Search Results (1,406)

Improving the ecological resilience in the Yellow River Basin is a crucial way to achieve ecological conservation and high-quality development in the region. Based on the panel data from 2011 to 2023 of 57 cities in the Yellow River Basin, the ecological resilience of each city was measured by using the Catastrophe Progression Model, and its spatial differences and dynamic evolution characteristics were analyzed by the Dagum Gini coefficient and kernel density estimation. At the same time, the STIRPAT model was integrated with the random forest model to identify the key factors influencing urban ecological resilience. The results demonstrated the following: (1) The urban ecological resilience in the Yellow River Basin exhibited a slight upward trend during 2011–2020 and presented a gradient spatial pattern with “high in the east and low in the west”. (2) Hypervariation density is the main source of spatial difference in urban ecological resilience, with trailing and polarization phenomena across the entire basin and its three major subregions. (3) There was significant regional heterogeneity of influences in the urban ecological resilience, with upstream, midstream, and downstream regions characterized by low interference intensity, high sensitivity, and strong adaptability, respectively. Full article

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

Based on multi-source data, including synthetic aperture radar (Sentinel-1, S1) and optical satellite images (Sentinel-2, S2), topographic data, and climate data, this study explored the performance and feasibility of different variable combinations in predicting SOC using three machine learning models. We designed the three models based on 244 samples from the study area, using 70% of the samples for the training set and 30% for the testing set. Nine experiments were conducted under three variable scenarios to select the optimal model. We used this optimal model to achieve high-precision predictions of SOC content. Our results indicated that both S1 and S2 data are significant for SOC prediction, and the use of multi-sensor data yielded more accurate results than single-sensor data. The RF model based on the integration of S1, S2, topography, and climate data achieved the highest prediction accuracy. In terms of variable importance, the S2 data exhibited the highest contribution to SOC prediction (31.03%). The SOC contents within the study region varied between 4.16 g/kg and 29.19 g/kg, showing a clear spatial trend of higher concentrations in the east than in the west. Overall, the proposed model showed strong performance in estimating grassland SOC and offered valuable scientific guidance for grassland conservation in the western Songnen Plain. Full article

The sustainable development of higher education exhibits a strong and measurable association with the level of regional innovation capacity. Drawing on panel data covering 31 provincial-level administrative regions in China from 2010 to 2022, we construct evaluation frameworks for both higher education and regional innovation capacity using the entropy weight method. These are complemented by kernel density estimation, spatial autocorrelation analysis, Dagum Gini coefficient decomposition, and the Obstacle Degree Model. Together, these tools enable a comprehensive investigation into the spatiotemporal evolution and driving mechanisms of coupling coordination dynamics between the two systems. The results indicate the following: (1) Both higher education and regional innovation capacity indices exhibit steady growth, accompanied by a clear temporal gradient differentiation. (2) The coupling coordination degree shows an overall upward trend, with significant inter-regional disparities, notably “higher in the east and low in the west”. (3) The spatial distribution of the coupling coordination degree reveals positive spatial autocorrelation, with provinces exhibiting similar levels tending to form spatial clusters, most commonly of the low–low or high–high type. (4) The spatial heterogeneity is pronounced, with inter-regional differences driving overall imbalance. (5) Key obstacles hindering regional innovation include inadequate R&D investment, limited trade openness, and weak technological development. In higher education sectors, limitations stem from insufficient social service benefits and efficiency of flow. This study recommends promoting the synchronized advancement of higher education and regional innovation through region-specific development strategies, strengthening institutional infrastructure, and accurately identifying and addressing key barriers. These efforts are essential to fostering high-quality, coordinated regional development. Full article

Exploring the impact of national park construction on county-level livelihood and well-being holds significant implications for enhancing social livelihood. This study treats Wuyishan National Park Construction (WNPC) as a quasi-natural experiment, utilizing panel data from 138 counties (2011–2023) to construct a county-level livelihood and well-being index through the CRITIC weighting method. Kernel density estimation and the Theil index are applied to depict the spatiotemporal dynamics of WNPC. Moreover, the difference-in-differences model and mediating effect model are employed to assess the impact and mechanisms of WNPC on livelihood and well-being. The results reveal that, in the period 2011–2023, livelihood and well-being scores ranged from 0.1329 to 0.4565, indicating considerable scope for improvement. Over time, inter-county disparities narrowed, displaying a spatial pattern of “higher in the east and west, lower in the middle.” Overall disparities remained pronounced, driven chiefly by within-region variation, and Jiangxi displayed notably larger internal gaps than Fujian and Zhejiang. Benchmark regressions confirm that WNPC significantly improved livelihood and well-being, with robust results according to multiple tests. Mechanism analysis indicates that WNPC enhances livelihood and well-being by promoting population mobility and improving infrastructure. Heterogeneity analysis suggests that compared to industrial counties, WNPC has a stronger positive effect on the livelihood and well-being of agricultural counties. Based on this, it is suggested that WNPC promotes population mobility and improves infrastructure construction. This study provides a scientific basis and decision-making reference for achieving high-quality construction of national parks and enhancing livelihood and well-being. Full article

Watershed ecosystem resilience (RES) plays a vital role in supporting ecosystem sustainability. However, comprehensive assessments and investigations into the complex mechanisms driving RES remain limited, particularly in ecologically sensitive basins. To address this gap, this study proposes a multidimensional RES evaluation framework tailored to watershed-specific natural characteristics. The framework integrates five core dimensions: ecosystem resistance, ecosystem recovery capacity, ecosystem adaptability, ecosystem services, and ecosystem vitality. RES patterns under 2030 different future scenarios were simulated using the PLUS model combined with CMIP6 climate projections. Spatial and temporal dynamics of RES from 2010 to 2020 were quantified using Geodetector and Partial Least Squares Path Modeling, offering insights into the interactions among natural and anthropogenic drivers. The results reveal that RES in the Upper Yangtze River Basin exhibits a spatial gradient of “high in the east and west, low in the middle” with an overall 2.80% decline during the study period. Vegetation coverage and temperature emerged as dominant natural drivers, while land use change exerted significant indirect effects by altering ecological processes. This study emphasizes the importance of integrated land-climate strategies and offers valuable guidance for enhancing RES and supporting sustainable watershed management in the context of global environmental change. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

The inter-provincial border region in eastern China, encompassing the junction of Jiangsu, Anhui, Shandong, and Henan provinces, serves as a crucial zone that connects the important economic zones of Beijing–Tianjin–Hebei and the Yangtze River Delta. It is of great significance to study the temporal variation characteristics, spatial distribution patterns, and driving factors of PM_2.5 concentrations in this region. Based on the PM_2.5 concentration observation data, ground meteorological data, environmental data, and socio-economic data from 2022 to 2024, this study conducted in-depth and systematic research by using advanced methods, such as spatial autocorrelation analysis and geographical detectors. The research results show that the concentration of PM_2.5 rose from 2022 to 2023, but decreased from 2023 to 2024. From the perspective of seasonal variations, the concentration of PM_2.5 shows a distinct characteristic of being “high in winter and low in summer”. The monthly variation shows a “U”-shaped distribution pattern. In terms of spatial changes, the PM_2.5 concentration in the inter-provincial border region of eastern China (Jiangsu, Anhui, Shandong, Henan) forms a gradient difference of “higher in the west and lower in the east”. The high-concentration agglomeration areas are mainly concentrated in the Henan part of the study region, while the low-concentration agglomeration areas are distributed in the eastern coastal parts of the study region. The analysis of the driving factors of the PM_2.5 concentration based on geographical detectors reveals that the average temperature is the main factor affecting the PM_2.5 concentration. The interaction among the factors contributing to the spatial differentiation of the PM_2.5 concentration is very obvious. Temperature and population density (q = 0.92), temperature and precipitation (q = 0.95), slope and precipitation (q = 0.97), as well as DEM and population density (q = 0.96), are the main combinations of factors that have continuously affected the spatial differentiation of the PM_2.5 concentration for many years. The research results from this study provide a scientific basis and decision support for the prevention, control, and governance of PM_2.5 pollution. Full article

As one component of a study to improve Medicago spp. germplasm in eastern Australia, fifteen phenotypic and agronomic attributes were recorded for 4715 plants grown from the seed of 90 accessions of the widely naturalized pasture legume Medicago polymorpha from 90 sites in eight regions of inland New South Wales. The species expressed wide polymorphism. However, many leaflet attributes were associated with specific climate and soil characteristics, which varied from east to west across the collection zone. Discriminant analysis showed that accessions from the four most northern (summer dominant rainfall) and western (arid–semiarid) regions (Group A) differed from accessions from the most southern, temperate (winter dominant rainfall) and eastern (higher rainfall) regions (Group B). Group A flowered earlier and had shorter pod spines. Group B had lower plant vigor. Regions from which Group A accessions were collected had higher soil pH, lower winter rainfall, and higher minimum winter temperature than Group B regions. The diversity in the population, particularly the difference in flowering times among accessions collected from drier, warmer regions and those from more mesic, cooler regions, and the wide variation in flowering time measured among plants grown from accessions within all collection regions, is likely to ensure the long-term persistence of M. polymorpha in a changing climate. Elite lines were subsequently identified and lodged in National and International Genebanks for future research. Full article

Optimizing the ecological network is an urgent need to enhance the stability of the ecosystem and maintain regional ecological security. We utilized the PLUS (Patch-generating Land Use Simulation) model to simulate the land use patterns of the Loess Plateau of China under four different development scenarios in 2030, constructed the corresponding ecological network, and evaluated the network structure. The results indicate the following: (1) By 2030, the spatial pattern of ecological network under the four scenarios will be concentrated in the east and west, in the north and south, and the middle of the Loess Plateau. (2) The change of land use pattern driven by a single policy has a trade-off effect on the ecological network and is prone to form the phenomenon of “ecological increase–functional lag”. (3) The regional ecological network layout of “four cores, multiple corridors and multiple sources” was proposed. The results reveal the development trends of land-use change and ecological protection construction under different future development scenarios in the Loess Plateau, which is helpful for decision-makers to balance the relationship between ecological protection and economic development and realize regional sustainable development. Full article

Arbuscular mycorrhizal (AM) fungi (AMF) form a symbiotic association with terrestrial plants and increase growth and productivity. The relationships between the growth of centipedegrass (CG) and AMF are not well understood. We monitored the growth and AM colonization of CG growing on the four slopes (north, east, south, and west) of a pasture, to obtain information on aspect differences in the soil chemical properties–grass–AMF association. Soil properties almost always varied between the slope aspects. The total soil N, C, EC, and moisture tended to be highest on the northern aspect, whereas the soil available P and pH tended to be highest on the western and southern aspects, respectively. Despite the aspect differences in the microclimate and soil properties, CG grew well in all aspects, showing similar dry matter weights (DMW) for the fouraspects. Furthermore, the AM colonization of CG, in any characteristic structures (internal hyphae, vesicles, and arbuscules), was not significantly different between the slope aspects on most measurement occasions, although the colonization usually varied between the seasons and years. There were no relationships between the DMW and AM characteristic structure colonization and between the DMW and soil chemical properties. However, the colonization of the arbuscules and vesicles of the CG had a correlation with some soil chemical properties. The results suggest that AM colonization on CG growing in a hill pasture did not differ between the slope aspects. This may be a factor contributing to the high adaptability of the grass to all slope aspects. Full article

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689, p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces. Full article

As a major contributor to energy consumption and carbon emissions, the low-carbon transformation of the construction industry is crucial for China to achieve its established carbon-emission reduction targets. Therefore, a systematic analysis of the spatial and temporal evolution trends and key drivers of carbon emissions in the construction industry is an important reference for the formulation of emission reduction policies in the industry and the promotion of green and low-carbon development. This study first estimated carbon emissions from direct and indirect energy consumption in China’s construction industry. Spatial and temporal variations in emissions were then analyzed using spatial autocorrelation and kernel density methods. Furthermore, an improved logarithmic mean Divisia index decomposition model, tailored to the characteristics of the construction industry, was applied to quantify the key driving factors. The results reveal that total carbon emissions follow an inverted U-shaped trend, with indirect carbon emissions—mainly from the production of cement and steel—being the dominant contributors. Emissions display a spatially uneven pattern: high in the east and south, low in the west and north, with the high-emission zone gradually expanding from the east to the central regions. Marked regional differences also exist in the evolution of emission intensity. Output intensity and energy intensity are identified as primary drivers of emissions, with their impact particularly prominent in the eastern region. These findings provide a quantitative basis and theoretical support for developing region-specific emission reduction policies, advancing the green and high-quality development of China’s construction industry. Full article

More than half of the world’s rivers are intermittent, and climate change is increasing their intermittency, affecting water resources and ecosystems. In Taiwan, steep topography and uneven rainfall have led to increased intermittency in some areas, reflecting changes in hydrological conditions. Using streamflow data, this study applied intermittency ratio (IR), modified 6-month dry period seasonality (SD6), and trend analysis, as well as watershed properties and climate indices. Results showed that 92% of stations had low flows for less than 20% of the time. The dry season was mainly from November to April, and intermittency was spatially affected mainly by upstream soil moisture, moderately by potential evapotranspiration and infiltration, and less by actual evapotranspiration and catchment area. Intermittency increased in the east and decreased in the west. It was negatively correlated with upstream soil moisture and strongly associated with rainfall frequency, especially the proportion of days with precipitation less than 1 mm. These patterns highlight regional differences in river responses to climate. Full article