Land, Volume 14, Issue 5 (May 2025) – 31 articles

In the current conditions of development of the country’s market economy, the methodological support for cadastral land valuation requires effective modernization and improvement of the existing mechanisms for determining cadastral value for a fair distribution of land tax among landowners. In this regard, the aim of the study was to develop a methodology for taking into account the qualitative state of soils in the cadastral valuation of agricultural lands in the conditions of an active land market, as well as to modernize the method for taking into account the quality of soils within the framework of the income approach in the conditions of a depressed land market. The study was conducted based on a set of scientific methods: the analytical method was used to conduct an analysis of the scientific review of the problem area and to substantiate the relevance of the study, a cycle of laboratory experiments was conducted using mechanical and chemical analyses, the construction of thematic maps was carried out using the dispersion method, the regression modeling method was used to determine the cadastral value of garden plots, and the land rent capitalization method was used to calculate the cadastral value of agricultural land. Research results were as follows: Methodological recommendations were provided for taking into account the quality of soils in the form of an integral indicator of physical and chemical properties in the model for calculating the specific indicator of cadastral value (SICV) of garden and vegetable lands in the conditions of an active land market. The method of accounting for the qualitative state of soil fertility in the form of a weighted quality score of an agricultural land plot was modernized when determining the specific gross income within the framework of the land rent capitalization method used to calculate the SICV. Based on field work and laboratory experiments, current indicators of soil fertility status were obtained, and soil quality scores for Saint Petersburg were calculated. The possibility of using an integral indicator (soil quality score) as a cost factor instead of a large number of fertility status indicators was proven. Also, models for calculating the SICV of garden and vegetable plots were built for the conditions of an active land market, according to which the cadastral value of land plots in Saint Petersburg was calculated for subsequent land taxation. For agricultural lands, using the example of a land plot of a high-commodity agricultural enterprise (Leningrad Region), the cadastral value was also calculated using the proposed income approach method. The scientific significance of the study lies in the improvement of the methodological foundations of cadastral valuation, as well as the technology of taking into account the quality of soils when calculating the cadastral value. The practical significance of the study lies in the applicability of the results of soil quality assessment and models for calculating the SICV for land taxation; individual market valuation for lending, purchase, and sale; lease of agricultural land; and allocation of land plots on account of a land share. In the area of developing a set of melioration measures on agricultural lands, including the development and implementation of agricultural technologies and technical means to improve soil fertility, the results of laboratory studies to determine the physical and chemical properties of soils can be used. The obtained soil quality scores for Saint Petersburg are also applicable to identifying unused and degraded lands for their transfer to other types of use. Full article

Free trade zones are key regions experiencing rapid economic growth, urbanization, and a sharp increase in population density. During the development of free trade zones, these areas undergo drastic transformations in landscape types, large-scale urban construction, heightened resource consumption, and other associated challenges. These factors have led to severe landscape ecological risk (LER). Therefore, conducting comprehensive assessments and implementing effective management strategies for LER is crucial in advancing ecological civilization and ensuring high-quality development. This study takes Hainan Island (HI), China, as a case study and utilizes multi-source data to quantitatively evaluate land use and land cover change (LULCC) and the evolution of the LER in the study area from 2015 to 2023. Additionally, it examines the spatial patterns of LER under three future scenarios projected for 2033: a natural development scenario (NDS), an economic priority scenario (EPS), and an ecological conservation scenario (ECS). Adopting a spatiotemporal dynamic perspective framed by the “historical–present–future” approach, this research constructs a zoning framework for LER management to examine the temporal and spatial processes of risk evolution, its characteristics, future trends, and corresponding management strategies. The results indicate that, over an eight-year period, the area of built-up land expanded by 40.31% (504.85 km²). Specifically, between 2015 and 2018, built-up land increased by 95.85 km², while, from 2018 to 2023, the growth was significantly larger at 409.00 km², highlighting the widespread conversion of cropland into built-up land. From 2015 to 2023, the spatial distribution of LER in the study area exhibited a pattern of high-risk peripheries (central mountainous areas) and low-risk central regions (coastal areas). Compared to 2023, projections for 2033 under different scenarios indicate a decline in cropland (by approximately 17.8–19.45%) and grassland (by approximately 24.06–24.22%), alongside an increase in forestland (by approximately 4.5–5.35%) and built-up land (by approximately 23.5–41.35%). Under all three projected scenarios, high-risk areas expand notably, accounting for 4.52% (NDS), 3.33% (ECS), and 5.75% (EPS) of the total area. The LER maintenance area (65.25%) accounts for the largest proportion, primarily distributed in coastal economic development areas and urban–rural transition areas. In contrast, the LER mitigation area (7.57%) has the smallest proportion. Among the driving factors, the GDP (q = 0.1245) and year-end resident population (q = 0.123) were identified as the dominant factors regarding the spatial differentiation of LER. Furthermore, the interaction between economic factors and energy consumption further amplifies LER. This study proposes a policy-driven dynamic risk assessment framework, providing decision-making support and scientific guidance for LER management in tropical islands and the optimization of regional land spatial planning. Full article

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities. Full article

Achieving the United Nations 2030 Sustainable Development Goals (SDGs) is a critical global challenge. Ensuring the sustainable utilization of water resources has long been a key policy priority for the Chinese government, balancing economic growth with ecological conservation and advancing ecological civilization. Taking China’s sustainable development agenda innovation demonstration area Chenzhou as the object, this work focuses on SDG6 and examines the progress in sustainable water resource utilization from 2015 to 2022, evaluating three dimensions—water quantity, water environment, and water ecology. A comprehensive evaluation index system closely related to SDG6 was constructed to assess the sustainable development progress. Furthermore, the interactions between SDG6 and related SDGs were analyzed. The results show that (1) from 2015 to 2022, the SDG6 composite index has significantly increased over time, with the establishment of the demonstration area (2019–2022) more than twice compared to before (2015–2018), particularly in water environment and water quantity; (2) the SDG composite index and individual SDG indexes have shown a fluctuating upward trend, with an increase of about 89.74% after the establishment of the demonstration area (2019–2022) compared to before (2015–2018), with the most significant progress in the society dimension; and (3) there were significant synergy effects between the improvements in SDG6 and related SDGs. For each unit increase in SDG6, the overall level of related SDGs increased by 0.73 units, specifically, with particularly strong synergies between SDG2, SDG7, SDG9, and SDG11. This study not only provides scientific guidance for water resource management and policy optimization in Chenzhou and similar water resource-based cities but also offers valuable localized case studies, methodologies, and data to support the monitoring of urban sustainable development at a global scale. Full article

The accurate extraction of mountainous cropland from remote sensing images remains challenging due to its fragmented plots, irregular shapes, and the terrain-induced shadows. To address this, we propose a deep learning framework, SE-ResUNet, that integrates Squeeze-and-Excitation (SE) modules into ResUNet to enhance feature representation. Leveraging Sentinel-1/2 imagery and DEM data, we fuse vegetation indices (NDVI/EVI), terrain features (Slope/TRI), and SAR polarization characteristics into 3-channel inputs, optimizing the network’s discriminative capacity. Comparative experiments on network architectures, feature combinations, and terrain conditions demonstrated the superiority of our approach. The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. This study provides a scalable solution for mountainous cropland mapping, supporting precision agriculture and sustainable land management. Full article

The United Kingdom has had a series of unsuccessful attempts at securing land value capture in 1909, 1947, 1967, and 1975. The 1909 land duties would have taxed increases in land values irrespective of the source. The latter three combined public bodies, acquiring development land with levies on developers. For them, value uplift was limited to that arising from the granting of planning consent. None of the measures were fully implemented and were reversed by incoming governments. One of the key problems with value capture policies has been the lack of political consensus. Since 1979, consensus has developed around the use of three types of value capture instruments. Development corporations have been created for the regeneration of local areas by acquiring development land and improving it. Local authorities have been able to use planning obligations and community infrastructure levies to oblige developers to mitigate externalities. Governments have made it clear that these are not to be used as value capture devices and therefore are really applications of the polluter pays principle. Thirdly, there are national taxes that fall on increases in the value of land, namely, business rates, stamp duty land tax, capital gains tax, and inheritance tax. Full article

One question about historical grassland ecosystems in the Great Plains region of central North America is the percentage of tree cover overall and near major rivers, compared to current tree cover. Here, I assessed tree cover in reconstructions of historical grasslands in the eastern Great Plains, isolating tree cover adjacent to major rivers, and then compared historical land cover to current (year 2019) land cover. As an extension to supply information for the entire Great Plains region, I modeled historical cover. For the 28 million ha extent of the eastern Great Plains, historical land cover was 86% grasslands and 14% trees, but 57% grasslands and 43% trees within 100 m of rivers. Tree cover near rivers ranged from 5.4% to 90% for 15 large river watersheds, indicating that any amount of tree cover could occur near rivers at landscape scales. Currently, the overall extent was 3.6% herbaceous vegetation and 6.6% forested, with 82% crops and pasture and 8% development. Within 100 m of rivers, crop and pasture decreased to 44% of cover, resulting in 14% herbaceous cover and 38% forested cover. Current tree cover ranged from 6.2% to 66% near rivers in 15 watersheds, which was relatively comparable to historical tree cover (ratios of 0.6 to 1.5). Results generally were similar for combined tree and shrub cover modeled for the entire Great Plains. Variability, even at landscape scales of large watersheds, was the normal condition for tree cover in grasslands and riparian ecosystems of the Great Plains. In answer to the question about tree cover in historical grassland ecosystems in the eastern Great Plains, tree cover typically was about three-fold greater near rivers than tree cover throughout grasslands. Combined tree and shrub cover near rivers was more than two-fold greater than tree and shrub cover throughout the Great Plains. Riparian forest restoration, as a management practice to reduce streambank erosion, overall has been effective, as indicated by current tree cover (38% near rivers in the eastern Great Plains) comparable to historical tree cover (43% near rivers in the eastern Great Plains), albeit as measured at coarse landscape scales with dynamics in vegetation and river locations. As a next step, restoration of grassland vegetation and non-riparian wetlands likely will help reestablish infiltrative watersheds, augmenting riparian forest restoration. Full article

This study presents a spatial methodology for integrating climate change (CC) risks and ecosystem service (ES) assessments into strategic spatial planning, applied to the Metropolitan Plan of the Province of Rimini (Emilia-Romagna, Italy). The proposed approach combines IPCC-aligned climate vulnerability analysis with ecosystem service mapping based on the methodology developed by CREN. Climate risks, including urban heat islands, droughts, and urban floods, were assessed using satellite-derived indices such as Land Surface Temperature (LST), Vegetation Health Index (VHI), and hydraulic modeling. For ESs, nine key services were evaluated and mapped by integrating land use, forest cover, and habitat data with biophysical modulation factors (e.g., slope, carbon stock, infiltration capacity). The results highlight priority areas where climate adaptation and ecological functions converge, enabling targeted interventions. This integrated workflow offers a replicable and scalable planning tool to support evidence-based decision-making at the metropolitan level. Its adoption is recommended by other local and regional authorities to strengthen the climate and ecological responsiveness of spatial planning instruments. Full article

With the acceleration of urbanization, the accessibility and equity of urban green spaces have become crucial issues in urban planning and public health. In the context of the 15-min living circle, whether residents can conveniently reach parks within a walkable or bikeable time frame directly impacts their quality of life and social well-being. Traditional park accessibility evaluation methods, such as the G2SFCA, effectively measure accessibility but fail to fully account for the diversity of travel modes and the impact of regional disparities on equity. This study employs the TB-G2SFCA method, integrating the concept of the 15-min living circle, to analyze the equity of park accessibility in the Dianchi Lake ring area of Kunming under different travel modes. The findings reveal significant disparities in park accessibility for walking and cycling, particularly in suburban communities distant from the city center, where many areas cannot reach a park within 15 min. Although accessibility improves under driving and public transit modes, resources remain concentrated in well-connected areas, leaving peripheral regions with insufficient access. Compared to the traditional G2SFCA method, the TB-G2SFCA approach more accurately reflects spatial differences and equity issues across travel modes. This study suggests that future urban park planning should optimize resource allocation, improve transportation networks, and enhance park accessibility in peripheral areas—especially for walking and cycling—to achieve a more equitable and sustainable distribution of urban green spaces. Full article

Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. While trajectory analysis is critical, existing studies struggle to interpret multi-dimensional perception-preference data owing to spatiotemporal mismatches in multi-source datasets. This study adopted an improved Ward–K-medoids hybrid clustering algorithm to analyze 885 trajectory samples and 34,384 synchronized data points capturing emotional valence, cognitive evaluations, and dwell time behaviors via panoramic digital twins across three heritage sites (Du Fu Thatched Cottage, San Su Shrine, and Wangjiang Tower Park). Our key findings include the following: (1) Axial bimodal patterns: Type I high-frequency looping paths (27.6–68.9% recurrence) drive deep exploration, in contrast to Type II linear routes (≤0.5% recurrence), which enable intensive node coverage. (2) Layout-perception dynamics: single-axis layouts maximize behavioral engagement (DFTC), free-form designs achieve optimal emotional-cognitive integration (WTP), and multi-axis systems amplify emotional-cognitive fluctuations (SSS). (3) Spatial preference hierarchy: entrance and waterfront zones demonstrate dwell times 20% longer than site averages. Accordingly, the proposed model synchronizes Type II peak-hour throughput with Type I off-peak experiential depth using dynamic path allocation algorithms. This study underscores the strong spatial guidance mechanisms of Xishu Gardens, supporting tourism management and heritage conservation. Full article

Unmanned aerial vehicles (UAVs) are rapidly becoming a popular tool for digital soil mapping at a large-scale. However, their applicability in areas with homogeneous vegetation (i.e., not bare soil) has not been fully investigated. In this study, we aimed to predict soil organic carbon, soil texture at several depths, as well as the thickness of the AB soil horizon and penetration resistance using a machine learning algorithm in combination with UAV images. We used an area in the Eurasian steppe zone (Republic of Bashkortostan, Russia) covered with the Stipa vegetation type as a test plot, and collected 192 soil samples from it. We estimated the models using a cross-validation approach and spatial prediction uncertainties. To improve the prediction performance, we also tested the inclusion of oblique geographic coordinates (OGCs) as covariates that reflect spatial position. The following results were achieved: (i) the predictive models demonstrated poor performance using only UAV images as predictors; (ii) the incorporation of OGCs slightly improved the predictions, whereas their uncertainties remained high. We conclude that the inability to accurately predict soil properties using these predictor variables (UAV and OGC) is likely due to the limited access to soil spectral signatures and the high variability of soil properties within what appears to be a homogeneous site, particularly in relation to soil-forming factors. Our results demonstrated the limitations of UAVs’ application for modeling soil properties on a site with homogeneous vegetation, whereas including spatial autocorrelation information can benefit and should be not ignored in further studies. Full article

Constructing a rational ecological network is crucial for balancing regional development with environmental protection. However, existing research typically emphasizes the analysis of overall patterns, lacking an in-depth exploration of the dynamic changes in key elements and the interactions between different components. Using the Yongding River floodplain as a case study, this study applied morphological spatial pattern analysis, landscape connectivity metrics, and biodiversity assessments to identify core ecological source areas. Circuit theory was used to delineate ecological corridors and analyze network evolution across four key years, while graph theory facilitated an in-depth analysis of network structural characteristics. Furthermore, key areas for ecological restoration were identified within the floodplain. We found that the number of ecological source patches in the study area has remained relatively stable, though their total area has shown a fluctuating decline, accounting for approximately 10% of the floodplain. Additionally, ecological corridors have decreased significantly from 1967 to 2021, with a marked reduction in major corridors, leading to increased resistance to material and energy flow and a corresponding decline in network connectivity and stability. More importantly, current ecological pinch points are primarily distributed in a bead-like pattern along the Yongding River channel, while ecological barriers are concentrated in the northern and eastern floodplain, often at intersections of dense road networks and ecological corridors. These critical areas of fragmentation within the ecological network are prioritized for targeted ecological protection and restoration efforts. Overall, this study advances our understanding of the spatial distribution and composition of key ecological elements within river corridor networks and offers a framework for evaluating these networks through a multidimensional optimization approach for ecological source patches. At the same time, we conducted an in-depth analysis of key fragmentation areas in the Yongding River floodplain, providing valuable guidance for future ecological protection and restoration initiatives in river corridors. Full article

Vegetation dynamics and their underlying driving mechanisms have emerged as a prominent research focus in ecological studies of the Chinese Loess Plateau (CLP). Current investigations, however, employ simplified methodologies in analyzing the influencing factors, limiting their capacity to comprehensively elucidate the intricate and multidimensional mechanisms that govern vegetation transformations. Utilizing fractional vegetation cover (FVC) datasets spanning 2000 to 2021, this research applies both XGBoost-SHAP and Geodetector approaches for comparative analysis of the driving factors and precise quantification of climatic change (CC) and human activity (HA). The results indicate that: (1) The CLP has experienced an annual FVC increase of 0.62%, with 95.1% of the region demonstrating statistically significant vegetation improvement. (2) Precipitation and land use emerge as the primary determinants of FVC spatial distribution, with their interactive effects substantially exceeding the impacts of individual factors. (3) While both XGBoost-SHAP and Geodetector methodologies consistently identify the primary driving factors, notable discrepancies exist in their assessment of temperature’s relative importance, revealing complementary dimensions of ecological complexity captured by different analytical paradigms. (4) Approximately 94.3% of FVC variations are jointly influenced by HA and CC, with anthropogenic factors predominating at a contribution of 67%. Land use modifications, particularly transitions among cropland, grassland, and forests, constitute the principal mechanism of human influence on vegetation patterns. This investigation enhances the understanding of vegetation responses under combined natural and anthropogenic pressures, offering valuable insights for ecological rehabilitation and sustainable development strategies on the CLP. Full article

This study evaluates the progress of Korea’s National Strategic Smart City Program (NSSCP), a flagship R&D initiative, in advancing sustainable and intelligent urban development on a global scale. Utilizing the United Nations’ United for Smart Sustainable Cities (U4SSC) framework, which integrates both sustainability and smartness in city development, this research examines the program’s alignment with global standards. The findings reveal that the NSSCP contributes to the attainment of the Sustainable Development Goals (SDGs) in areas such as health, energy, innovation, and sustainable communities. It also effectively addresses key dimensions of smart cities, including smart living, environmental stewardship, mobility, and economic vitality. Despite these achievements, this study identifies critical challenges, such as the absence of robust evaluation tools and an overemphasis on quantitative targets. This research is important in advancing the discourse on smart city development, offering insights into the efficacy of smart services and systems through the lens of the NSSCP’s cloud-based open data hub model. Full article

At the crossroads of technological innovation and established practice, property valuation is experiencing a significant shift with the introduction of artificial intelligence (AI) and machine learning (ML). While these technologies offer new efficiencies and predictive capabilities, their integration raises important legal, ethical, and professional questions. This paper addresses these challenges by proposing a structured framework for incorporating Explainable Artificial Intelligence (XAI) techniques into valuation practices. The primary aim is to improve their consistency, objectivity, and transparency to ensure the internal accountability of AI-driven methodologies. Drawing from the international valuation standards, the discussion centres on the essential balance between automated precision and the professional duty of care—a balance that is crucial for maintaining trust in and upholding the integrity of property valuations. By examining the role of AI within the property market and the consequent legal debates about and requirements of transparency, this article underscores the importance of developing AI-enabled valuation models that professionals and consumers alike can trust and understand. The proposed framework calls for a concerted cross-disciplinary effort to establish industry standards that support the responsible and effective integration of AI into property valuation, ensuring that these new tools meet the same high standards of reliability and clarity expected by the industry and its clients. Full article

Conducting research on the evaluation of rural resilience and risk governance strategies in the middle reaches of the Heihe River can provide a scientific basis for the sustainable development of rural areas in the inland river basins of arid regions. Affected by water resource constraints, the expansion of artificial oases, and excessive exploitation of groundwater, the rural areas in the middle reaches of the Heihe River Basin, the second largest inland river in the arid region of northwest China, are confronted with prominent contradictions in the human-land relationship and urgently need to enhance their ability to cope with risks. Based on the remote sensing data of land use and major socio-economic data, this study draws on the theory of landscape ecology to construct a disturbance-resistance-adaptability evaluation system. Taking Ganzhou District, a typical irrigated agricultural area, as a case study, the study uses the entropy weight method, resilience change rate, and obstacle degree model to analyze the rural resilience level and its changing characteristics from 1990 to 2020, identifies the key obstacle factors affecting the development of rural resilience, and proposes risk governance strategies accordingly. Main conclusions: (1) The overall rural resilience index is relatively low, showing significant spatial disparities. Towns with well-developed multifunctional agriculture, nature reserves, and ecological-cultural control lines have higher resilience indices. (2) The change rate of the rural resilience index demonstrates phase heterogeneity, generally undergoing a “relative stability-increase-decrease” process, and forming a differentiation pattern of “decrease in the north and increase in the south”. (3) Internal risks to rural resilience development in the Ganzhou District mainly stem from low economic efficiency, fragile ecological environment, and unstable landscape patterns, among which efficiency-dominant and landscape-stability obstacle factors have a broader impact scope, while habitat resistance-type obstacle factors are mainly concentrated in the western part and suburban areas. Enhancing the benefits of water and soil resource utilization, strengthening habitat resistance, and stabilizing landscape patterns are key strategies for current-stage rural resilience governance in the middle reaches of the Heihe River. This study aims to optimize the human-land relationship in the rural areas of the middle reaches of the Heihe River. Full article

The Jinsha River Basin in Yunnan serves as a crucial ecological barrier in southwestern China. Objective ecological assessment and identification of key driving factors are essential for the region’s sustainable development. The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological assessments. In recent years, interpretable machine learning (IML) has introduced novel approaches for understanding complex ecological driving mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, and kNDVI—for the study area from 2000 to 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, and kNDVI-RSEI). Additionally, it analyzed the spatiotemporal variations of these RSEI models and their relationship with vegetation indices. Furthermore, an IML model (XGBoost-SHAP) was employed to interpret the driving factors of RSEI. The results indicate that (1) the RSEI levels in the study area from 2000 to 2022 were primarily moderate; (2) compared to NDVI-RSEI, SAVI-RSEI is more susceptible to soil factors, while kNDVI-RSEI exhibits a lower saturation tendency; and (3) potential evapotranspiration, land cover, and elevation are key drivers of RSEI variations, primarily affecting the ecological environment in the western, southeastern, and northeastern parts of the study area. The XGBoost-SHAP approach provides valuable insights for promoting regional sustainable development. Full article

With the intensification of global climate change, the frequent occurrence of typhoon disaster events has become a great challenge to the sustainable development of cities around the world; thus, it is of great significance to carry out the assessment of typhoon-directed economic losses. Typhoon disaster loss assessment faces key challenges, including complex regional environments, scarce historical data, difficulties in multi-source heterogeneous data fusion, and challenges in quantifying assessment uncertainties. Meanwhile, existing studies often overlook the complex relationship between the spatial expansion of urban and rural construction (SEURC) and typhoon disaster losses, particularly their differential manifestations across different regions and disaster intensities. To address these issues, this study proposes CLPFT (Comprehensive Uncertainty Assessment Framework for Typhoon), an innovative assessment framework integrating prototype learning and uncertainty quantification through a UProtoMLP neural network. Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. This reveals that implicitly classifying typhoon disaster loss types through prototype learning can significantly improve assessment accuracy in data-scarce scenarios. (2) By designing a dual-path uncertainty quantification mechanism, we realized high-reliability risk assessment, with 95.45% of actual loss values falling within predicted confidence intervals (theoretical expectation: 95%). This demonstrates that the dual-path uncertainty quantification mechanism can provide statistically credible risk boundaries for disaster prevention decisions, significantly enhancing the practical utility of assessment results. (3) Further investigation through controlling dynamic assessment factors revealed significant regional heterogeneity in the relationship between SEURC and directed economic losses. Furthermore, the study found that when typhoon intensity reaches a critical value, the relationship shifts from negative to positive correlation. This indicates that typhoon disaster loss assessment should consider the interaction between urban resilience and typhoon intensity, providing important implications for disaster prevention and mitigation decisions. This paper provides a more comprehensive and accurate assessment method for evaluating typhoon disaster-directed economic losses and offers a scientific reference for determining the influencing factors of typhoon-directed economic loss assessments. Full article

As ecologically sensitive zones within natural ecosystems, national parks demand more precise evaluation models for ecological sensitivity assessment. This study takes Shennongjia Forestry District, a pioneer among China’s first-batch national parks, as an study object to optimize the ecological sensitivity evaluation framework. In this study, we developed an integrated methodology incorporating high-precision ASTER GDEM elevation data, Landsat8 TM vegetation density inversion, SWAT-based flash flood simulation, and SVM-LSM landslide prediction while introducing dynamic protection elements including species migration corridors and human activity risks. The results demonstrate that the refined data structure enhances terrain coupling accuracy by transitioning from “Vegetation Type—Runoff Coefficient” to “Vegetation Density—Runoff Coefficient” conversions, with the optimized model exhibiting superior sensitivity in spatial element identification. This approach provides scientifically grounded technical support for balancing ecological conservation and visitor management in protected areas. Full article

Amid the global urbanization process, addressing the spatial carrying capacity constraints of historic urban districts and enhancing sustainable tourism vitality has become a critical issue in urban renewal research. This study takes Suzhou Old City as a case study and innovatively constructs a dynamic spatiotemporal analytical framework to examine the relationship between tourism facility attractiveness and tourism vitality in historic districts. This study integrates multi-source spatiotemporal data and applies factor analysis, weighted kernel density estimation (KDE), spatial autocorrelation analysis, and multiscale geographically weighted regression (MGWR) to systematically investigate the spatial distribution patterns of tourism facilities and elucidate their multidimensional driving mechanisms on tourism vitality. The findings reveal a generally positive correlation between tourism attractiveness and tourism vitality. However, significant temporal and spatial variations exist, with different types of tourism facilities demonstrating distinct attractiveness patterns at different times of the day. These variations underscore the intrinsic link between visitor behavior and regional functionality as well as the structural contradictions within historic districts. This study not only advances theoretical insights into the spatial optimization of tourism facilities and tourism vitality enhancement but also provides scientific evidence and policy recommendations for improving facility distribution, revitalizing historic districts, and promoting sustainable urban development. Full article

Urban flooding represents a critical socio-ecological challenge exacerbated by climate change and rapid urbanization, with green infrastructure (GI) emerging as a transformative approach to flood management. This study employs an innovative methodological framework integrating the Biblioshiny, CiteSpace, and Orange3 analytical tools to examine research trends and evolutions in GI for urban flooding from 2015 to 2024. The bibliometric analysis of 813 publications reveals a profound epistemological transition from technically oriented approaches toward integrated socio-ecological frameworks. The citation patterns demonstrate increasing scholarly attention on multifunctionality, climate resilience, and governance dimensions, with the United States and China emerging as dominant research hubs. The analysis identifies distinct thematic clusters reflecting the field’s intellectual progression from hydrological engineering paradigms toward systemic conceptualizations that recognize the complex interactions between technical, ecological, and social dimensions. Despite these advancements, persistent knowledge gaps remain regarding longitudinal performance evaluations, governance frameworks for maintenance, and scalar integration from site-specific interventions to watershed-level outcomes. These findings emphasize the need for methodological innovation addressing the temporal dimensions of GI performance and institutional arrangements for its implementation across diverse urban contexts, positioning GI as a critical component of sustainable urban water management amid increasing climatic uncertainty. Full article

Excessive human activities associated with rapid industrialization and urbanization have exerted tremendous pressure on limited land resources. Scientific land use planning is essential for attaining sustainable development. This study focuses on multi-objective land use optimization in Xinjiang, China’s largest arid region, targeting the dual goals of maximizing ecosystem services and economic benefits. The non-dominated sorting genetic algorithm II (NSGA-II) and the future land simulation (FLUS) model are integrated innovatively to explore optimal land use in terms of both quantity and spatial distribution. Four distinct development scenarios are predefined and compared: natural development, ecological preservation, economic development, and sustainable development. The main results are as follows: (1) The fragile ecosystem of Xinjiang has been under tremendous pressure during the past 40 years. The predominant pattern in land use transition was the increase in construction land (+115.66%) and cultivated land (+47.18%) at the expense of grassland (−5.48%) and forest land (−4.15%), both of which hold substantial ecological significance. (2) Among these predefined scenarios, the sustainable development scenario is considered more favorable in the future due to its ability to balance ecological preservation and economic development. All the ecologically valuable lands will have certain degrees of growth, whereas the expansion scale of construction land will be effectively controlled. (3) The lack of high-quality land and the unpredictability of water resources will be the two major obstacles to implementing this sustainable development scenario. To overcome them, the government should provide policy and financial support for restricting construction land expansion, exploiting unused land, and strengthening water conservation. This study contributes to formulating more effective land use strategies under multiple conflicting goals and ultimately achieving sustainable development of the economy and ecology in Xinjiang as well as other similar regions. Full article

This systematic review examines the intersection of systems thinking and learning theory in addressing rural–urban challenges in light of increasing global urbanization. We explore how different dimensions of systems thinking—ontological (how we understand systems) and epistemological (how we think about systems)—align with single-, double-, and triple-loop learning in rural–urban research from 2014 to 2024. Through a rigorous screening process of the peer-reviewed literature, we analyze how theoretical frameworks manifest in research approaches, methodological choices, and learning outcomes. Our findings reveal promising developments and persistent gaps in current approaches, and suggest pathways for more integrated theoretical and methodological frameworks. We also highlight the need for studies that develop knowledge and practices that support collective learning and joint trajectories towards sustainability from a cross-sectorial perspective in rural–urban geographies. This synthesis contributes to discussions on how to effectively address complex challenges at the rural–urban interface while advancing both theoretical understanding and practical application. Full article

Post-industrial and post-mining areas are an important element of cities historically associated with industrial activity. The transformation of degraded areas is a challenge for spatial policy, which is characterized by a substantial impact on the cultural heritage of mining and industry. The case of Piekary Śląskie shows the consequences of deindustrialization, which leads to the degradation of urban space and requires innovative revitalization strategies considering the principles of sustainable development and the concept of blue–green infrastructure. Archived topographic maps and current interactive maps of the study city were used in a spatial data analysis. The aim was to determine the directions of the spatial development of post-industrial and post-mining areas using the example of a medium-sized city located in the core of the Katowice conurbation, while considering the role of blue–green infrastructure in the revitalization process. Integrating blue–green infrastructure into the city’s planning documents may serve as a model for other urban areas, highlighting the synergy benefits between urban development and environmental protection. Such solutions support the development of a green economy to improve residents’ living conditions and increase the city’s competitiveness in the region. The specific examples of the revitalization of the areas in the Andaluzja and Julian mines and the reclamation of the brickyard in the area of Kozłowa Góra in Piekary Śląskie show that a multifunctional approach to revitalization contributes to the harmonious development of urban spaces. Full article

Traditional urban expansion struggles to balance economic and ecological demands. Intensive development planning based on re-naturalization has become the policymakers’ choice. However, planning-oriented land use patterns and re-naturalization pathways remain difficult to determine. This study developed a spatial decision-support framework integrating ecosystem service valuation (ESV), land-use simulation, and ecological planning for Shanghai. This study assessed the spatiotemporal dynamics of ESV and simulated land use patterns and ESV for 2035 under different scenarios (inertial development, cropland protection, and ecological development). The optimal scenario and corresponding re-naturalization pathways were determined based on the principle of the optimal ESV. The results showed that ESV has declined over the past 20 years (−5.21%/5 years). High-value areas shrank significantly due to ecological space degradation. The planning-oriented ecological development scenario is the optimal scenario, with the highest ESV of CNY 189,240.29 million, which is higher than the status quo, inertia development scenario, and cropland protection scenario by 9.69%, 23.27%, and 9.53%, respectively. Taking the land use patterns under the ecological development scenario as the re-naturalization objective, 12 re-naturalization pathways totaling 686.88 km² were identified. Cropland to forestland and built-up land to cropland were the largest, accounting for 67.88% and 15.02%, respectively. This study provides valuable insights into ecological planning and re-naturalization in urbanized areas. Full article

The interplay between land availability and the housing market highlights the importance of government intervention through land policies. Effective land policies ensure in-time land availability and facilitate private sector involvement in housing development. This study examines how public land policies influence formal housing development by the private sector, with a particular focus on land banking strategies—mechanisms involving the acquisition and holding of land for future use to ensure availability and capture value increases. While land banking policy aims to serve public benefits, private-sector land banking often prioritizes profit, creating governance challenges that shape housing development outcomes. This paper analyzes this phenomenon in the context of Indonesia by developing analytical framework of legitimacy, effectiveness, efficiency, and fairness. As a rapidly growing country with significant housing backlogs and a private-sector-dominated market, Indonesia’s land regulations present a critical case for examining these dynamics. Our analysis shows that while regulatory framework regulations emphasize land consolidation, acquisition, and development as instruments to facilitate private sector involvement in housing development, weak enforcement and regulatory ambiguities often undermine their effectiveness. The findings indicate that private-sector land banking is largely speculative, driven by profit-maximization strategies rather than housing provision, and is reinforced by inconsistent policy enforcement at the municipal level. A municipal case study further illustrates how governance challenges and discretionary compliance allow private developers to prioritize profitability over the affordable housing needs outlined in public policies. While land policies in Indonesia are framed as comprehensive planning tools, their implementation often favors specific beneficiaries, limiting their broader social impact. Full article

Earthquakes disrupt local organic carbon distribution by stripping vegetation, destabilizing soil, and triggering landslides, leading to immediate carbon loss and potential long-term climate impacts. While remote sensing techniques effectively assess post-earthquake vegetation loss, they fail to capture subsurface carbon dynamics along vertical profiles. This study quantifies ecosystem carbon loss from the Luding Earthquake by integrating field sampling, UAV-based LiDAR, and machine learning models to assess vegetation and soil carbon stocks. Field investigations were conducted at landslide deposits, debris flow deposits, and undisturbed sites to analyze soil organic carbon and biomass carbon content. UAV-derived point cloud data improved vegetation biomass estimation, reducing sample plot overestimation by 30.4% due to uneven vegetation distribution. The results indicate that landslides and debris flows caused an 83.9–95.9% reduction in carbon storage, with the total ecosystem carbon loss estimated at 7.36 × 10⁵ Mg. This study provides a comprehensive assessment of earthquake-triggered carbon loss, offering critical insights for carbon budget research on natural disasters and the development of post-earthquake ecological restoration policies. Full article

Rural areas are undergoing a transformation, shifting from traditional agriculture to green and leisure industries, driven by urban–rural imbalances and environmental challenges. This transition, however, presents the growing conflicts between preserving spatial features and promoting industrial development. Based on the unique rural spatial typology of polders, this study integrated theories from cultural, landscape, ecological, economic, and social perspectives to construct a conceptual framework of the interactive relationship between spatial features (SFs) and industrial development (ID). Then, an evaluation index system was constructed to measure the current status of SFs and ID, using data from field surveys, satellite imagery, and 2020 yearbooks, with the Gaochun Polder District, Nanjing (China), as the case study. Next, the coupling coordination degree (CCD) model and a scenario analysis based on orthogonal design were applied to assess the coherence and development between SFs and ID, and to identify strategies for optimizing rural industrial development. The results show that (1) the current SFs and ID are in the break-in and basic coordination stage, with ID lagging behind SFs, and (2) the 25 scenarios generated through orthogonal design were categorized into three groups: high-level coordination with synchronization between the two systems, high-level coordination but ID lagging behind SFs, and basic coordination where ID lags behind SFs. To achieve a high-level coordination with synchronization, specific strategies were proposed to enhance agricultural input–output benefits, improve agricultural scale–quality benefits, and, overall, protect the SF system while making minor adjustments to the village dwelling subsystem. Therefore, the findings provide recommendations for traditional polder villages to optimize their rural industries while preserving the distinctive SFs of the rural cultural landscape. Full article

The rapid pace of urbanization has led to excessive resource consumption and worsening environmental pollution, particularly in resource-based cities, where prolonged exploitation of mineral resources has resulted in dual challenges of ecological degradation and economic imbalance. Using Fushun, a resource-exhausted city still struggling with its transformation, as a case study, this research develops a progressive analytical framework that integrates the InVEST model, optimal parameter geographic detector, and multi-scale geographically weighted regression. This framework, comprising a sequence of analytical steps—single-factor analysis, interaction-factor analysis, global regression analysis, and geographically weighted regression analysis—enables a comprehensive exploration of the driving mechanisms behind ES changes in Fushun from 2000 to 2020. The results indicate the following: (1) Significant changes in ecosystem services were observed, with water yield and soil conservation showing a fluctuating upward trend, while carbon storage and habitat quality experienced slight declines. (2) Over time, the dominant drivers transitioned from primarily socio-economic factors to a synergistic influence of natural and human activities. GDP and land use intensity increasingly contributed to explaining ecosystem services through their interaction effects. (3) At the street scale, driving mechanisms exhibited spatial heterogeneity. For instance, the negative effects of built-up land and cultivated land were more pronounced in urban–rural transition zones, while elevation and NDVI had a more positive impact in ecological source areas. This framework provides systematic and targeted recommendations that offer data-driven insights to guide policies prioritizing regional ecological sustainability. Furthermore, it provides practical reference points for improving the ecological quality of other coal resource-exhausted cities undergoing incomplete transformations. Full article

Against the intensifying mismatch between urban and rural land resources, activating farmers’ intentions to revitalize their idle homesteads is a key issue in optimizing land resource allocation and promoting urban–rural integrated development. However, existing studies mostly focus on the marginal effect of a single factor and ignore the synergistic effect of multiple factors, making it difficult to reveal the complex causal logic of farmers’ decision-making. This study aims to explain the causal asymmetry and equivalent path problem in farmers’ revitalized decision-making by capturing the multidimensional interaction mechanism of “external stimulus–mental cognition”. This study integrates the social stratification theory, the theory of planned behavior, and the Stimulus–Organism–Response framework to systematically explore how the interactive configuration of farmer differentiation and cognition from a multidimensional perspective drives the formation of farmers’ willingness to engage in high inventory activities, based on the 881 farmer research data in Shaanxi Province, using fuzzy-set qualitative comparative analysis (fsQCA) methodology. This study found that (1) a single condition cannot independently explain the intentions of farmers to revitalize, and its formation needs to rely on the synergistic linkage of multiple conditions; (2) the configuration of farmers’ high intentions to revitalize includes “wealth capital differentiation–dual cognitive-driven type”, “single cognitive-driven type”, “reputation capital differentiation–single cognitive-driven type”, “wealth capital differentiation–single cognitive-driven type”, which wealth capital differentiation is the common core condition triggering high intention; and (3) the formation of farmers’ low revitalization intentions stems from the insufficient differentiation of farmers and the lack of cognitive elements. Therefore, policymakers should take a holistic perspective in enhancing farmers’ revitalization intentions, focusing on the rational allocation between farmer differentiation and farmers’ cognition. Full article