Search Results (9)

Fine particulate matter (PM_2.5) plays a major role in haze, and studying its spatio-temporal dynamics and influencing factors is crucial for improving air quality. However, previous studies have often obscured the spatio-temporal interactions of PM_2.5 and neglected local spatio-temporal differences in influencing factors. To address these limitations, this research utilized PM_2.5 concentration data derived from satellite remote sensing and employed exploratory spatio-temporal data analysis (ESTDA) methods to investigate the spatio-temporal evolution patterns of PM_2.5 in Chinese cities from 2000 to 2021. Furthermore, the effects of natural environmental and socioeconomic factors on PM_2.5 were analyzed from both global and local perspectives using a spatial econometric model and the geographically and temporally weighted regression (GTWR) model. Key findings include (1) The annual value of PM_2.5 from 2000 to 2021 ranged between 27.4 and 42.6 µg/m³, exhibiting a “bimodal” variation trend and phased evolutionary characteristics. Spatially, higher concentrations were observed in the central and eastern regions, as well as along the northwestern border, while lower concentrations were prevalent in other areas. (2) The spatial–temporal distribution of PM_2.5 was generally stable, demonstrating a strong spatial dependence during its growth process, with significant path dependence characteristics in local spatial clusters of PM_2.5. (3) Precipitation, temperature, wind speed, and the Normalized Difference Vegetation Index (NDVI) significantly reduced PM_2.5 levels, whereas relative humidity, per capita Gross Domestic Product (GDP), industrialization level, and energy consumption exerted positive effects. These factors exhibited distinct local spatio-temporal variations. These findings aim to provide scientific evidence for the implementation of coordinated regional efforts to reduce air pollution across China. Full article

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved. Full article

With the implementation of the rural revitalization strategy, rural e-commerce has become an essential means of promoting rural economic development and increasing farmers’ income. However, the development of rural e-commerce varies significantly among different regions. Based on the perspective of “three rural areas”, this study constructs a rural e-commerce development capability measurement system centered on readiness, utilization, and influence. It adopts a panel vector autoregressive model to identify key influencing factors. Through the exploratory spatiotemporal data analysis (ESTDA) method, the spatiotemporal dynamic characteristics of rural e-commerce development capacity and the interaction relationship between provinces and regions are revealed. The study shows that (1) China’s rural e-commerce development capacity gained significant improvement from 2011 to 2022, but provincial polarization is evident, with eastern and central provinces leading and western and marginal provinces lagging; the rural e-commerce development capacity shows a decreasing dynamic pattern from the east to the central and western to the northeastern regions. (2) The eastern region has active rural e-commerce development, stable spatial structure, and provincial solid correlation, which creates a significant linkage effect. The western region shows strong internal spatial dependence, the district cross-regional interaction and linkage effect are beginning to emerge, and the northeastern low-development provinces are challenging to leap to a higher level in the short term; (3) the spatiotemporal interaction network of rural e-commerce development among several provinces and regions shows a positive synergistic relationship, and it is an essential consideration for the high-quality development of rural e-commerce to strengthen regional cooperation and realize complementary advantages. The study results provide a theoretical basis for formulating differentiated regional e-commerce development policies, which can help enhance regional synergy and narrow the regional development gap. Full article

The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial–temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of ‘high in the east and low in the west’. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of ‘decreasing and then increasing’, with a spatial distribution of ‘high in the east and low in the west’. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern’s path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction. Full article

The integration of sea and land ecological economies is crucial for the development of a high-quality sea–land economy. This study explores and proposes the concept of a sea–land-integrated ecological economy. By constructing the evaluation index system for developing a sea–land-integrated ecological economy, the development level, evolution process, and development trend prediction of a sea–land-integrated ecological economy were evaluated and analysed in cities around the Bohai Sea from 2009 to 2019 using methods such as a model for assessing the development level, a spatio-temporal autocorrelation model, and an exploratory spatio-temporal data analysis model. The results of the study show that (1) the development level of the ecological economy of the cities of Bohai Rim’s sea–land integration generally had an upward trend; however, the magnitude significantly varied between cities; (2) the spatio-temporal autocorrelation pattern formed three major agglomerations with Dalian in the north, Yantai and Qingdao in the south, and Tianjin and Tangshan in the centre as the core cities and contained low agglomerations and scattered L–H spatio-temporal heterogeneous units; and (3) the integration prediction curve for 2020–2029 indicates that the level value for integration of most cities will improve over time. Full article

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional “double carbon” goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial–Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15′57″~112°25′43″ E and 27°43′13″~27°49′21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the “northwest–southeast” pattern to the “north–south” pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China. Full article

Exploring the assessment methods and multi-scale spatiotemporal interaction characteristics of ecosystem health is of great significance for current ecosystem health theory and application research. Based on the regional differentiation theory and ecosystem service flow theory, the spatial weight coefficient and the modified coefficient of spatial proximity effect were introduced to improve the regional ecosystem health assessment model. Then, the improved VORS model was used to evaluate the ecosystem health level in the Middle Reaches of the Yellow River (MRYR) in China at multiple scales, and the ESTDA method was used to reveal the multi-scale spatiotemporal interaction characteristics of ecosystem health. The results show that: (1) From 1990 to 2018, the ecosystem health level at grid and county scale in the MRYR showed a trend of first decline and then increase, and experienced a slow decline and a steady rise from 1990 to 2005 and 2005 to 2018, respectively. The ecosystem health level at the grid and county scale presented a spatially hierarchical structure with alternating low-value and high-value zones. (2) Compared with the county scale, the grid scale can describe the spatial distribution characteristics of ecosystem health more refined, indicating the existence of spatial scale effects in ecosystem health assessment. (3) The rapid urbanization areas, the ecologically fragile areas in the central and western regions and the transitional zone between mountain and basin have more dynamic spatial structure, and stronger spatio-temporal interaction process. (4) In terms of LISA spatio-temporal transition, the regional system as a whole had strong path-dependent and lock-in characteristics, and the local spatial correlation structure of ecosystem health gradually tended to be stable during the study period. (5) In terms of spatio-temporal interaction network, there were strong spatio-temporal competition in the process of time evolution in six typical regions, such as the surrounding cities of provincial capitals, the fringe areas of cities, the transitional zone between mountain and basin, the transitional zone of ecologically fragile regions, the mountainous areas of western Henan Province, and the areas along rivers. Full article

Urban-land development and utilization is one of the main sources of carbon emissions. Improving the green and low-carbon utilization efficiency of urban land (GLUEUL) under the goal of carbon neutrality is crucial to the low-carbon transition and green development of China’s economy. Combining the concept of green and low-carbon development in urban land use, carbon emissions and industrial-pollution emissions are incorporated into the unexpected outputs of the GLUEUL evaluation system. The super-efficient slacks-based measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method and Geographically and Temporally Weighted Regression (GTWR) model were used to analyze the spatial-temporal transition and the influencing factors of GLUEUL in 282 cities in China from 2005 to 2020. The result shows that: (1) From 2005 to 2020, the green and low-carbon land-utilization efficiency of Chinese cities shows an increasing temporal-evolution trend, but the gap between cities is gradually widening. (2) From the spatial-temporal dynamic characteristics of Local Indicators of Spatial Association (LISA), regions with the highest GLUEUL have strong dynamics and instability, while cities at the lowest level have a relatively stable spatial structure. On the whole, the local-spatial-transfer direction of GLUEUL of each city is stable, with certain path-dependent characteristics. (3) There are differences in the degree of influence and direction of action of different factors on GLUEUL. The economic development level, industrial-structure upgrading, financial support, wealth level, and green-technology-innovation ability have positive effects on overall GLUEUL, with industrial-structure upgrading promoting GLUEUL the most, while urban population size, foreign-investment scale, and financial-development level play a negative role. This study can provide some empirical and theoretical references for the improvement of GLUEUL. Full article

The problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the spatiotemporal evolution of water pollutants and their factors of influence using statistics on the discharge of two water pollutants, namely chemical oxygen demand (COD) and NH₃-N (ammonia nitrogen), in 154 counties in both 2012 and 2016 as research units in the region. The study employed Exploratory Spatial-Time Data Analysis (ESTDA), Standard Deviational Ellipse (SDE), and the Geographically Weighted Regression (GWR) models, as well as ArcGIS and GeoDa software, obtaining the following conclusions: (1) From 2012 to 2016, pollutant discharge dropped significantly, with COD and NH₃-N emissions decreasing 65.9% and 47.2%, respectively; the pollutant emissions possessed the spatial feature of gradual gradient descent from the central districts to the periphery. (2) The water pollutants discharge displayed significant and positive spatial correlations. The spatiotemporal cohesion of the spatiotemporal evolution of the pollutants was higher than their spatiotemporal fluidity, representing strong spatial locking. (3) The level of economic development, the level of urbanization, and the intensity of agricultural production input significantly and positively drove pollutant discharge; the environmental regulations had a significant effect on reducing the emission of pollutants. In particular, the effect for NH₃-N emissions reduction was stronger; the driving effect of the industrial structure and the distance decay was not significant. Full article