Search Results (10,429)

This study employs panel data for 275 Chinese cities from 2011 to 2021. Water use efficiency is measured as an aggregate city-level indicator via stochastic frontier analysis, while the level of digital economy development is quantified using principal component analysis. We then employ the system generalized method of moments to investigate the causal relationship between the digital economy and urban water use efficiency, and further identify industrial structure upgrading as the mediating role through which the digital economy affects water efficiency. The main findings are as follows: (1) The digital economy has a significant positive impact on urban water use efficiency. (2) Regional heterogeneity analysis shows that the digital economy presents a stronger positive effect on water use efficiency in eastern regions than in central and western regions. (3) Exploratory mechanism analysis indicates that industrial structure upgrading serves as the mediating role through which the digital economy improves urban water use efficiency. Based on the empirical findings, this paper draws targeted policy implications. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Rail-trail corridors in the agricultural Midwest exhibit layered ecological conditions influenced by the material legacy of railroad infrastructure and contemporary land use pressures. This study uses a mixed-methods approach integrating GIS analysis, field documentation, and open-response surveys with trail managers to characterize the structural and ecological heterogeneity of two rail-trails within the Corn Belt. Spatial methods quantify variation in right of way width, land cover context, connectivity, and patterns of fragmentation, revealing that corridors shift in response to agricultural edges, successional woodlands, riparian zones, and urban conditions. Field visits and on-site sketching provide fine-grained insight into vegetative structure, topography, and edge dynamics, while the thematic analysis of survey responses highlights how management regimes, resource limitations, invasive species, and adjacent land uses shape ecological patterns along the trail. Together, these methods support the development of a typology of rail-trails based on their vegetative, hydrological, and disturbance patterns. We argue that design and management should work with the nuance of the corridors, noting the potential for landscape experimentation. Novel design approaches can support the performance of rail-trails as ecological infrastructure while enabling meaningful human–environment interactions within the right of way. Full article

Accurate forecasting of near-surface PM_2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits their robustness under complex meteorological conditions. In this study, we propose a multi-source spatiotemporal learning framework(MST-Net) to enhance PM_2.5 forecasting accuracy by integrating vertically resolved atmospheric information from lidar and radiosonde observations. The proposed approach incorporates vertical profile features together with surface measurements to provide complementary information on atmospheric vertical structure and its temporal evolution. Experimental results demonstrate that MST-Net consistently outperforms conventional time-series models across multiple forecast horizons. Notably, at extended lead times (12–24 h), the proposed framework exhibits enhanced stability and slower error growth. For 24 h forecasts, MST-Net reduces RMSE by approximately 13% and MAE by about 19%. These results indicate that leveraging multi-source vertical atmospheric information can effectively improve the reliability of urban air quality forecasting. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Urban green spaces provide essential ecosystem services, yet mismatches between subjective perceptions and objective assessments may constrain effective planning. This study examines the correspondence between perceived and measured ES across two contrasting urban green spaces in Shanghai: Century Park, a managed urban park, and Sanlin Green Space, a naturalistic urban forest. Objective ecosystem services (regulating, supporting, and cultural) were quantified using UAV-based biotope mapping and indicators including biophysical metrics (Net Primary Production, Water Retention, PM10 removal, and Land Surface Temperature), structural diversity indices (Shannon Diversity of land cover, vegetation, and tree structure), and visual–spatial proxies (Green View Index, Sky View Index, Water View Index, color metrics, and spatial openness). Subjective perceptions were derived from panoramic image-based questionnaires, with perception scores predicted using XGBoost and aggregated via SHapley Additive exPlanations (SHAP). Correlation analyses, spatial regression models, and partial least squares structural equation modeling were applied to explore relationships and pathways. Results show weak but significant positive associations in the urban park, whereas no overall correspondence was observed in the urban forest. Spatial mismatches were concentrated in biotopes with distinctive visual–ecological features and in fragmented areas. Green View Index is associated with higher perceptions in both sites, while the Sky View Index reduced perception in the forest context. These findings highlight strong context dependence in perceived–measured ecosystem service relationships and underscore the importance of integrating ecological structure and visual legibility in the design and management of the studied urban green spaces in Shanghai. Full article

Traffic flow prediction is important for traffic management and safety control in mountainous areas. In these environments, traffic flow is affected by complex terrain, changing weather, and mixed vehicle types, so the resulting time series often show strong fluctuation and poor stability. Many existing prediction models were developed for urban roads or flat highways, and their performance is therefore limited in mountainous scenarios. To address this problem, this paper proposes a hybrid model called DK-VCA Net. The model combines adaptive signal decomposition with a terrain-aware deep learning structure to separate useful traffic variation from complex noise. It also integrates traffic flow, speed, slope, and weather information to better describe mountain traffic conditions. The proposed method is evaluated using real traffic data collected at 5 min intervals from detection stations on the Guibi Expressway in Guizhou Province, China, during September 2020. Experimental results show that DK-VCA Net achieves better prediction accuracy than several representative baseline models, including 1D-CNN, LSTM, Transformer, STWave, and Mamba. Across the 15 min, 30 min, and 60 min forecasting tasks, the proposed model reduces the average RMSE by 14.8% compared with the conventional 1D-CNN model and by 8.9% compared with the baseline Transformer model. The ablation study further proves the effectiveness of the decomposition strategy, terrain-related features, and the attention mechanism. The results show that the proposed method is effective for traffic flow prediction in the studied mountainous highway scenario. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures. Full article

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments. Full article

Urban railway corridors—including abandoned, redesigned, and in-use lines—can support biodiversity and ecological connectivity in fragmented cities, yet their ecological dynamics and roles in Nature-based Solutions (NbS) remain poorly understood. Addressing this requires a context-sensitive approach that differentiates corridor types and compares their ecological functions. This study compares vegetation dynamics along railway corridors in two cities with contrasting socio-ecological contexts—Perth (Western Australia) and Beijing (China)—using a typology-based comparative approach. The results show that: (1) vegetation dynamics differ fundamentally between the two cities, with Perth characterized by vertically structured vegetation dominated by native tree layers and non-native disturbance-tolerant annual groundcover, while Beijing supports more continuous vegetation with widespread natural regeneration of native species; and (2) these differences correspond to distinct suggested NbS strategies. For Perth, NbS should combine phenology-aware management (wet versus dry seasons) with disturbance-based zoning and staged native planting strategies. In contrast, Beijing corridors are characterized by more uniform disturbance patterns but differentiated corridor typologies, indicating NbS structured around corridor-type management with a stronger emphasis on the support of native groundcover establishment and allowing for self-sustaining regeneration. These findings highlight how different contexts shape vegetation dynamics and provide comparative ecological insights for developing context-specific NbS for urban railway corridors. Full article

Railways constitute a core component of China’s national comprehensive transportation network, and their spatial organization and temporal accessibility jointly shape transport integration and system efficiency. Identifying their evolution from the dual perspectives of spatial expansion and time compression is therefore of both theoretical and practical significance. Drawing on fractal theory, this study examines the structural characteristics, evolutionary trends, and driving factors of railway networks in China’s five major urban systems from 2014 to 2024 from a “space–time” dual perspective. The results show that railway networks exhibit a staged pattern of “spatial filling preceding temporal correlation”, with a lag of approximately 1–8 years—about 1 year in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), 5 years in the Middle Yangtze River (MYR) region and Beijing–Tianjin–Hebei (BTH), and up to 8 years in the Chengdu–Chongqing (CC) region. In addition, clear regional differences are observed: the Yangtze River Delta (YRD) is polycentric, with the greatest potential, projected to continue rapid spatial growth until 2027 and to remain in a fast-growth phase of temporal correlation; GBA is highly coordinated; BTH is developed but characterized by dual-core agglomeration; CC grows rapidly with lagging functionality; and MYR is corridor-dependent with limited potential. These findings indicate that network functionality does not emerge synchronously with infrastructure expansion, but depends on subsequent improvements in operational organization and service capacity. Compared with single-scale-based indicators, the “spatial distribution–temporal correlation” framework more effectively captures network performance and provides quantitative support for transport optimization and coordinated regional development. Full article

In the context of China’s rapid urbanization and rural land marketization reforms, the entry of rural collectively owned commercial construction land into the market (ERCCCLM) coexists with the traditional government-led land expropriation, forming a dual land supply system. China’s dual-structure land ownership system—where urban land belongs to the state and rural land to rural collectives—aims to balance land market allocation efficiency with government regulation for public interests. However, significant differences exist between the two patterns in terms of revenue distribution, risk-bearing, and institutional constraints. Consequently, stakeholders including rural collective economic organizations, farmers, local governments, and development companies face dilemmas in selecting land supply patterns, thereby limiting land resource allocation efficiency. The research employs multidimensional economic analysis to systematically compare the ERCCCLM and land expropriation patterns, establishing a land supply pattern selection mechanism with land market price and compensation for expropriation as key variables. First, the expenditure and revenue of stakeholders in both patterns were clarified based on relevant documents, and investment revenue models were constructed. Second, through comparative analysis of revenue formation mechanisms across land supply patterns and sensitivity analysis of multi-scenario calculations, the land market price and compensation for expropriation are identified as key variables determining economic revenue. The findings indicate that when the land market price exceeds compensation for expropriation, ERCCCLM generates higher economic revenue for the rural collective economic organization and farmer. Conversely, when the land market price is equal to or lower than the compensation for expropriation, land expropriation provides more stable revenue. The land expropriation and ERCCCLM examined in this research represent a unique land expropriation and utilization system exclusive to China. The proposed selection mechanism improves land market distribution efficiency and informs policy discussions on optimizing land supply patterns, ensuring a balance between market efficiency and stakeholder equity. Full article

South Africa’s food system reflects a growing imbalance between nutritionally rich indigenous foods and the increasing dominance of commercially processed alternatives. Despite well documented health, cultural, and environmental benefits, indigenous leafy vegetables and edible insects remain marginal within formal markets and everyday diets. This systematic review synthesised 141 empirical and theoretical studies to examine how household attitudes, consumption behaviours, and market structures interact to shape the role of indigenous foods in South Africa. The review identifies a consistent pattern in which positive perceptions of indigenous foods do not translate into regular consumption. Rural households continue to utilise these foods as part of seasonal and livelihood strategies, while uptake in urban areas remains uneven and context specific. Emerging interest among certain consumer segments highlights potential for product diversification and market development, particularly where indigenous foods are adapted to align with modern preferences. However, this potential is constrained by weak value chain integration, limited standardisation, and the absence of reliable consumption data. These structural limitations restrict both market participation and consumer access, reinforcing the peripheral position of indigenous foods within the broader food system. The findings suggest improving availability, strengthening markets, and enhancing positioning critical for inclusion of indigenous foods in diets. Full article