Search Results (1,021)

Grassland lake basins are mostly located in arid and semi-arid regions and represent typical ecologically fragile zones. As a representative inland lake in the cold and arid region of northern China, Hulun Lake serves as a crucial node for maintaining the ecological balance of the Hulunbuir grassland. Studying its ecological carrying capacity is particularly key to implementing the philosophy of a holistic approach to the management of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Based on data from 2018 to 2024 across four cities (banners, districts) in the Hulun Lake basin, this study constructs an evaluation system to measure ecological carrying capacity across three dimensions—ecosystem support, human activity pressure, and socio-economic response—using the Pressure–State–Response (PSR) model. Spatial analysis and geodetector methods are employed to explore its spatiotemporal differentiation and influencing factors. The findings are as follows: (1) The ecological carrying capacity in the Hulun Lake basin exhibits a significant spatial differentiation pattern, characterized by a gradient of “high in the east, low in the west; high in pastoral areas, low in urban areas.” (2) The overall trend in ecological carrying capacity shows a slow increase amid fluctuations, but the carrying capacity level remains relatively low. (3) The core driving forces of ecological carrying capacity primarily stem from the dimensions of population quality and infrastructure, while the direct influence of agricultural production is relatively limited. (4) Transportation infrastructure plays a strongly influential role as a driving mechanism of ecological carrying capacity in the Hulun Lake basin. Its synergy with factors such as education, information, and industry significantly affects both the ecosystem support capacity and the socio-economic responses of the basin. This study provides a reference for ensuring the ecological security of the Hulun Lake basin. Full article

Achieving sustainable development requires moving beyond pollution metrics to holistic measures, such as the load capacity factor (LCF), which balances ecological demand and supply. While recent studies have provided important insights into the determinants of LCF in OECD countries, further research is needed to incorporate additional determinants and updated estimation approaches. This study addresses this gap by examining the impacts of financial innovation, forestry, urbanization, population, and economic growth on the LCF in Organization for Economic Cooperation and Development (OECD) economies from 1990 to 2023. Using second-generation panel econometric methods, including tests for cross-sectional dependence, slope heterogeneity, second-generation unit roots, and cointegration techniques, this paper confirms a stable long-run relationship among the variables. The core analysis applies the method of moments quantile regression to uncover the heterogeneous effects across the LCF distribution. The results indicate that financial innovation consistently enhances the ratio of biocapacity to ecological footprint. In contrast, economic growth and urbanization exert significant negative pressure on the LCF, whereas population size shows a uniformly detrimental effect. Forestry has a positive but less pronounced influence. Robustness checks using fully modified ordinary least squares, dynamic ordinary least squares, and panel-corrected standard errors confirm these results. The present study concludes that targeted financial innovation and stringent urban demographic policies support OECD nations in improving ecological balance and reducing ecological deficits. Full article

With the rapid expansion of rural tourism in China, high-quality development has become a key concern for academics and policymakers. Existing studies have focused primarily on economic and industrial growth, with limited attention paid to development quality from the perspective of resident well-being. Using panel data from 30 Chinese provinces from 2012 to 2022, this study establishes a multidimensional evaluation framework for high-quality rural tourism. We employed the entropy weight method, Theil index, and quadratic assignment procedure analysis to examine its level, regional differences, and driving factors. The findings revealed that: (1) the overall level of rural tourism development remained relatively low but rose steadily from 0.064 (2012) to 0.150 (2022) (134.38% cumulative growth), driven by supply-side improvements and demand-side expansion. (2) Pronounced regional inequalities existed: eastern provinces had higher overall levels but larger internal gaps, whereas central/western provinces had lower overall levels but smaller internal differences, with intra-regional disparities accounting for over 66% of the national inequality. (3) The tourism market and transportation were universal key drivers, but the underlying mechanisms differed: the ecological environment exerted greater influence in the east, while public services and living standards were more critical in the central/western regions. By incorporating resident well-being into a systemic analytical framework, this study reconceptualizes high-quality rural tourism as an adaptive socio-ecological system shaped by multilevel interactions among the economy, society, and the environment. The results provide empirical evidence and systemic governance insights for promoting balanced and sustainable rural tourism development. Full article

Microbial contamination in aquatic environments poses severe threats to aquaculture sustainability, ecological balance and public health. Traditional culture-based detection methods, while standardized, are time-consuming and labor-intensive, often failing to meet the urgent need for rapid on-site monitoring required to prevent disease outbreaks and manage water quality effectively. By integrating latest research advances (2020–2025), this study reviews advances in rapid detection technologies for aquatic microorganisms, including the evolution of nucleic acid amplification strategies, with a focused comparison of the analytical sensitivity and field deployability of quantitative polymerase chain reaction (qPCR) and mainstream isothermal amplification techniques (loop-mediated isothermal amplification, LAMP; recombinase polymerase amplification, RPA). Furthermore, this study reports on the emergence of Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein (Cas) systems as next-generation diagnostic tools, highlighting their integration with microfluidic Lab-on-a-Chip (LOC) platforms to achieve attomolar sensitivity. We also consider the application of portable nanopore sequencing for real-time pathogen identification and the growing role of Artificial Intelligence (AI) in analyzing complex diagnostic datasets. Advanced molecular methods have achieved significant reductions in time consumption—from days to less than one hour—while challenges regarding sample preparation and environmental matrix inhibition remain. The future of aquatic monitoring lies in integrated, automated systems that combine the specificity of CRISPR-Cas diagnostics with the connectivity of IoT-enabled biosensors. Comparative analysis indicates that isothermal amplification methods (LAMP, RPA) coupled with CRISPR-Cas systems offer the optimal balance of sensitivity, speed, and field deployability for point-of-care aquaculture diagnostics, while qPCR/dPCR remain indispensable for quantitative regulatory applications. We propose a structured technology selection framework to guide researchers and practitioners in choosing appropriate detection modalities based on specific sensitivity, cost, throughput, and deployment requirements. Full article

The rapid development of metropolitan areas means that their spatial patterns must be reconstructed and brings a series of urban problems such as traffic congestion and imbalance among transportation facilities. As the skeleton of the comprehensive transportation network, the planning of passenger corridors in metropolitan areas has a positive impact on the integrative development of urban spaces and transportation systems. The identification of passenger corridors is the basis for the optimization of the configuration and organization of transportation facilities. In this paper, passenger transportation modes were distinguished through a multilayer network. Considering the technological and economic characteristics of each mode synthetically, an improved method for identifying passenger corridors was proposed. First, a multilayer network was constructed based on the passenger transportation facilities network in a metropolitan area to distinguish between different transportation modes. Based on the traditional importance degree model of nodes, an importance degree model of routes was constructed by considering transportation modes, passenger demand, and transportation costs. Through qualitative judging using regional planning, supported by quantification according to the importance degree of routes, passenger corridors in the chosen metropolitan area were identified and divided into primary and secondary corridors. Suzhou metropolitan area was studied as an example. Identification results for three transverse corridors and two longitudinal corridors were obtained after analysis and calculation, verifying the availability of the method. The study can contribute to the balance of transportation supply and demand, realize the intensive use of transportation facilities, and promote the sustainable development of metropolitan transportation systems. In particular, the proposed method provides a reference for the rational optimization of transportation facility configuration within passenger corridors in metropolitan development areas, facilitating the formation of efficient passenger transport organization systems and compact, transit-oriented land use patterns by improving the coordination between passenger corridors and ecological spaces. Full article

Land use change detection (LUCD) serves as a crucial technical cornerstone for natural resource management and ecological environment monitoring, playing an indispensable role in advancing the modernization of national governance capacities. Nonetheless, severe interference from radiometric variations on feature representation readily induces spurious changes and thus a high false alarm rate. Additionally, the challenge of balancing discriminative feature extraction and fine-grained contextual modeling leads to fragmented change regions and missed detection. To address these issues and eliminate the reliance on annotated samples, a novel framework is proposed for unsupervised LUCD, integrating superpixel-based deep feature analysis with a dense conditional random field (CRF). Firstly, relative radiometric correction and band-wise maximum stacking fusion are performed on the bi-temporal images. A simple non-iterative clustering (SNIC) algorithm is adopted to generate homogeneous superpixels with cross-temporal consistency. Then, a deep feature coupling mining mechanism is introduced to implement spatial–spectral feature extraction and in-depth parsing of invariant semantic information. Meanwhile, the difference confidence map based on dual features is constructed using superpixel-level discriminant vectors to enhance the separability. Finally, leveraging homogeneous units with spatial correspondence, a task-specific redesign of a global optimization model is established to achieve the precise extraction of change regions, which incorporates difference confidence, spatial adjacency relationship, and cross-temporal feature similarity into the dense CRF. The experimental results demonstrate that the proposed method achieves an average overall accuracy of over 90% across all datasets with excellent comprehensive performance, striking a well-balanced trade-off in practical applicability. It can effectively suppress salt-and-pepper noise, significantly improve the recall rate of change regions (maintaining at approximately 90%), and exhibit favorable superiority and robustness in complex land cover scenarios. Full article

Analyzing the dominant drivers of the Food-Water-Land-Ecosystem (FWLE) nexus in the future is important for improving sustainable development in dryland ecosystems. However, the future trajectories of food–water–land–ecosystem interactions in typical drought-prone regions remain poorly understood. To address this gap, this study coupled the Gray Multi-Objective Programming with Patch-generating Land Use Simulation (GMOP-PLUS) model and applied spatial analysis methods (including longitudinal and zonal statistical analysis, trade-off synergy analysis, and redundancy analysis) to examine the spatiotemporal differentiation patterns of the FWLE nexus in Xinjiang under different development scenarios. Over the past two decades, water yield in Xinjiang’s agricultural landscapes has declined by 57.4%, primarily due to land-use and land-cover changes. Under the 2030 sustainable development scenario, a custom optimization developed via the GMOP model that balances economic and ecological objectives, crop production and habitat quality are projected to increase by 47.9% and 55.1%, respectively. Moreover, redundancy analysis results indicate that the driving contribution of precipitation on the FWLE nexus is expected to reach 76.9% by 2030. These findings provide a clear delineation of priority spatial units for improvement within Xinjiang agro-ecosystem and offer a strategic pathway for balancing ecological conservation and economic development. Full article

Adaptive reuse of industrial heritage from a tourism perspective presents a complex design challenge requiring a balance between heritage preservation, functional innovation, and diverse stakeholder expectations. However, current practices often face issues such as ambiguous demand interpretation and a disconnect between design generation and systematic evaluation. Addressing these limitations, this paper proposes and illustrates a human–machine collaborative design paradigm that integrates generative AI into a closed-loop process of “demand analysis–intelligent generation–comprehensive evaluation.” The method first employs Kansei Engineering and the KANO model to qualitatively extract and quantitatively prioritise heterogeneous user needs, translating subjective perceptions into structured design constraints and optimisation objectives. Next, these needs are encoded as text prompts to drive targeted spatial exploration by the generative AI tool Nano Banana AI. Finally, the TOPSIS method is applied for multi-criteria performance evaluation and solution selection. A case study of Shanghai Libo Brewery suggests that this paradigm can enhance design efficiency and show potential to outperform traditional methods across dimensions such as historical preservation, public accessibility, ecological integration, social inclusivity, and formal innovation. The research offers a quantifiable and systematically documented intelligent design methodology for industrial heritage renewal, while acknowledging the exploratory nature of the generative phase. Furthermore, it provides a visitor-demand-driven innovation pathway for developing industrial heritage tourism destinations, thereby potentially enhancing cultural experiences and tourism appeal at heritage sites. This research illustrates a move from an experience-driven paradigm toward a data- and value-driven approach, contributing theoretical methodologies to the intersection of cultural tourism and artificial intelligence. Full article

Coastal cities are located at the critical interface of land–sea interaction, and scientifically assessing their ecological importance is essential for identifying conservation priority areas. Existing assessments focus primarily on static function while neglecting dynamic system processes and resilience characteristics. To address this limitation, this study developed an innovative “State-Interaction-Resilience” (SIR) assessment framework. It integrates ecosystem services (state), ecological connectivity and network supply-demand relationships (interaction), and social-ecological system adaptive capacity (resilience) and incorporates differentiated weighting based on the unique “sea–land gradient” pattern of coastal zones. Using Dongying City in the Yellow River Delta as a case study, the results show the following: (1) The SIR framework evaluation results demonstrate balanced and significant positive correlations with all dimensional indicators (r = 0.3~0.8), showing greater comprehensiveness and scientific validity than traditional evaluation methods, with 81% spatial agreement between identified extremely important areas and existing protected areas. (2) From 2000 to 2020, the overall ecological importance of Dongying City showed an upward trend, with the proportion of extremely important areas significantly increasing from 6.03% to 10.24%, while maintaining a stable spatial gradient pattern of “high along the coast, low inland”. (3) The improvement in ecological importance in coastal core areas mainly resulted from state improvement and resilience enhancement driven by restoration projects such as “aquaculture retreat and wetland restoration”, while inland areas were constrained by both habitat fragmentation and ecological supply-demand mismatch. This study confirms that the SIR framework can accurately capture the spatial heterogeneity of coastal zones. The proposed “core protection-corridor restoration-function enhancement” hierarchical and zonal spatial governance strategy provides scientific evidence and actionable spatial guidance for coastal territorial spatial planning, ecological protection redline optimization, and targeted ecological restoration. Full article

Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The objective of this scientific research is to address these limitations by proposing a deep learning-based framework for simulating the future distribution of these three spaces. Utilizing the Unet++ model and integrating empirical data sources including multi-period remote sensing land-use mapping and prefecture-level socioeconomic statistical data, the framework predicts regional spatial patterns for the year 2030. Empirical results from the Yangtze River Economic Belt demonstrate that the model achieves high precision in large-scale spatial forecasting (with an average test accuracy of 99.32%) and effectively captures non-linear evolutionary characteristics. Predictions across various growth scenarios reveal that a moderate socioeconomic growth rate facilitates ecological preservation; controlling the expansion of urban space to approximately 20% by 2030 can prevent excessive resource depletion and regional imbalances. Consequently, it is recommended to implement the construction land increment targets outlined in current spatial planning to achieve a balance between economic growth and ecological protection. Full article

Although tourism is increasingly seen as a key component of sustainable regional development and economic diversification, its extraordinary expansion raises governance and environmental issues at the local level. The current study assesses the influencing factors of inbound tourism demand to Saudi Arabia, a strategic empirical study due to its rapid and ambitious transformation under Vision 2030. This national strategy is designed to cultivate diverse tourist destinations, including coastal eco-resorts, mountain nature escapes, and urban cultural hubs. The unique sustainability hurdles in each area make the Kingdom a prime location for analyzing the development of regional tourism. This research focuses on the vibrant interfaces among sustainable practices, logistical efficiency, perceptions of safety and uncertainty, and macroeconomic environments that shape the Kingdom’s competitiveness as a tourism region. The study draws several beneficial findings using balanced panel data of 16 origin countries during the period of 2009–2023 and is assessed using a dynamic panel Generalized Method of Moments model. The findings state extensive perseverance within tourism flows, such that past arrivals significantly enable simultaneous inflows. Inbound tourism is strongly and favourably influenced by destination-side factors, particularly logistical performance, human rights conditions, and Saudi Arabia’s socioeconomic prosperity. In a similar vein, the demand for outward travel is strongly reinforced by origin-country prosperity. But travel expenses attenuate, environmental pressures and political risk reduce arrivals, and relative prices and pandemic uncertainty play a negligible role. The findings highlight the need to upgrade the country’s logistics infrastructure, enhance rights protection and governance, integrate sustainable practices, and capitalise on prosperity to make Saudi Arabia a desirable travel destination by Vision 2030. A key contribution of this study is to demonstrate how infrastructure, environmental stewardship, and institutional quality shape a region’s tourism attractiveness. The study illustrates how sustainability must be incorporated into regional-specific strategies to balance economic goals with ecological and social imperatives, providing a framework for other countries interested in sustainable tourism. Full article

A vital region for China’s water resource storage and ecological balance maintenance, the Yangtze River Basin is strategically significant for maintaining regional water security and promoting long-term social and economic development. Precipitation is the main driver of the hydrological cycle. In order to address current problems with the basin’s ecological environment and water supplies, comprehensive analyses of multi-source precipitation data are necessary. They provide an essential scientific basis for evaluating the sustainability of water resources in the Yangtze River Basin in the context of climate change. Most existing precipitation fusion studies utilize only a limited number of datasets and do not fully consider the independence among different data sources, which leads to less-than-ideal fusion accuracy and assessment metrics. This paper employs the Triple Collocation (TC) method to evaluate and fuse multiple precipitation datasets over a 19-year period from 2003 to 2021, with the aim of enhancing precipitation accuracy in the Yangtze River Basin. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data were found to have the highest accuracy among seven datasets, with a Correlation Coefficient (CC), Relative Bias (Rbias), and Root Mean Square Error (RMSE) of 0.907, −0.027, and 25.930 mm, respectively. The “MSWEP–PERSIANN–NOAH (MPN)” fusion was shown to be the best using the Multiplicative Triple Collocation (MTC) method in conjunction with cross-error analysis. Compared to MSWEP alone, it improved CC by 0.8% and decreased RMSE by 3.8%, with matching spatial-grid CC and RMSE improvements of 1.2% and 1.8%, respectively. Further spatiotemporal analysis of the fused data increase detection capabilities for short-term flood and waterlogging occurrences and provide better knowledge of basin water-resource status. Full article

The Northeast Black Soil Region is an important commercial grain production base in China. However, ecological issues such as black soil degradation and soil erosion pose direct threats to food security. Previous studies have mainly examined individual factors of black soil degradation. Few have integrated spatial thickness distribution with multi-dimensional ecological sensitivity. To address this gap, this study establishes an ecological sensitivity evaluation index system for Bayan County, located in the eastern Songnen High Plain. Based on a review of relevant literature, the system includes four dimensions: topography, climate, natural resources, and human activities. A combined Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was used to determine indicator weights. Compared with single-weighting methods, this approach balances expert judgment with data-driven variation. The results are as follows. (1) The thickness of the black soil layer in Bayan County ranges from 18 to 77 cm. Medium, thin, and thick layers account for 78.81%, 16.32%, and 4.87% of the area, respectively. The total black soil reserve is estimated at about 1.267 billion m³. (2) Among the primary indicators, natural resources have the highest weight (0.53). The five most important secondary indicators are the river buffer zone (0.14), NDVI (0.13), soil type (0.12), land use type (0.12), and road buffer zone (0.09). (3) The overall ecological sensitivity of the county is moderate, with a composite index ranging from 1.45 to 4.45. The proportions of extremely sensitive, highly sensitive, moderately sensitive, mildly sensitive, and insensitive areas are 10.79%, 25.51%, 28.95%, 24.23%, and 10.52%, respectively. These findings provide a scientific basis for ecological protection and black soil conservation. They also support the development of targeted, zone-specific management strategies in Bayan County. Full article

Understanding individuals’ connection to nature is crucial for promoting sustainable attitudes and behaviors. The environmental identity (EID) scale, widely used to assess this connection, plays a key role in environmental research; however, its cross-cultural application requires rigorous psychometric validation. Although the revised 14-item EID scale has demonstrated good reliability, questions remain regarding its dimensionality and the potential influence of acquiescence due to exclusively positive worded items. This study examined both issues in Portuguese samples. In Study 1, exploratory and confirmatory factor analyses were conducted to test the factorial structure. Results supported a two-factor model with correlated dimensions: Restorative Connection to Nature (RCN) and Ecological Identity (EI), rather than a strictly unidimensional solution. In Study 2 acquiescence was assessed by comparing the original version with a balanced version that included partially reverse-worded items. Item distributions, factor loadings, and reliability were analyzed. The balanced version did not improve control of acquiescence; instead, reversed-worded items showed weaker loadings, lower explanation variance, and method effects, suggesting increased measurement bias. Overall, the findings support the robustness of the revised 14-item EID scale in Portugal while indicating that environmental identity is better conceptualized as a bidimensional construct portraying both reflective connection and identity-based engagement with nature. The results also highlight the limitations of reverse-worded items as a strategy for reducing response bias in value-laden constructs. Full article

The inhibitory effect of vegetation on soil wind erosion along grassland highways in semi-arid regions has not been fully elucidated. In this study, the dry vegetation near S105 provincial highway in the Sangendalai area of Xilingol League, Inner Mongolia was selected for a wind tunnel test, and the vegetation coverage and porosity during the test were determined by using image processing methods. On this basis, a porous medium model of dry vegetation was established, and the two-phase flow of wind and sand was numerically simulated. The results show that: (1) The numerical simulation results are in good agreement with the wind tunnel observations, confirming the feasibility of using CFD to simulate wind erosion affected by vegetation along grassland highways in semi-arid areas. (2) The aerodynamic roughness of the grassland surface increases nonlinearly with the increase of vegetation cover, and the increase of aerodynamic roughness is more obvious when the vegetation cover is more than 16% in the scope of this study. (3) Vegetation changed the typical jump-dominated wind–sand flow structure on the bare ground surface, showing a significant interception and attenuation effect of vegetation, which was manifested by the reduction of sand accumulation at the wind outlet and the increase of deposition within the vegetated area, thus effectively inhibiting the wind erosion process. The results of the study provide methodological references and a theoretical basis for the study of wind erosion along grassland highways in semi-arid regions and help to promote the sustainable development and ecological balance of grassland ecosystems in semi-arid regions. Full article