Search Results (3,730)

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

This study investigates the factors influencing the expansion of organic farming in Europe between 2000 and 2022. Driven by consumer demand and EU support through the Common Agricultural Policy, organic farming has grown significantly. The research uses panel data and linear regression to assess the impact of socio-economic, agronomic, and environmental variables, including GDP, HDI, population density, education, broadband access, pesticide use, and biodiversity indicators. Data sources include FAOSTAT, FiBL, Eurostat, and the World Bank. The analysis also incorporates crop-specific organic farming data and environmental metrics such as ammonia emissions. The results show that expansion is shaped simultaneously by environmental pressures and socio-economic conditions: greater pesticide use, larger land availability, higher human development, and agricultural employment support organic adoption, while intensive livestock-related emissions and indicators of urbanization, such as broadband access, tend to constrain it. Full article

Driven by the advancement of the global blue economy strategy and the rapid expansion of urbanization into deep underground spaces, the scale of critical infrastructure, ranging from cross-sea bridges and undersea tunnels to offshore wind farms and deep-buried utility tunnels, has reached unprecedented levels [...] Full article

Chennai’s transport sector is undergoing a structural transition as the city seeks to accommodate rapidly growing travel demand while reducing energy consumption and emissions. This study develops a city-scale system dynamics model using STELLA to simulate long-term transitions in bus and Intermediate Public Transport (IPT) systems over the period 2011–2038. Four policy scenarios—Do Minimum, Partial, Desirable, and Ideal—are evaluated to examine how fleet expansion, propulsion technology substitution, and service restructuring influence urban transport energy sustainability. The model integrates demographic growth, service-level fleet benchmarks, and multiple propulsion pathways, including diesel, CNG, LPG, bio-CNG, hydrogen, and battery- and solar-electric technologies. Full article

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use patterns for 2040 under three scenarios: Natural Development (NDS), Ecological Protection (EPS), and Urban Development (UDS). Results indicated a consistent decline in agricultural land and an expansion of urban land across all scenarios, with the most pronounced urban growth under UDS (6.79%) and the largest ecological land area under EPS (5178.96 km²). Since 2000, the number of EN sources and corridors had decreased, with sources mainly concentrated along coastal areas. The source and corridor under UDS exhibited the highest area ratio (20.08%), while NDS showed the lowest (18.72%), with UDS demonstrating the strongest resilience. Through community detection, the UDS EN was divided into five ecological clusters, encompassing 127 intra-cluster corridors (2285.95 km) and 34 inter-cluster corridors (1171.32 km), among which the cluster near the Yellow River estuary was determined to be the most critical (Level 1). These findings will provide valuable insights for managing landscape fragmentation and biological habitat protection in YRDEEZ. Meanwhile, the multi-scenario simulations of ENs could play an important role in constructing ecological security patterns and protecting ecosystems. Full article

Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km² (pre-2018) to a peak of 12.61 km² in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km² in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

Land use/cover change (LUCC) is a dominant driver of ecosystem service dynamics in arid inland basins. Focusing on the Yarkant River Basin (YRB), Xinjiang, we coupled the PLUS land-use simulation with the InVEST Habitat Quality Model to project 2040 land-use patterns under four policy scenarios—Natural Development (ND), Arable Protection (AP), Ecological Protection (EP), and Economic Development (ED)—and to quantify their impact on habitat quality. Model validation against the 2020 map indicated strong agreement (Kappa = 0.792; FOM = 0.342), supporting scenario inference. From 1990 to 2023, arable land expanded by 58.17% and construction land by 121.64%, while forest land declined by 37.45%; these shifts corresponded to a basin-wide decline and increasing spatial heterogeneity of habitat quality. Scenario comparisons showed the EP pathway performed best, with 32.11% of the basin classified as very high-quality habitat and only 8.36% as very low-quality. In contrast, under ED, the combined share of very low + low quality reached 11.17%, alongside greater fragmentation. Spatially, high-quality habitat concentrates in forest and grassland zones of the middle–upper basin, whereas low-quality areas cluster along the oasis–desert transition and urban peripheries. Expansion of arable and construction land emerges as the primary driver of degradation. These results underscore the need to prioritize ecological-protection strategies especially improving habitat quality in oasis regions and strengthening landscape connectivity to support spatial planning and ecological security in dryland inland river basins. Full article

The Campos Rupestres, ancient and nutrient-poor mountaintop ecosystems in Brazil, harbor exceptional biodiversity and endemism but face severe threats from mining and urban expansion. Native grasses (Poaceae), represented by nearly 300 documented species—many of them poorly studied—are fundamental elements of these ecosystems. They provide critical ecological services, including soil stabilization, enhancing carbon storage and nutrient cycling, regulating water availability, and resilience to disturbances. This review synthesizes current knowledge on the diversity, functions, and propagation of Campos Rupestres grasses, with emphasis on their potential in ecological restoration. Despite their ecological importance, large-scale use of native grasses remains incipient, constrained by limited knowledge of reproductive biology, low seed viability, and scarce commercial seed availability. Advances in propagation include seedling and plug production, vegetative propagation, and rescue/reintroduction strategies, which have shown promising results in post-mining restoration. However, reliance on seed collection from natural populations risks depleting already limited genetic resources, highlighting the need for ex situ production systems. Expanding research on taxonomy, ecology, and cost-effective propagation methods, alongside supportive policy and market development, is crucial for integrating native grasses as cornerstone species in restoration programs. Bridging these gaps will enhance biodiversity conservation and restoration in one of the world’s most threatened megadiverse systems. Full article

Mangrove ecosystems along the Pacific coast of Panama are increasingly exposed to anthropogenic pressures such as urban expansion and deforestation. These habitats provide resources for orchid bees (tribe Euglossini), yet information on their assemblages in mangrove environments remains limited. In this study, we documented the diversity and composition of orchid bee communities in mangrove–forest edges from two coastal areas with contrasting levels of human disturbance: Panama Bay and Chame Bay. Orchid bee sampling was carried out during two independent periods: from April to July 2022 at three sites in Panama Bay, and from December 2022 to January 2023 at one site in Panama Bay and one site in Chame Bay, using McPhail traps baited with eucalyptus oil and distributed across multiple zones within each site. A total of 427 individuals representing 14 species and three genera were recorded. Observed species richness and abundance were lower at the more urbanized mangrove sites, where collections were dominated by a few widespread species, particularly Eulaema nigrita. Multivariate analyses revealed differences in community composition between sites. These patterns suggest associations between anthropogenic context and orchid bee assemblage structure in mangrove edges, although longer-term and multi-method studies are required to evaluate temporal consistency and underlying mechanisms. This study provides baseline information to support future monitoring of orchid bee communities in tropical coastal ecosystems. Full article

In the context of the “Dual Carbon” goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosystem models to project NPP dynamics, they often face challenges in acquiring high-resolution future vegetation parameters and typically overlook the stability of NPP under changing climates. To address these gaps, this study focuses on Nanjing and develops a long-term, multi-scenario analysis framework based on the Dynamic Land Cover–Climate Model (DLCC–NPP). This framework innovatively integrates the PLUS model with a Random Forest (RF) algorithm. By establishing a direct statistical mapping between macro-climate/micro-land cover and NPP, the RF model functions as a statistical downscaling tool. This approach bypasses the uncertainty accumulation associated with simulating future vegetation indices, enabling precise spatiotemporal NPP prediction at a 30 m resolution. Using this approach, we systematically analyzed the NPP dynamics from 2004 to 2044 under three SSP scenarios. The results revealed that Nanjing’s NPP exhibited a fluctuating upward trend, with urban forests contributing the highest productivity (mean NPP ~266.15 gC/m²). Crucially, the volatility analysis highlighted divergent response characteristics: forests demonstrated the highest stability and “buffering effect,” whereas grasslands and croplands showed high volatility and sensitivity to climate fluctuations. Spatially, a distinct “stable high-NPP core, decreasing periphery” pattern was identified, driven by the interaction of urban expansion and ecological conservation policies. In conclusion, the DLCC–NPP framework effectively overcomes the data scarcity bottleneck in future simulations and characterizes the spatiotemporal heterogeneity of vegetation carbon fixation in urban ecosystems, providing scientific support for optimizing green space patterns and enhancing urban ecological resilience in high-density cities. Full article

Regional green development requires balancing anthropogenic carbon emissions (CEs) with vegetation carbon sequestration (VCS). Using the CASA model and plant photosynthesis equation, we estimated VCS from net primary productivity (NPP) and proposed a Carbon Balance Pressure Index (CBPI) to quantify the imbalance between carbon sources and sinks. Spatial analysis and a geographic detector were applied to examine influencing factors of CBPI across Sichuan–Chongqing from 2001 to 2017. Results show that CE increased by 178%, while VCS rose by 27%. Regional CBPI thus enhanced from 0.35 to 0.76, aligning with CE trends. The CBPI presented a clear west-low (0–0.2, except Panzhihua), center-high (peak 3.1 in Chengdu), moderate-east (0.1–0.8) pattern. Geographic detector reveals that economic development and urbanization accounted for 80% of CBPI heterogeneity, followed by transportation (65%). Energy-intensive industries dominated developed areas, while construction-land expansion prevailed in developing regions. This study underscores region-specific emission-sink pathways and provides an empirical basis for differentiated low-carbon strategies in similar rapidly urbanizing regions in China. Full article

Rapid urbanization has driven land-use transitions, leading to the continuous replacement of land-use types with high carbon storage capacity by those with lower capacity. A deeper analysis of the drivers behind these changes and predictions of their future development is essential for optimizing land-use patterns and enhancing regional carbon sink functions. This study takes the Guanzhong Plain Urban Agglomeration (GPUA) as a case study. It employs the PLUS and InVEST models to simulate land use and land cover (LULC) dynamics from 2000 to 2020 and to project the LULC and associated spatial clustering characteristics of carbon storage in 2030. The results show that: (1) From 2000 to 2020, LULC changes in the region were dominated by the conversion of cropland to built-up land, primarily concentrated in urban areas and along the Wei River corridor. By 2030, built-up land is expected to continue expanding along transportation corridors and urban peripheries, further reducing the area of cropland. (2) Changes in carbon storage were mainly driven by LULC transitions, with an overall declining trend observed from 2000 to 2020 (decreasing from 2754.69 Mt to 2741.79 Mt) despite the buffering effect of ecological restoration, and a projected continued decrease to 2734.28 Mt by 2030. (3) The spatial distribution of carbon storage was characterized by a strengthening polarization. The proportion of hotspot areas increased from 30.38% to 32.33% over the 2000–2020 period, with a concentration in ecological function zones such as the Qinling Mountains, Ziwuling, and Huanglongshan. Concurrently, coldspot areas also expanded. Future efforts should prioritize the protection of high-carbon-sink mountainous zones, strictly control the outward expansion of built-up land, and enhance carbon storage capacity in agricultural areas to support low-carbon development and spatial optimization in the GPUA. Full article

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions. Full article