Spatiotemporal evolution and driving factors of ecosystem services' transformation in the Yellow River basin, China.

The Yellow River basin (YRB) holds immense ecological significance in China, but it is currently undergoing profound transformations in its ecosystem services (ESs). To formulate appropriate environmental policies, it is vital to gain a comprehensive understanding of the characteristics and influential factors driving the ESs' transformation in the YRB. The spatiotemporal dynamics in ESs was evaluated using the InVEST model, and the modes of the ESs' transformation were summarized. The elements impacting ESs' transformation and their interactions were assessed using the optimal parameter-based geographical detector (OPGD). Over the period from 1980 to 2020, the water yield within the YRB exhibited an upward trajectory, with a distinctive spatial pattern characterized by higher values in the southern and eastern regions, in contrast to lower values observed in the northern and western regions. Similarly, soil conservation demonstrated a tendency to rise over the duration of the research, with southern and western regions consistently exhibiting higher values compared to the northern and eastern regions. In contrast, habitat quality decreased over time and was accompanied by a progressive spatial decline from the southeast regions to the northwest regions. The ESs' transformation in the YRB from 1980 to 2020 indicated three modes: (1) simultaneous increases, this mode was characterized by concurrent increases in water yield and soil conservation; (2) increase and decrease, in this mode, there was an increase in soil conservation accompanied by a decrease in habitat quality; and (3) increase and deterioration, the third mode entailed an increase in water yield but a simultaneous deterioration in habitat quality. The 45-km grid was the best spatial scale for the analysis in this study. Over the span of 2000 through 2020, the ESs' transformation in the YRB was subject to the influence of natural environmental, geographic location-related, socioeconomic, and policy factors. The determinants of the spatiotemporal heterogeneity in ESs' transformation in the YRB demonstrated double-factor and nonlinear enhancement effects. The counterchange with the most significant effects on ESs' transformation were those between economic density and annual mean precipitation, annual mean temperature and ecological restoration, and the per capita income of urban residents and vegetation index.

The dynamic patterns and driving factors of land use conflict in the Yellow River basin of China.

Land use conflict, as the spatial manifestation of conflicting human-land relationship, has a profound impact on sustainable use of regional land resources. Taking the Yellow River Basin (YRB) as an example, a land use conflict assessment model was constructed based on landscape pattern indices. The dynamic patterns and driving factors of land use conflict in the YRB and the corresponding driving factors were then assessed from 2000 to 2020 based on spatial autocorrelation analysis and the geodetector method. Significant spatial and temporal differences in land use conflict were observed in the YRB from 2000 to 2020. During this period, the area of stable controllable decreased by 3465 km2, whereas the areas of strong and extreme conflict increased by 34,964 and 13,057 km2, respectively. The expansion of areas with extreme and strong conflict mostly occurred in regions with high urbanization and human activity, including northern Shaanxi, Hetao Plain, and the Yellow River Delta. The distribution of land use conflict in the YRB from 2000 to 2020 was characterized by significant spatial agglomeration; high-value cluster conflict mainly extended from the midstream area to the upstream area, whereas low-value clusters tended to be concentrated in the upstream area of the Qinghai and Qilian Mountains. The spatial and temporal differentiation in land use conflict from 2000 to 2020 was influenced by factors related to the natural environment, geographic location, social economy, and regional policy in the YRB. The effects of elevation, distance to the nearest major river, population, economic density, and per capita disposable income of residents increased continuously during the study period, whereas the influences of mean annual precipitation and ecological retreat weakened. Analysis of the interactions between driving factors showed significant dual-factor and non-liner enhancement effects on the spatial and temporal differentiation in land use conflict. The findings provide a scientific reference for the comprehensive management of national land and ecological construction in the YRB.

Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin.

The Yellow River Basin (YRB) is a significant area of economic development and ecological protection in China. Scientifically clarifying the spatiotemporal patterns of carbon emissions and their driving factors is of great significance. Using the methods of spatial autocorrelation analysis, hot-spot analysis, and a geodetector, the analysis framework of spatiotemporal differentiation and the driving factors of carbon emissions in the YRB was constructed in this paper from three aspects: natural environment, social economy, and regional policy. Three main results were found: (1) The carbon emissions in the YRB increased gradually from 2000 to 2020, and the growth rates of carbon emissions in the different river reaches were upper reaches > middle reaches > lower reaches. (2) Carbon emissions have an obvious spatial clustering character from 2000-2020, when hot spots were concentrated in the transition area from the Inner Mongolia Plateau to the Loess Plateau. The cold spots of carbon emissions tended to be concentrated in the junction area of Qinghai, Gansu, and Shaanxi. (3) From 2000 to 2020, the driving factors of spatial differentiation of carbon emissions in the YRB and its different reaches tended to be diversified, so the impacts of socioeconomic factors increased, while the impacts of natural environmental factors decreased. The influence of the interactions of each driving factor showed double factor enhancement and nonlinear enhancement. This study will provide a scientific reference for green and low-carbon development, emphasizing the need to pay more attention to environmental protection, develop the green economy vigorously, and promote the economic cycle, so as to achieve green development and reduce carbon emissions.

The Pattern and Local Push Factors of Rural Depopulation in Less-Developed Areas: A Case Study in the Mountains of North Hebei Province, China.

Rural depopulation is the most significant geographical phenomenon in rural areas during the process of urbanization. Although many studies have investigated the driving force of rural depopulation based on rural-urban migration at the macro level, the local factors, and their impact on rural depopulation from the rural areas have been not fully revealed. This paper selected the northern mountains of China's Hebei province as a study area to explore the pattern and local push factors of rural depopulation at the rural-township levels based on GeoDetector. The main findings are summarized as follows. (1) Rural depopulation varies substantially, demonstrates spatial correlation, and is distributed in clusters. From a dynamic perspective, compare that in years 2000-2010, the population growth areas during 2010-2017 have been significantly expanded, while the sharp depopulation areas and severe depopulation areas experienced shrinkage in our study area. (2) The pattern of rural depopulation is in accordance with terrain. Rural depopulation tends to be stronger in plateaus and mountains, while relatively milder in intermontane basins, hills, and piedmont plains. (3) The agricultural suitability of natural environmental and rural economic opportunities together with climate changes were the most important driving forces of rural depopulation at local levels. Location, sparse population, and inadequate public services also contributed to rural depopulation. However, the dominant driving factors are different in the different periods. Rural depopulation was mainly driven by arable land per capita and natural environmental variables in the years 2000-2010, while the population density, location, and off-farm economic opportunities played a decisive role in the years 2010-2017. (4) Rural depopulation is a complex, multi-dimensional process driven by a combination of multiple factors including different environmental factors, economic opportunities, and location. This paper reveals the push factors of rural depopulation in underdeveloped mountainous areas by a quantitative empirical approach, inspiring increased attention to the impacts of local factors and spatial correlations on rural depopulation, and has many implications for the policy design of China's rural revitalization.

Spatial relationships between ecosystem services and socioecological drivers across a large-scale region: A case study in the Yellow River Basin.

Understanding the relationships between ecosystem services (ES) and their underlying socioecological drivers is essential for forming the efficient management decisions of ecosystems. We use a large watershed area as a case-study to analyze trade-offs/synergies and bundles of ESs and identify the associated socioecological variables (SEVs). This study assessed the supply of 7 ES indicators, namely, three provisioning services (crop production, livestock production, and industrial production), three regulating services (water conservation, soil conservation, and carbon sequestration), and one cultural service (recreation), across 65 municipalities in the Yellow River Basin (YRB) in China. We analyzed the paired trade-offs/synergies using Spearman's coefficient and identified the ES bundles (ESBs) by applying principal component analysis and K-means clustering. Subsequently, we detected the SEVs that affect the ES supply using the geo-detector model and characterized the associations between ESBs and socioecological clusters according to the spatial overlap. The results demonstrated that the synergies between ESs substantially exceeded the trade-offs, among which the strongest synergies were between the crop production and the livestock production, and both responded strongly to the cropland and the population density. Trade-offs were identified between provisioning services and soil conservation. Municipalities were grouped into three ESBs in the YRB. The ESB, which was dominated by provisioning ESs, was associated with areas where cropland, precipitation and socioeconomic conditions were all important, and the regulation of ESB was linked to regions with distinct ecological characteristics. We also identified an ESB that was dominated by carbon sequestration, as determined by extensive grassland and bare land. The land use/land cover strongly affected the characteristics of the ESBs. The findings can be used by land managers to identify areas in which ESs are dominant, to determine the associations of these compositions of the ESs with SEVs, and to support the formulation of optimal ES management in large-scale basins.

Land use change and its driving forces toward mutual conversion in Zhangjiakou City, a farming-pastoral ecotone in Northern China.

Land use/cover change (LUCC), a local environmental issue of global importance, and its driving forces have been crucial issues in geography and environmental research. Previous studies primarily focused on major driving factors in various land use types, with few explorations of differences between driving forces of mutual land use type conversions, especially in fragile eco-environments. In this study, Zhangjiakou City, in a farming-pastoral ecotone in Northern China, was taken as an example to analyze land use change between 1989 and 2015, and explore the driving forces of mutual land use type conversions using canonical correlation analysis. Satellite images and government statistics, including social-economic and natural data, were used as sources. Arable land, forestland, and grassland formed the main land use structure. From 1989 to 2015 forestland, orchard land, and construction land significantly increased, while arable land, grassland, unused land, and water areas decreased. Conversions from grassland to forestland; from arable land to orchard land, forestland and construction land; and from unused land to grassland and forestland were the primary land use changes. Among these, the conversion from grassland to forestland had the highest ranking. Average annual precipitation and per capita net income of rural residents positively affected the conversion of arable land to forestland and unused land to grassland. GDP, total population, and urbanization rate contributed most significantly to converting arable land to construction land; total retail sales of social consumer goods, average annual temperature, and GDP had important positive influences in converting arable land to orchard land.