Spatial-temporal Differentiation Research Articles

Spatial–Temporal Differentiation and Influencing Factors of Rural Education Development in China: A Systems Perspective

Education is the cornerstone of rural revitalization. This study aims to comprehensively evaluate the development of rural education in China from 2006 to 2020. From a systemic perspective, this study established a multidimensional evaluation index system for rural education and used the weight-TOPSIS method for measurement. Additionally, geographic information system and spatial econometric methods were employed to explore spatial–temporal differentiation and influencing factors. The results show that (1) rural education levels in China have generally improved in recent years, with higher development in northern, northeastern, and eastern regions and lower levels in central and southwestern regions. (2) In terms of spatial differentiation, rural education development among provinces has significant spatial agglomeration. The provinces around Beijing are hot spots, while remote southwestern provinces are cold spots. (3) Regarding dynamic evolution, the disparity in rural education development among provinces has widened, with a few provinces significantly ahead. There are club convergence features, and the hierarchy of rural education development between provinces is relatively stable, with less likelihood of lagging provinces catching up. (4) Economy, finance, industry, population, and urbanization are key factors influencing rural education, with spatial spillover effects on neighboring provinces. The study provides empirical support and policy insights for advancing balanced and high-quality rural education development.

What drives the spatial–temporal differentiation of transportation carbon emissions in China? Evidence based on the optimal parameter-based geographic detector model

What drives the spatial–temporal differentiation of transportation carbon emissions in China? Evidence based on the optimal parameter-based geographic detector model

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013–2020

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013–2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

Evaluation of spatial conflicts of land use and its driving factors in arid and semiarid regions: A case study of Xinjiang, China

The study of spatial conflicts of land use (SCLU) is an important breakthrough to reveal the evolution mechanism of regional human-land relationship. Accurate identification of SCLU and scientific exploration of its spatio-temporal characteristics of their driving factors are pivotal for regional sustainable development. However, the basic theoretical system of SCLU still needs to be improved, and the spatio-temporal characteristics of SCLU at different spatial scales and landforms as well as the spatio-temporal correlation effects of its driving factors need to be further studied. This study utilizes theoretical models for the identification of SCLU. Employing a comprehensive index approach involving ArcGIS, FRAGSTATS landscape pattern analysis, and the multi-scale geographically weighted regression model, the spatio-temporal characteristics of SCLU are analyzed across different administrative scales and Macro-landforms. The investigation unveils the intrinsic mechanisms of spatio-temporal differentiation driven by various factors from 2000 to 2020 in the arid and semiarid region of Xinjiang, China. Results indicated that: (1) SCLU is a sensitive indicator of human-land interaction, measurable using conflict risk and effect; (2) The SCLU in Xinjiang presents a spatial pattern of “severe conflict around the three mountains and mild conflict around the two basins” and the SCLU transitioned from an outbreak to a relief stage over the past two decades; (3) SCLU varies significantly among landform types, with conflict intensity in Plain > Mountains > Platform > Hill areas; (4) Elevation and slope in natural environmental factors, human footprint and distance of patches from water, from roads, and from railroads in social activity factors have inhibitory effects on SCLU, and GDP, population density, and human impact index in economic development factors have positive effects on SCLU, and focusing on controlling potentially high-conflict risk areas and enhancing the upgrading of ecological quality are key to mitigating SCLU. The results validate the proposed theoretical framework and provide theoretical and practical references for accurately identifying SCLU and optimizing national territory spatial patterns.

Spatio-temporal Evolution and Coordinated Development of Compactness with Carbon Emission Intensity in the Chengdu-Chongqing Urban Agglomeration

Studies on the spatio-temporal variation and coordinated development level of compactness with carbon emission intensity in the Chengdu-Chongqing urban agglomeration is of great significance to achieve green, low-carbon, and high-quality development of society. The spatial-temporal differentiation, coupling coordination, and driving factors of the compactness and carbon emission intensity of the Chengdu-Chongqing urban agglomeration from 2010 to 2020 were analyzed by using the methods of comprehensive evaluation, carbon emission identical equality, coupling coordination, and the Geo-detector model. The results showed that:① The compactness of the Chengdu-Chongqing urban agglomeration continued to rise from 0.18 in 2010 to 0.22 in 2020, with an overall increase of 22.22 %, in which the contribution rate of economic compactness to urban compactness increased yearly. There were significant regional differences in the compactness of cities. The high-value areas were concentrated in Chengdu, Deyang, and Mianyang in the northwest and Zigong, Neijiang, and Luzhou in the southwest, whereas the low-value areas were distributed in the middle. Ya'an was always the area with the lowest compactness of urban agglomeration. ② The carbon emission intensity of the Chengdu-Chongqing urban agglomeration was decreasing yearly, with a decrease rate of 39.57 % during the study period. The carbon emission intensity in the southern part of the urban agglomeration was higher than that in other regions as a whole, whereas Chengdu and Chongqing were low-value areas all the year round, and the regional differences of carbon emission intensity were gradually narrowing. ③ The coupling degree between urban compactness and carbon emission intensity changed from the antagonistic stage to the running-in stage; the coupling coordination degree increased notably, from 0.21 in 2010 to 0.69 in 2020; and the two systems gradually moved towards benign and coordinated development. The coupling coordination between the northwest of the urban agglomeration (Chengdu, Deyang, and Mianyang) and the south of Sichuan (Zigong and Luzhou) was generally high. Industrial structure, scientific and technological innovation, urbanization level, government intervention, and environmental livability all had significant effects on the coupling and coordination of the two systems. ④ Optimizing industrial structure, strengthening scientific and technological support, improving the quality of urbanization development, implementing active policy guidance, and building green barriers were effective ways to promote the coordinated development of compactness and low carbon in the Chengdu-Chongqing urban agglomeration.

Rural Industrial Integration and New Urbanization in China: Coupling Coordination, Spatial–Temporal Differentiation, and Driving Factors

Understanding the coupling coordination status between rural industrial integration (RII) and new urbanization (NU) is critical for the Chinese government to optimize policies that promote the synergetic and sustainable development of RII and NU. Based on constructing evaluation index systems, this paper uses the entropy value method, coupling coordination degree model, exploratory spatial analysis method, gravity center model, and geographical detector model to reveal the characteristics of the spatial–temporal differentiation of the coupling coordination and its driving factors in China. The results show that: (1) The regional differences in the coupling coordination degree between RII and NU are obvious, and the coupling coordination degree of China’s three economic regions are all in an increasing trend, but the mean values in the central and western regions are always below the national average. (2) The coupling coordination degree has significant spatial agglomeration characteristics, but the regional differences are gradually decreasing. The whole country is still dominated by the low–low agglomeration, and the provinces with the high–high agglomeration are mainly located in the eastern region. (3) The gravity center of the coupling coordination degree has moved in both the east–west and north–south directions from 2011 to 2021, but the movement trend in the north–south direction is more obvious. (4) The economic development level, industrial structure, transportation conditions, government support capacity, financial support level, and agricultural mechanization level are important factors driving the spatial–temporal variation, and their interactions will enhance the differentiation. The results can provide a theoretical basis and decision-making reference for relevant government departments in China to promote the sustainable development of RII and NU.

Ecological risk assessment and spatial–temporal differentiation of soil and water resources in the Hefei metropolitan area

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities

Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China.

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006-2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Spatial–Temporal Differentiation and Trend Prediction of Coupling Coordination Degree of Port Environmental Efficiency and Urban Economy: A Case Study of the Yangtze River Delta

Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in the Yangtze River Delta (YRD) were selected and port environmental efficiency (PEE) was calculated through the window SBM model, while the EW-TOPSIS model was used to evaluate high-quality urban economic development (HED). The coupling coordination degree (CCD) model, the kernel density model, GIS spatial analysis, and the grey prediction model were used to further explore the spatial–temporal dynamic evolution and prediction of the CCD between PEE and HED. The results suggested that: (1) PEE fluctuation in the YRD is increasing, with a trend of seaports achieving higher PEE than river ports; (2) HED in the YRD shows upward trends, and the polarization of individual cities is obvious; (3) Temporally, the CCD in the YRD has risen from 0.438 to 0.518. Shanghai consistently maintains intermediate coordination, and Jiangsu has experienced the most significant increase in CCD. Spatially, CCD is led by Lianyungang, Suzhou, Shanghai, and Ningbo-Zhoushan, displaying a decreasing distribution pattern from east to west. The projection for 2026 suggests that all port cities within the YRD will have transitioned to a phase of orderly development. To enhance the coordination level in the YRD, policymakers should consider the YRD as a whole to position the ports functionally and manage them hierarchically, utilize the ports to break down resource boundaries to promote the synergistic division of labor among cities, and then tilt the resources towards Anhui.

Spatial–Temporal Differentiation and Driving Factors of Cultivated Land Use Transition in Sino–Vietnamese Border Areas

Understanding the transformation of cultivated land use is crucial for advancing sustainable development goals related to food security. However, in mountainous regions, there is a lack of comprehensive studies that fully account for the diverse factors influencing cultivated land use transformation. This study aims to elucidate the temporal and spatial dynamics of cultivated land resource transformation in the mountainous Sino–Vietnam border area, uncover its underlying driving mechanisms, and offer insights for safeguarding cultivated land, promoting economic development, ensuring homeland security, enhancing ecological security, and bolstering border stability and prosperity. To investigate the cultivated land use transformation pattern in the Sino–Vietnam border area from 2000 to 2020, we employed kernel density estimation and geo-information spectra. Additionally, we developed a comprehensive driving force system tailored to the unique characteristics of cultivated land use in the border region. We applied a spatial econometric model to dissect the driving mechanisms governing cultivated land use transformation. Our findings revealed several key insights: (1) The density of cultivated land in the Sino–Vietnam border area exhibited an initial increase followed by a decrease. Notably, the transformation of cultivated land was most prominent in the eastern plains, intensifying over time. (2) The predominant type of transformation in the Sino–Vietnamese border area revolved around the mutual conversion of cultivated land and woodland, with the mutual conversion of cultivated land and grassland ranking second. (3) Against the backdrop of urban–rural integration, the transformation of cultivated land use at the border progressed from a phase of rapid decline to a phase of slower decline. (4) The transformation of cultivated land was influenced by a complex interplay of socio-economic factors, natural environmental conditions, policy management, and transportation infrastructure. The relative importance of these factors in driving cultivated land use transformation varied significantly across different time periods. In light of these findings, we recommend promoting agricultural modernization and industrialization in the Sino–Vietnamese border areas. It is essential to consider the region’s distinct cultivated land characteristics, implement tailored land policies, and develop diversified strategies for the utilization and management of cultivated land. Furthermore, harnessing land resources to stimulate economic development should be a focal point of future initiatives in the area.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Spatial-temporal differentiation and driving forces of medical service resilience of prefecture-level cities in China

Spatial-temporal differentiation and driving forces of medical service resilience of prefecture-level cities in China

Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province.

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town's industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.

Spatial-temporal differentiation characteristics and driving factors of China's energy eco-efficiency based on geographical detector model

As China is striving to build a modernization in which people and nature live in harmony, evaluating energy eco-efficiency (EEE) is an important part of achieving a balanced development of socio-economic construction and ecological civilization. From the perspective of the “energy-economy-ecology-society” system, this study included social welfare elements and PM2.5 into the EEE evaluation index, and the EEE of 30 provinces in China from 2000 to 2019 were examined more comprehensively. The distribution differences and sources of EEE were measured by Trend analysis and the Dagum Gini coefficient. A unique analytical method that reveals the drivers of spatial heterogeneity, the Geographical Detector model (Geodetector), was used to better account for the explanatory power of the drivers and their interactions. The results show that China's EEE has a weak “N"-shaped trend in time series, with significant spatial heterogeneity, and the contribution of this imbalance is ranked as inter-regional (39.83%) > hypervariable density (31.95%) > inter-regional (28.22%). The core factors that dominate the spatial-temporal differentiation in the East, Center and West are energy consumption structure (0.688), environmental regulation (0.414), and the level of urbanization (0.548), respectively. The factor interactions are all greater than the explanatory power of factor independent effects on EEE divergence, and the strongest interaction is between Environmental Regulation and Level of educational development in 2010(0.91). The interactions have regional characteristics, and targeted EEE enhancement paths should be selected for different regions.

Spatial–Temporal Coupling Analysis of Land Use Function and Urban–Rural Integration in Heilongjiang, China

Urban–rural integration relies on the rational flow of factors between urban–rural areas. Land represents a closely related factor between urban–rural areas, so the effective utilization of land resources can promote the flow of urban–rural factors. Therefore, there is a certain correlation between land use function and urban–rural integration. The purpose of this study is to explore the coupling and coordination relationship between the two systems and to find out the spatial–temporal differentiation characteristics in the process of land use function and urban–rural integration. The main conclusions are as follows: (1) The comprehensive level of land use function and urban–rural integration in Heilongjiang Province shows an overall upward trend, but there is a large differentiation on a municipal scale. (2) The coupling coordination degree of the two systems in Heilongjiang Province shows a spatial distribution pattern of “high in the north and low in the south, high in the middle and low in the east and west”. From 2013 to 2022, except for Harbin and Yichun, the overall trend in other regions is gradually upward. (3) The obstacle degree analysis of land use function and urban–rural integration in Heilongjiang Province shows that there is a close correlation of obstacle factors between the two systems.

Analysis on spatial-temporal variation characteristics of climate in Qinling-Huaihe demarcation zone since 1961

The emerging global environmental crisis, intensified by global warming, necessitates a deeper understanding of climate variations, especially in critical geographical divisions like the Qinling-Huaihe demarcation zone, which acts as a natural division between northern and southern mainland China. This study investigates the spatial–temporal differentiation of climatic elements within the Qinling-Huaihe demarcation zone from 1961 to 2018, employing data accumulated from 144 meteorological stations. Utilizing methodologies including the univariate linear regression, the Mann-Kendall test, and the Kriging interpolation, this investigation unveils the spatial–temporal variation characteristics of precipitation, relative humidity, and temperature. Temporally, the climatic alterations in the zone are predominantly marked by warming trends, significantly pronounced since the 1990s, with the elevation in minimum temperature being the most notable. Both precipitation and relative humidity exhibit a fluctuating ascending trend. Spatial analysis reveals a west-to-east increase in precipitation, while relative humidity presents lower values in the central region, escalating towards the eastern and western edges. The spatial distribution patterns for average, maximum, and minimum temperatures align closely, showcasing higher temperatures in the central region, extending to the Huaihe River area, with the Qinling Mountains exhibiting the lowest temperatures, particularly noticeable north of the Qinling Mountains.

Spatial–temporal differentiation and control strategies of nitrogen environmental loss in China's coastal regions based on flow analysis

Nitrogen pollution emissions from human production and living activities in coastal regions are important topics in the management of environmental pollution in coastal waters. However, to date, there has been relatively little research systematically assessing the environmental loss of nitrogen (NEL) from human activities that negatively affect marine ecosystems. This study categorised emission sources into five subsystems, namely livestock, farming, aquatic, industrial, and residential. Through flow analysis, the anthropogenic emissions of nitrogen in the gas, liquid, and solid phases from 11 coastal provinces in China in 2011, 2015, and 2020 were determined. A nitrogen cost index was constructed by combining the social indicators of each province. The effectiveness of nitrogen emission control since the land-sea coordination and the future challenges for the coastal region were discussed from various perspectives. The results of the study showed that the total NEL that poses a potential threat to marine ecosystems in coastal areas of China has decreased from 18.93 TgN to 14.66 TgN since the proposal of land-sea coordination, with livestock systems and aquatic systems emitting the most. The Bohai and Yellow Seas area were most threatened by nitrogen pollution. Among the three oceanic pathways, liquid-phase nitrogen discharge from each subsystem was effectively controlled, and the control of gas-phase nitrogen emissions is still the most numerous NEL state, although it has had a significant effect. The results of the correlation analysis suggest that NEL flow can characterize the regional management of nutrient-based organic pollutants. Past management tools and environmental investments in China have been more effective in controlling emissions from point and line sources involving artificial facilities, but less direct effect on mariculture. How to control surface source pollution from livestock and aquaculture will be an important challenge to reduce reactive nitrogen emissions in the future.

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province