Search Results (4,790)

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74−0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially varying shadows, specular reflections, and periodic occlusions that confound visual cues for diagnosing crop diseases and abiotic stresses. Meanwhile, agrivoltaic deployments are often distributed across farms and operators, making centralized data collection impractical due to privacy, ownership, and regulatory concerns. This paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition that explicitly models PV-induced illumination and enables collaborative training without sharing raw images. The core algorithm integrates (i) a PV-geometry-conditioned shadow normalization module that fuses estimated array layout and sun-angle priors into a shadow-aware appearance canonization network, reducing illumination-induced domain shift across times and sites; (ii) a federated contrastive stress learner that aligns stress semantics across farms via prototype-based contrastive objectives while remaining robust to heterogeneous sensors and crop stages; and (iii) an adaptive privacy layer that combines secure aggregation with budget-aware gradient perturbation and client-level clipping to provide formal privacy guarantees while preserving fine-grained diagnostic performance. Extensive experiments on real agricultural vision benchmarks and agrivoltaic shadow variants demonstrate that PrivAgriVolt improves stress recognition and segmentation under PV shading while maintaining strong privacy–utility trade-offs. Full article

As the digital economy advances, information infrastructure has become a core engine for driving green economic transition and optimizing sustainable land systems. However, its heterogeneous governance effects on different types of pollutants and spatial spillover mechanisms remain insufficiently explored. This study draws on the theoretical framework of the dynamic game between scale and technique effects. It utilizes the PSTR model and the SDM to systematically investigate the nonlinear and spatial synergistic impacts of information infrastructure. The analysis covers aggregate information infrastructure and its structural subdivisions, including traditional and new information infrastructure. To ensure empirical rigor, this study introduces a Bartik instrumental variable constructed via the shift share approach and thoroughly eliminates endogeneity interference through the Control Function Approach and a core variable lagging strategy. The empirical research reveals three core findings. Firstly, after crossing the initial extensive scale effect dominated by physical construction, the profound technique effect dominates long-term environmental governance. Secondly, new-type information infrastructure demonstrates a superior capacity for long-term environmental governance and land use efficiency compared to traditional telecommunications. Finally, spatial spillover analysis indicates that although PM2.5 exhibits strong cross-regional physical contagion, the current environmental dividends of information infrastructure remain highly localized due to regional administrative data silos, lacking significant cross-regional synergistic spillover effects. This study provides a solid empirical basis for formulating differentiated digital spatial governance frameworks, breaking interprovincial data factor barriers, and preventing the physical expansion trap of traditional infrastructure. Full article

Recirculating aquaculture systems (RASs) improve water-use efficiency in fish production but generate nutrient-rich effluents requiring management. Integrating aquatic biomass cultivation into RASs offers a promising approach to nutrient recovery, CO₂ utilization, and biomass production. This study evaluates the technical and economic feasibility of integrating Wolffia globosa cultivation with RASs through process simulation and techno-economic analysis (TEA). A pilot-scale system in Thailand was modeled using SuperPro Designer, comparing static and suspended aeration cultivation. The suspended configuration required only ~10–12 m² for 28.80 m³, whereas static cultivation required 131 m² for 32.80 m³, corresponding to about a 12-fold reduction in land area. The suspended system achieved higher annual biomass production (1056 kg dry weight (DW) yr⁻¹) than the static system (690 kg DW yr⁻¹), corresponding to CO₂ fixation of ~1.50 and ~0.98 t CO₂ yr⁻¹, respectively. The static system achieved higher nutrient removal efficiencies (97% N and 99.66% P), while the suspended system showed lower removal (64% N and 65.30% P) but higher productivity. Economic analysis confirmed feasibility, with the suspended system achieving higher return on investment (17.56% vs. 12.89%) and a shorter payback period (5.70 vs. 7.76 years). These results demonstrate the potential of RAS–Wolffia integration as a circular approach for resource recovery and sustainable aquaculture. 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

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes. Full article

Rapid global urbanization has exacerbated the conflict between land expansion and ecosystem carrying capacity, making the enhancement of urban land use efficiency (ULUE), a critical pathway for sustainable development. While the digital economy offers a new engine for green transition, its spatiotemporal mechanisms remain underexplored. Taking China, a representative emerging economy, as a case study, this paper investigates the impact of digital transformation on ULUE from 2013 to 2020. By integrating the Super-EBM model with GTWR, we reveal a dynamic evolution where national efficiency improves while regional polarization intensifies. A key finding challenges traditional agglomeration theory, that population density increasingly exerts a negative impact on ULUE, suggesting that congestion costs and ecological pressures are outweighing agglomeration benefits in the digital era. Furthermore, digital infrastructure demonstrates a consistent positive effect by overcoming geographical barriers, whereas environmental regulation exhibits a J-curve effect that is initially constraining but eventually boosts efficiency. These insights provide a roadmap for developing nations to leverage digital tools for balancing economic growth with ecological sustainability, emphasizing the need for spatially differentiated strategies to manage the digital divide and urban congestion. Full article

This interdisciplinary study integrates criminological theory with geospatial methods to analyse large, multi-format datasets using geostatistical techniques. The aim is to predict where Child Criminal Exploitation (CCE) is likely to cluster, based on the spatial convergence of contextual risk factors. Drawing on insights from General Strain Theory (GST) and prior research on CCE, this study integrated seven open-source datasets capturing educational attainment, age demographics, violent crime, deprivation, and paramilitary-related violence. These variables were operationalised to construct a proxy measure for strain. Spatial analysis was conducted using ArcGIS Pro, including the Data Interoperability extension, to enable efficient integration and interrogation of multi-format geospatial data. Geospatial analysis demonstrated that contextual risk factors for CCE are spatially clustered. Using four search parameters, a small subset of wards with elevated risk were identified. This resulted in a reduction in ward locations by 85–99%, land area under investigation from 14.45% to 0.84%, and affected population from 17.91% to 1.41%, enabling more targeted and efficient resource allocation. As understanding of the contextual factors contributing to CCE improves, this methodological approach offers scalable and data-driven means of identifying high-risk areas. By integrating geospatial analysis with criminological theory, the model supports more effective safeguarding strategies and prioritisation of limited public resources. This study is limited by the absence of multi-agency datasets, which were beyond its scope. Future research aims to incorporate cross-sector data to validate and refine the model through ground-truthing, enhancing its predictive accuracy and practical applicability. Full article

The sponge city pilot policy (SCP) is a green infrastructure initiative that integrates ecological stormwater management, land-use planning, and urban sustainability goals. This study employs the super-efficiency slack-based measure (SBM) model to evaluate the green total factor productivity (GFP) of 278 prefecture-level and above cities in China from 2010 to 2022. It then applies a difference-in-differences (DID) model to identify the causal effect of the SCP on urban GFP while further examining transmission mechanisms and heterogeneous policy effects. The empirical findings show that: (1) the SCP significantly enhances urban GFP, with pilot cities exhibiting an average increase of approximately 6.08% relative to non-pilot cities, indicating broader medium- to long-term ecological–economic co-benefits beyond the policy’s immediate hydrological objectives; (2) the policy effect is more pronounced in cities with stronger economic foundations, larger urban scales, greater environmental governance pressure, weaker resource dependence, and more favorable locational conditions; and (3) the SCP promotes industrial structure transformation (IST) and green technological innovation (GTI), which jointly mediate the relationship between ecological infrastructure and green productivity. Drawing on ecological modernization theory and structural change theory, this study explains how ecological infrastructure, as a techno-structural reform mechanism, can internalize environmental externalities, stimulate innovation, and facilitate sustainable urban transformation. These findings provide evidence that green infrastructure policies can generate both ecological and economic co-benefits, offering useful insights for climate-resilient and sustainable urban planning. Full article

Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such as SegFormer have demonstrated a strong capability in modeling long-range context through hierarchical encoding, yet their lightweight decoders mainly rely on linear projection and feature fusion, providing limited capacity for local refinement after multi-scale aggregation. This limitation may reduce spatial precision in boundary-sensitive and small-object-rich regions. To address this issue, we propose the Post-fusion Enhanced Block (PFEB), a lightweight decoder-side refinement module inserted after multi-scale feature fusion and before pixel-wise classification. PFEB combines channel expansion, depthwise and pointwise convolutions, efficient channel attention (ECA), and residual learning to enhance local semantic refinement while largely preserving computational efficiency. Built upon SegFormer, the proposed method was evaluated on two widely used remote sensing benchmarks, i.e., LoveDA and ISPRS Vaihingen, under both Mix Transformer-B0 (MiT-B0) and Mix Transformer-B2 (MiT-B2) backbones. Experimental results show that PFEB consistently improves the SegFormer baseline across datasets and model scales. Under MiT-B2 backbone, our method achieves 53.82 ± 0.31 mean intersection over union (mIoU) on LoveDA and 74.84 ± 0.41 mIoU on ISPRS Vaihingen. Boundary- and size-aware evaluations further indicate that the gains are mainly reflected in improved semantic correctness near boundaries and in the recoverability of small objects. With only modest additional cost (approximately +0.53 M parameters and +8.7 G floating point operations (FLOPs)), PFEB provides a favorable accuracy–efficiency trade-off. These results suggest that PFEB is an effective and lightweight post-fusion refinement module for improving fine-grained remote sensing semantic segmentation. Full article

Designing sustainable pedestrian infrastructure in hyper-arid cultural landscapes requires balancing visitor experience, heritage protection, and environmental constraints. This study develops a statistically grounded model for planning sustainable walking trails in Al-Ula, Saudi Arabia, using multi-spectral remote sensing data integrated with expert-based evaluation. A GIS-based Multi-Criteria Decision-Making (MCDM) framework was applied to assess topographic slope, vegetation cover (NDVI), built-up density (NDBI), Land Surface Temperature (LST), and solar exposure. Indicator weights were validated through a three-round Delphi survey involving fifteen experts. The results indicate strong consensus among experts, identifying LST (21%) and slope (20%) as the most influential determinants of trail suitability in desert environments. These findings highlight the critical role of thermal stress in shaping safe and sustainable pedestrian mobility in hot climates. The optimized 44.5 km trail network, classified into three difficulty levels, improves energetic efficiency by reducing caloric expenditure by 24% compared to conventional routing. In addition, the proposed network has the potential to reduce carbon emissions associated with heritage-related travel by approximately 75% through modal shift from vehicles to walking. The framework provides a practical decision-support tool for planners seeking to develop low-carbon, climate-responsive tourism infrastructure aligned with the objectives of Saudi Arabia’s Vision 2030. Full article