidemic prevention and control practices. The timely and accurate release of data on public health emergencies and emergency response measures can help improve the authority and credibility of the governments and relevant departments. Furthermore, in the face of public health emergencies, the public's cognition demand for safety gradually expands. It is essential for relevant departments to timely interpret the concerning issues of public concern, such as emergency protective measures and public service information related to public health emergencies.This study analyzed the collaborative mode in emergency information release from a network perspective, which is an attempt in the cross-research field of emergency management and government information release. It clarified the difference and time evolution characteristics of the organizations and contents of emergency information release in different emergency stages. This study is the first to combine the emergency information release with the network analysis method, which enriches the research on the emergency management networks. However, this study is not without limitations. The research data were mainly based on the response of the Chinese central government to the COVID-19 epidemic. There are significant differences between the central government and the local government in the authority and mode of emergency information release. Therefore, it is necessary for future research to focus on the emergency information release of the local government and clarify the difference. In addition, because of the ongoing COVID-19 pandemic, the characteristics of emergency information release in Stage V still have some extensibility. Furthermore, the collaborative mechanism of emergency information release of multiple subjects such as the governments, experts and scholars, and opinion leaders of social platforms could be used as the main content of the next research to enrich the theoretical connotation of emergency collaboration continuously.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This study was supported by the National Natural Science Foundation of China (72174044).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Malaria, one of the deadliest infectious diseases, has been reported to infect the Italian people since the first century . AlthougAccording to the WHO, Europe is free of indigenous malaria cases . MalariaAnopheles atroparvus is one of the dominant potential vectors of malaria in Europe  and their association with the movement of population  from malaria-endemic regions to Europe may increase the risk of local transmission and re-emergence of malaria in Europe .The 2019 EU Epidemiological Report on Malaria reported 8,641 cases of malaria, with nine confirmed cases reported as locally acquired  . Vector-Indeed, the high flow of travelers, the current migration crisis, and the presence of malaria vectors in countries, such as Belarus, Bulgaria, France, Greece, Italy, Portugal, Spain, and the Republic of Moldova, would increase the risk of malaria resurgence in these countries , 18–24. P. falciparum and P. vivax to thrive are reached and run between April and October in southern Europe .Beyond the risk of malaria resurgence or re-emergence in Europe, the increasing number of imported malaria cases in Europe challenges us and shows that malaria is an urgent problem not only in Africa, India, Asia, Latin America, etc., but also in European countries.AV and JD conceptualized and wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In recent years, the new economy has entered a phase of rapid development and upgrading China's service consumption is driving the continuous optimization of the population's consumption structure. To realize the rationalization of the Chinese household consumption structure, the ELES model is used to analyze the structure system of Chinese household consumption expenditure. This article constructs the ELES model, divides the types of Chinese household consumption expenditure structure systems, establishes consumption expenditure function, analyzes the influencing factors of the consumption expenditure structure system, and obtains the analysis results from static and dynamic aspects. Based on the statistics of Chinese household consumption expenditure data in recent years, this article obtains the analysis results of the consumption expenditure structure system: the basic consumption demand and marginal consumption tendency of food are in the first place, and the consumption expenditure structure system has gradually changed into the development-type and enjoyment-type consumption mode. Through increasing the income of rural residents, guiding reasonable consumption concept, optimizing consumption environment, and so on, we can promote the proposal and implementation of the optimization of China's household consumption expenditure structure system to improve the rationalization of China's household consumption structure system. Consumption is not only the end of social reproduction, but also the starting point of its logic. It is also one of the three main driving forces of economic growth and plays an important role in economic growth. Consumption structure is one of the key research contents in the field of current economics. It can not only reflect the living standard and quality of residents, but also reflect the development of economy. It is a very important index in economic research. The consumption structure is affected by many comprehensive factors, which in turn affect the development of these factors . ThroughAt this stage, most foreign scholars are more committed to the construction and application of demand function model in the study of residents' consumption structure, which brings a lot of innovation in research methods. At the same time, the entry point of foreign scholars' research on the consumption structure is novel and diverse. The research results of foreign scholars on consumption function and consumption system model have laid a solid theoretical and model foundation for the research on China's household consumption expenditure structure. However, most of the domestic scholars introduce new variables to innovate based on the existing models to inject new vitality into the research of consumption structure. Based on the research results at home and abroad, we can find that the AIDS model and Panel Data are mostly used in the research of the consumption structure, while VAR model, factor analysis, cluster analysis, and grey correlation analysis are mostly used in the research of influencing factors of the consumption structure. However, these methods can only rank the influencing factors of the consumption structure, so the analysis results of the consumption expenditure system are one-sided, so the ELES model is introduced.The ELES model is an extended linear expenditure system model, which assumes that people's demand for various goods in a certain period depends on people's income and the price of various goods. Moreover, people's demand for all kinds of goods is divided into two parts—basic demand and demand beyond basic demand, and they think that basic demand has nothing to do with income level. After the basic demand is met, residents arrange various nonbasic consumption expenditures according to certain marginal consumption tendency . In the t as the education consumption expenditure and then adds the expenditure of cultural and entertainment supplies to other expenditures, to properly process the statistical data of the National Bureau of Statistics . Then, the form of consumption function is as follows:The consumption function is mainly used to measure the relationship between consumption and income. Assuming that consumption is mainly determined by permanent income, permanent income is the expected long-term income. The persistent income hypothesis suggests that rational consumers make consumption decisions to maximize their effects, not based on temporary income in the present period. Instead, consumption decisions are based on the level of income that can be maintained long-term, that is, the level of lasting income. The life cycle hypothesis relates consumption to lifetime income and property. It assumes that consumers are rational and able to use their income sensibly and consume. The only goal of consumer behavior is to maximize utility. The life cycle and persistent income hypotheses are highly similar, emphasizing that consumption is mainly determined by future income .There are many factors affecting the consumption structure of urban residents, which can be divided into macro- and microfactors, such as industrial structure, consumer prices, and family population; they can also be divided into economic and noneconomic factors, such as income, industrial structure, and social and cultural, scientific, and technological progress . These fUnder the ELES model, through the analysis of the influencing factors and components of China's household consumption expenditure structure system, the final analysis results of China's household consumption expenditure structure system are obtained from the static and dynamic aspects, respectively, and the basic demand and marginal consumption tendency of China's household consumption expenditure structure system are shown in the results.According to the definition of the consumption structure in the Statistical Yearbook, consumption expenditure is divided into food consumption, clothing consumption, residential consumption, household equipment and services consumption, medical and health care consumption, transportation and communication consumption, education, culture and entertainment services consumption, and other goods and services consumption, a total of eight consumption expenditures . This clThe internal structure of household goods and services consumption expenditure is shown in On this basis, the analysis results of the Chinese household consumption expenditure structure system are obtained from static and dynamic aspects.Consumption level refers to the scale and level of life consumption and services used by individual consumers or the whole society in a certain period. The level of consumption is determined by social development and people's living standards . AccordiIt can be seen from Marginal propensity to consume refers to the proportion of increased consumption in increased income. According to the annual per capita consumption expenditure, per capita disposable income, increased consumption, and income of Chinese households from 2016 to 2020, the value of marginal propensity to consume is calculated, as shown in According to the law of diminishing marginal propensity to consume, although people's consumption increases with the increase in income, the part used to increase consumption in the increased income is less and less. From 2016 to 2020, China's household marginal propensity to consume is within the limit of marginal propensity to consume (between 0 and 1), basically fluctuates between 0.5 and 1, and does not show an obvious decreasing trend, which does not conform to the law of diminishing marginal propensity to consume, indicating that China's household consumption confidence is insufficient . In 2018The change degree of consumption structure measures the change degree of residents' consumption structure every year. The larger the calculated value is, the greater the change range of residents' consumption structure in this period is. Combined with the consumption expenditure data of China in recent years, Chinese families are divided into three stages: 1997–1999, 2000–2013, and 2014–2020. These three stages correspond to the well-off, affluent, and richest life of Chinese families in turn. The specific statistics of structural change are shown in From the calculation results in According to the calculation principle of demand income elasticity, we can calculate the demand income elasticity of Chinese households' consumption expenditure. The demand income elasticity of food, clothing, housing, family equipment, medical treatment, transportation, culture and education, and other items are 0.763, 0.889, 0.958, 0.696, 0.721, 1.119, 0.648, and 0.842 respectively. Therefore, the demand income elasticity of residents' consumption is positive, which indicates that with the increase in disposable income, residents' consumption expenditure will increase accordingly.Based on the analysis of basic consumption demand, marginal consumption tendency, and demand income elasticity of the ELES model, the basic consumption demand and marginal consumption tendency of food are in the first place; the marginal consumption propensity and demand income elasticity of transportation, communication, and housing are the largest, and the consumption of transportation, communication, and housing is developing into a hot area of urban residents' consumption in Hebei Province; people will pay more and more attention to medical treatment, culture, and education, and gradually change into the development type and enjoyment type of consumption .Consumption is of great significance in the development of national economy, and the current economic situation makes it particularly important to promote consumption. The government should optimize the structure of Chinese household consumption expenditure from many aspects.As far as the current economic situation is concerned, measures can be taken from the following aspects: first, accelerate the reform of state-owned enterprises, accelerate the improvement of the overall efficiency of state-owned enterprises, improve their economic efficiency and increase workers' wages; vigorously develop various forms of collective economy, encourage and support the healthy development of individual economy and private economy, create more employment opportunities, and increase residents' income. Support laid-off workers and other low-income people to ensure the minimum living standard of the city; encourage secondary entrepreneurship through technical training, policy support, financial assistance, etc., vigorously develop the individual and private economy, and gradually increase their income . EncouraAs the main body of consumption, consumers and consumption environment work together and promote each other. Therefore, it is necessary to guide consumers to consume reasonably and scientifically, change traditional consumption concepts, abandon the consumption habits of comparison and extravagance, and establish a correct and reasonable consumption concept. On the one hand, we need to improve the quality of consumers and update the concept of consumption through education and guidance, so that people can correctly understand the importance of scientific consumption mode to the harmonious development of human, society, and ecological environment. On the other hand, we should pay attention to the consumption of spiritual culture . CompletAs an external condition, consumption environment determines the utility that consumer goods can bring to consumers and then has an important impact on the consumption mode and behavior choice of residents. First, improve the consumer market laws and regulations. We should strengthen legal publicity, strengthen market supervision, ensure product quality, crack down on fake and shoddy commercial fraud, establish a good and orderly market order, and purify the consumption environment. To protect the legitimate consumption rights and interests of residents. Secondly, strengthen infrastructure construction . Ensure In the process of adjusting the consumption structure of residents, in addition to the diversification and personalized supply of consumer goods in the traditional consumption field, we should continue to expand new consumption fields. According to the existing traditional market, we should find new potential consumption markets, such as information consumption and green consumption, to form new consumption hot spots and realize the sustainability of consumption structure optimization.From the national level, according to the marginal propensity to consume, income elasticity of demand, and price elasticity of all kinds of consumer goods, we can know that the marginal propensity to consume food in China's household consumption expenditure structure is gradually decreasing, the marginal propensity to consume health care and housing is gradually increasing, and the household consumption expenditure structure is gradually changing to the development-type and enjoyment-type structure. From the regional level, due to different income and prices, there are differences between different provinces. Therefore, analyzing the results of residents' consumption expenditure can optimize the structure of residents' consumption expenditure and help rationalize the system of household consumption expenditure in all regions of China.