Search Results (168)

As an integral component of humanity’s cultural heritage, traditional villages universally confront challenges such as population loss and cultural discontinuity amid rapid urbanization. Cluster-based protection models have increasingly become the international consensus for addressing the survival crisis of such settlements. This study selects the Central Yunnan region of Southwest China—characterized by its complex geography and multi-ethnic habitation—as the research area. Employing ArcGIS spatial analysis techniques alongside clustering algorithms, we examine the spatial distribution characteristics and clustering patterns of 251 traditional villages within this region. The findings are as follows. In terms of spatial distribution, traditional villages in Central Yunnan are unevenly dispersed, predominantly aggregating on mid-elevation gentle slopes; their locations are chiefly influenced by rivers and historical courier routes, albeit with only indirect dependence on waterways. Regarding single-cluster attributes, the spatial and geomorphological features exhibit a composite “band-and-group” pattern shaped by river valleys; culturally, two dominant modes emerge—“ancient-route-dependent” and “ethnic-symbiosis”—reflecting an economy-driven cultural mechanism alongside latent marginalization risks. Concerning construction characteristics, the “Qionglong-Ganlan” and Han-style “One-seal” residential features stand out, illustrating both adaptation to mountainous environments and the cumulative effects of historical culture. Based on these insights, we propose a three-tiered clustering classification framework—“comprehensive-element coordination”, “feature-led”, and “potential-cultivation”—to inform the development of contiguous and typological protection strategies for traditional villages in highland, multi-ethnic regions. Full article

The increasing levels of ozone pollution have become a significant environmental issue in urban areas worldwide. Previous studies have confirmed that the urban ozone pollution in China is mainly controlled by volatile organic compounds (VOCs) rather than nitrogen oxides. Therefore, a study on the emission characteristics and source analysis of VOCs is important for controlling urban ozone pollution. In this study, hourly concentrations of 57 VOC species in four groups were obtained in April 2022, a period of high ozone pollution in Kunming, China. The ozone formation potential analysis showed that the accumulated reactive VOCs significantly contributed to the subsequent ozone formation, particularly aromatics (44.16%) and alkanes (32.46%). In addition, the ozone production rate in Kunming is mainly controlled by VOCs based on the results of the empirical kinetic modeling approach (K_NOx/K_VOCs = 0.25). The hybrid single-particle Lagrangian integrated trajectory model and polar coordinate diagram showed high VOC and ozone concentrations from the southwest outside the province (50.28%) and the south in local areas (12.78%). Six factors were obtained from the positive matrix factorization model: vehicle exhaust (31.80%), liquefied petroleum gas usage (24.16%), the petrochemical industry (17.81%), fuel evaporation (11.79%), coal burning (7.47%), and solvent usage (6.97%). These findings underscore that reducing anthropogenic VOC emissions and strengthening controls on the related sources could provide a scientifically robust strategy for mitigating ozone pollution in Kunming. Full article

As a crucial manifestation of ecosystem water regulation and supply functions, water conservation plays a vital role in regional ecosystem development and sustainable water resource management. This study investigates nine major Chinese river basins (Songliao, Haihe, Huaihe, Yellow, Yangtze, Pearl, Southeast Rivers, Southwest Rivers, and Inland Rivers) through integrated application of the InVEST model and geographical detector model. We systematically examine the spatiotemporal heterogeneity of water conservation capacity and its driving mechanisms from 1990 to 2020. The results reveal a distinct northwest–southeast spatial gradient in water conservation across China, with lower values predominating in northwestern regions. Minimum conservation values were recorded in the Inland River Basin (15.88 mm), Haihe River Basin (42.07 mm), and Yellow River Basin (43.55 mm), while maximum capacities occurred in the Pearl River Basin (483.68 mm) and Southeast Rivers Basin (517.21 mm). Temporal analysis showed interannual fluctuations, peaking in 2020 at 130.98 mm and reaching its lowest point in 2015 at 113.04 mm. Precipitation emerged as the dominant factor governing spatial patterns, with higher rainfall correlating strongly with enhanced conservation capacity. Land cover analysis revealed superior water retention in vegetated areas (forests, grasslands, and cultivated land) compared to urbanized and bare land surfaces. Our findings demonstrate that water conservation dynamics result from synergistic interactions among multiple factors rather than single-variable influences. Accordingly, we propose that future water resource policies adopt an integrated management approach addressing climate patterns, land use optimization, and socioeconomic factors to develop targeted conservation strategies. Full article

As major countries around the world have successively proposed the construction goal of “Beautiful National Land Space”, how to effectively integrate urban design with spatial control in specific geographical environments and use urban design to achieve efficient spatial control has become a new research trend. The process of planning the national territory is constrained by the legal framework, involving multiple planning stages and multiple stakeholders. In an ideal state, these planning stages and stakeholders should coordinate with each other, but during the actual implementation of the plan, these factors are often not coordinated enough, making it difficult for the plan to play a role. In this study, Mile City in Yunnan Province, a representative city in the unique intermontane basin area of Southwestern China, was used as a case to explore how to use urban design methods in territorial spatial planning to achieve more efficient spatial control. This study provides scientific support for establishing an indicator control system for urban design methods by combining multiple data collection methods such as text analysis, image analysis, and interview methods. The distinctive features of Mile City have been further enhanced by optimizing its spatial layout through urban design, and it has been scientifically integrated into the territorial spatial planning system. The results indicate that the successful implementation of urban design highly relies on the reform willingness of local governments, clear control frameworks, and the coordinated integration of regional ecological resources and landscape features. This study proposed a set of urban design control methods suitable for intermontane basin-type cities and formed a comprehensive control framework including city, town, and landscape. In addition, it will provide methodological support and references for improving the scientific management of “Beautiful Land” in the special geographical environment of Southwest China. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

Soil erosion is a significant environmental challenge in Southwest China, influencing regional ecological security and sustainability. This study investigates the spatiotemporal evolution, driving mechanisms, and future projections of soil erosion in Southwest China, with a focus on the period from 2000 to 2023. The RUSLE model was used to analyze the spatiotemporal variation of soil erosion intensity over the 23-year period in Southwest China. The XGBoost and SHAP models were then employed to identify and interpret the driving factors behind soil erosion. These models revealed that precipitation, temperature, vegetation cover, and land use change were the primary drivers of soil erosion in the region. Finally, future soil erosion risks were projected for 2030, 2040, and 2050 under three climate scenarios (SSP119, SSP245, and SSP585) based on the CMIP6 climate model. The results suggest that (1) the analysis of soil erosion in Southwest China from 2000 to 2023 reveals a significant decline in soil erosion intensity, with a 58.16% reduction in average erosion intensity, from 4.23 t·ha⁻¹·yr⁻¹ in 2000 to 1.77 t·ha⁻¹·yr⁻¹ in 2020. The spatial distribution of erosion in 2023 showed that 90.9% of the region experienced slight erosion, with only 4.56% of the area facing moderate to severe erosion. (2) Natural factors, particularly elevation and precipitation, are the primary drivers of soil erosion. Regions with higher elevations and greater precipitation are more susceptible to soil erosion, particularly on steep slopes with shallow soil layers. Human activities, including GDP growth, land use patterns, and population density, also significantly influence soil erosion dynamics, with higher GDP levels and increased urbanization correlating with elevated erosion risks. The interaction between natural and socioeconomic factors demonstrates a complex relationship in soil erosion processes. (3) By 2050, soil erosion intensity in southwestern China is projected to increase overall, with the most significant increase occurring under the SSP585 scenario. The spatial distribution of soil erosion will largely maintain current patterns, with high-erosion areas concentrated in the northwest and low-erosion areas in the southeast. Areas experiencing mild erosion are expected to decrease, while moderately eroded regions will expand. Projection results suggest that increased precipitation and extreme weather events will lead to the most severe soil erosion in high-altitude regions, particularly in western Sichuan. Our historical assessments and future forecasts suggest vegetation conservation, rainfall monitoring, and restoration of western Sichuan in southwest China are critical for future erosion control and regional ecological security in southwest China. Full article

Urbanization always leads to increasing challenges to the groundwater resources in karst regions due to intensive land use, infrastructure development, and the rapid transmission potential of pollutants. This study proposed an improved groundwater vulnerability assessment (GVA) framework by modifying the widely used COP (Concentration of flow, Overlying layers, and Precipitation) model, through the integration of three additional indicators: urban underground rail transportation (UURT), land use and cover (LULC), and karst development (K). Guiyang, a typical urbanized karst city in southwest China, was selected as the case study. The improved COP model, namely the COPK model, showed stronger spatial differentiation and a higher Pearson correlation coefficient ($r$) with nitrate concentrations ($r$ = 0.4388) compared to the original COP model (R = 0.3689), which validates the effectiveness of the newly introduced indicators. However, both R values remained below 0.5, even after model modification, suggesting that intensive human activities play a role in influencing nitrate distribution. The pollution load index (PI) was developed based on seven types of pollution sources, and it was integrated with the COPK vulnerability index using a risk matrix approach, producing a groundwater risk map classified into five levels. Global Moran’s I analysis (0.9171 for COP model and 0.8739 for COPK model) confirmed strong and significant spatial clustering patterns for the two models. The inclusion of UURT and LULC improved the model’s sensitivity to urban-related pressures and enhanced its capacity to detect local risk zones. It is a scalable tool for groundwater risk assessment in urbanized karst areas and offers practical insights for land use planning and sustainable groundwater management. Full article

The Huaihe River Basin stands as a vital grain-producing base in China. Predicting the dynamic evolution of its carbon storage (CS) is of great theoretical value and practical significance for maintaining regional ecological security, guaranteeing food production capacity, and coping with climate change. This study established a multi-dimensional analysis framework of “scenario simulation–reservoir assessment–value quantification”. Using a sample of 195 cities, the PLUS-InVEST-GIS method was combined to explore the overall CS, spatial differentiation, and value changes in future scenarios. The results indicate that the following: (1) From 2000 to 2020, CS kept on declining, with cultivated land and forest land being the dominant carbon pools, accounting for over 86% of the total CS. (2) From a “city–grid–raster” perspective, the spatial pattern of high-value hot spots of CS remained stable, and the overall pattern remained unchanged under multi-scenario simulation, yet the overall carbon sink center of gravity shifted to the southwest. (3) The top five driving factors are elevation, slope, NDVI, GDP per capita, and population density, accounting for 77.2% of the total driving force. (4) The carbon sequestration capacity at the county scale continued to weaken, and the overall capacity presented the following order: 2035 Farmland protection scenario (FPS) > 2035 Natural development scenario (NDS) > 2035 Urban development scenario (UDS). The resulting carbon economic losses were USD 2.28 × 10⁸, 4.57 × 10⁸, and 6.90 × 10⁸, respectively. The research results will provide scientific land use decision-making support for the realization of the “double-carbon” goals in the Huaihe River grain-producing area. Full article

As urbanization progresses at an accelerating pace, the depletion of natural resources and environmental degradation are becoming increasingly severe. Constructing ecological security patterns (ESPs) has become a crucial strategy for mitigating environmental stress and promoting sustainable social development. Currently, the methods for constructing ESPs remain under exploration. Particularly, in the identification of ecological sources, insufficient emphasis has been placed on trade-offs among ecosystem services (ESs). This study focuses on Liaoning Province, situated in China’s northeast revitalization area—a region with a developed heavy industry and abundant ecological resources. The InVEST model was employed to assess ESs, and the ordered weighted average (OWA) method was utilized to identify ecological sources. By integrating both natural and social factors, the ecological resistance surface was constructed, and circuit theory was applied to determine ecological corridors, ultimately leading to the development of an ESP. The results show that (1) between 2010, 2015, and 2020, water yield continued to increase, habitat quality continuously declined, soil conservation tended to decrease and then gradually increase, and carbon storage tended to increase and then decrease. The four ESs show similar spatial features, characterized by elevated levels in the eastern and western areas and a comparatively reduced level in the central region; (2) a total of 179 ecological sources were identified, covering 26,235.34 km². The overall distribution showed a concentration in the east, with a fragmented and dispersed pattern in the southwest. The identification of 435 ecological corridors, with an overall length totaling 8794.59 km, resulted in a network-like distribution pattern. Additionally, 65 ecological pinch points and 67 barrier points were identified; and (3) a “four zones, three corridors, and two belts” pattern of ecological protection and restoration has been proposed. The findings offer valuable insights for Liaoning Province and other rapidly developing regions facing escalating environmental pressures. Full article

An urban–rural–natural imbalance is evident; investigating the spatiotemporal evolution of the transitional geo-space (TG) between them facilitates the integration of urban–rural land use planning. In this study, we proposed a complex system model to explore the interactive dynamics between the social–economic systems and natural ecosystems of Changning County, Southwest China, with the TG being identified and classified across the two systems. Based on a three-dimensional “direction–speed–pattern” framework, we further quantified production–living–ecological space (PLE) changes and examined the impacts of these changes on the TG from 2000 to 2022. The results are as follows: (1) The TG was classified into five categories that were stratified according to the coupling intensity and orientation of the socioeconomic system and natural ecosystems in Changning County. (2) The transition type with the most complex socio-ecological coupling was the type of semi-socioeconomic process–semi-natural ecological process, occupying 32.6% (309.4 km²) of the county’s total area in 2000 and demonstrating the most pronounced spatial dynamics, exhibiting a reduction of 78.6 km² during the study period. (3) Negative impacts on TG dynamics were observed for the conversion of ecological space into agricultural production space (p < 0.01; R² > 0.24) and the dynamic degree of PLE transformations (p < 0.01; R² > 0.13). (4) The impacts of trends in PLE on the TG varied significantly across temporal phases, whereas the CONTAG index exhibited consistently non-significant effects throughout all study periods. This study provides a new insight into understanding the optimization of spatial development patterns in urban–rural–natural regions and offers theoretical support for the governance of national land space and high-quality economic and social development in mountainous areas. Full article

Optimizing the allocation of basic educational facilities in mountainous rural areas is important for narrowing the education gap between urban and rural areas, constructing high-quality regional education systems, and achieving sustainable education development. This paper considered preschool, primary, and secondary schools in Qixingguan District, which is located in a mountainous area of China, using vector data of rural residential areas and educational facility points as a source of information on supply and demand. The study combined travel modes and acceptable time of rural school-age population, and applied the Gaussian two-step mobile search method to calculate the level of accessibility of basic educational facilities at the scale of residential areas. Location optimization and scale optimization models were used to determine the optimal location and service qualities for basic educational facilities. Our results yielded three main conclusions. First, the spatial pattern for the distribution density and accessibility of basic educational facilities in Qixingguan differed at all stages, but all of them showed a strong orientation toward the central urban area. Service capacity in each stage tended to extend toward the northeast and southwest, except for a certain orientation toward the central urban area. Second, the main reason for the low spatial accessibility of schools was that the density and service capacity of the available schools did not align with the distribution of the school-age population. Third, after optimizing for location and service capacity, schools at all stages shifted to the northeast of Qixingguan, which reduced the difference in service capacity between schools and improved the accessibility and balance of schools in the northeast and southwest. Full article

Landslides pose a significant threat to the safety and stability of settlements in karst regions worldwide. The long-standing tight balance state of settlement funding and infrastructure makes it difficult to allocate disaster prevention resources effectively against landslide impacts. There is an urgent need to fully leverage the landscape resources of karst settlements and develop landslide risk prevention strategies that balance economic viability with local landscape adaptability. However, limited research has explored the differential resilience characteristics and patterns of landslide disaster risk and settlement landscapes from a spatial coupling perspective. This study, based on landslide disaster and disaster-adaptive landscape data from a typical karst province in China, employs the frequency ratio-random forest model and weighted variance method to construct landslide disaster risk (LDR) and disaster-adaptive landscape (DAL) base maps. The spatial characteristics of urban, urban–rural transition zones, and rural settlements were analyzed, and the resilience differentiation and driving factors of the LDR–DAL coupling relationship were assessed using bivariate spatial autocorrelation and geographical detector models. The key findings are as follows: (1) Urban and peri-urban settlements exhibit a high degree of spatial congruence in the differentiation of LDR and DAL, whereas rural settlements exhibit distinct divergence; (2) the Moran’s I index for LDR and DAL is 0.0818, indicating that urban and peri-urban settlements predominantly cluster in H-L and L-L types, whereas rural settlements primarily exhibit H-H and L-H patterns; (3) slope, soil organic matter, and profile curvature are key determinants of LDR–DAL coupling, with respective influence strengths of 0.568, 0.555, and 0.384; (4) in karst settlement development, augmenting local vegetation in residual mountain areas and parks can help maintain forest ecosystem stability, effectively mitigating landslide risks and enhancing disaster-adaptive capacity by 6.77%. This study helps alleviate the contradiction between high LDR and weak disaster-adaptive resources in the karst region of Southwest China, providing strategic references for global karst settlements to enhance localized landscape adaptation to landslide disasters. Full article

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA is particularly evident in remote mountainous regions. However, the rugged terrain and highly fragmented cropland pose significant challenges in mapping abandoned cropland with high precision using remote sensing technology. Moreover, CA is the result of multi-level factors, yet previous studies have primarily analyzed its driving factors from a single level, leading to a lack of comprehensive understanding of the underlying mechanisms. We took Sichuan Province, located in the mountainous regions of Western China, as a case study, utilizing satellite-derived high-precision CA maps to reveal the spatiotemporal patterns of CA. Additionally, we employed hierarchical linear model to explore the determinants of CA and their interactions at both county and municipal levels. The results indicate that the CA rate decreased continuously from 6.75% in 2019 to 4.47% in 2023, with abandoned cropland exhibiting significant spatial clustering. High-value clusters were predominantly concentrated in the western mountainous areas, and hotspots of CA exhibited a general migration trend from the northeast to the southwest. Furthermore, we found that CA is influenced by multi-level factors, with 61% and 39% of the variance in CA being explained at the county and municipal levels, respectively. The agglomeration index of cropland (AI) is a key determinant at the county level, with the Digital Elevation Model (DEM) and the distance to roads also playing significant roles. At the municipal level, urbanization rate and the proportion of non-agricultural employment (PNAE) are dominant factors, and an increase in PNAE weakens the negative impact of AI on CA rates. To curb CA in mountainous areas, we recommend implementing land consolidation projects, improving rural land transfer markets, and strengthening legal mechanisms to combat CA. Our study has broad application prospects, providing critical support for assessing the ecological and environmental consequences of CA and exploring the potential of reutilizing abandoned cropland for food production, bioenergy, and carbon sequestration. Full article

Equilibrating the supply and demand for ecosystem services (ESs) is essential for sustainable development. Nonetheless, elements like policy modifications, land utilization, and climate change are profoundly transforming the dynamics of ecosystem service supply and demand (ESSD). As a result, there is an imperative necessity to methodically evaluate and predict these alterations by including both social and environmental elements. This study utilized the Henan region of the Yellow River Basin (HYRB) as a case study to forecast alterations in the supply and demand for three ESs—water production (WY), carbon storage (CS), and food production (FP)—under three scenarios for 2030 and 2050, grounded in the SSP-RCP framework. We further evaluated the supply–demand equilibrium at both grid and county degrees. The results indicate the following key findings: (1) From 2020 to 2050, there are significant spatial differences in the supply and demand of these services. While the supply of CS and FP exceeds demand, the supply of WY falls short. (2) The supply–demand ratios for WY and CS are projected to decline under all scenarios, whereas FP is expected to continue growing. Surplus areas for WY and CS are aggregated in the northwest, southwest, and central areas, while FP surpluses are found in the eastern and northern plains. Deficits for all three services are primarily located in urban areas. (3) The dominant spatial patterns of supply–demand matching also vary. WY and CS exhibit high–low agglomeration patterns, particularly in the northwest and southwest mountain regions, while FP shows low–low agglomeration, mainly in the southwest and northwest mountain areas. These findings enhance comprehension of the dynamics of ESSD, serving as a foundation for environmental preservation and sustainable advancement in the Yellow River Basin, China. Full article

As urbanization accelerates globally, preserving and developing historical cultural districts is increasingly critical, especially in areas with unique historical and cultural value. To understand the development of urban construction and the diachronic and spatial patterns of development, this paper focuses on Guiyang, a key transportation hub in Southwest China connected to Southeast Asia. It examines the historical districts from four representative periods: the early Ming Dynasty (1413–1420), the early Qing Dynasty (1616–1626), the Republican era (1912–1949), and the 1980s (1980–1990). Employing complex network analysis, the study investigates the changes in the connectivity characteristics of construction land and road networks. Key findings reveal: (1) Stability: The construction land networks stability decreased steadily from the early Ming period to the 1980s, whereas the road network density exhibited a wave-like decline. (2) Centrality: The construction land networks centrality decreased linearly, and the road network density exhibited a wave-like decrease. (3) Vulnerability: Both networks showed increased vulnerability, with fluctuations in the road network during the early Qing period, but generally reduced vulnerability. The analysis also indicates that changes in the connectivity of Guiyang’s historical district construction land and road networks are influenced by shifts in social structures, improvements in productivity, and the physical geography of the area. In mountainous cities with limited terrain, urban forms have transitioned from single-center aggregation to multi-center aggregation, and areas where administrative expansion is not feasible have adopted compact spatial development strategies. The application of complex network analysis has proven effective in urban spatial studies, revealing that changes in construction land and road networks reflect multifaceted internal transformations in society, politics, economy, military, and culture, significantly impacting the formation of a diverse yet unified national identity. Based on these findings, this paper offers recommendations for the planning and development of mountainous cities globally. Full article