Search Results (10,682)

This paper explores how collective resilience is built and sustained through situated, relational, and reparative approaches to design within conditions of deep spatial inequality. Focusing on Johannesburg’s Slovo Park settlement and the long-standing 15 year collaboration between the Slovo Park Community Development Forum (SPCDF) and 1to1—Agency of Engagement, it examines how participatory tool-making—centred on two keystone tools, the Blue File (a community-held, cloud-based knowledge repository) and the Timeline Tool (a multi-workshop planning and accountability device)—supports iteration, voice change, leadership transitions, and decision-making “with the map in hand.” Grounded in Southern urbanist theory and spatial justice scholarship, the paper re-politicises resilience as ongoing negotiation, repair, and shared authorship. It details how a map-based pointing practice translated situated knowledges into spatial choices; how the Blue File preserved continuity and evidence through leadership turnover; and how the Timeline Tool embedded care and transparency. Alongside benefits, the paper surfaces key tensions—expectation management, idea overload, triage and prioritisation, and legitimacy during leadership changes—and shows the concrete decision protocols used to move from many inputs to buildable design options. It concludes with ethical reflections for practitioners working in postcolonial/post-apartheid contexts and offers transferable lessons for allied urban conditions. Full article

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

This study reconstructs and projects relative sea-level change (RSLC) along Chesapeake Bay, a global hotspot for sea-level rise, from 1900 to 2100 by statistically extrapolating observed tide gauge trends, rather than employing climate model-based scenarios. The approach integrates global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS) to evaluate both past and future behavior. Tide gauge data reveal that Chesapeake Bay’s sea level has accelerated at 0.099 ± 0.013 mm/year² since 1992, with a linear rate of 1.26 mm/year since 1900, slightly outpacing global averages. LS, primarily driven by glacial isostatic adjustment (GIA) and sediment compaction, has been the dominant contributor to RSLC since the early 20th century, accounting for up to 71% of the RSLC prior to 1992 across 15 tide gauge stations. However, with GMSLR accelerating at 0.120 ± 0.025 mm/year², the relative contribution of LS to RSLC is projected to decline to 31–43% by 2100. The reconstructed RSLC for the 20th century ranges between 32 and 44 cm, while extrapolated projections for the 21st century indicate a further increase of 53–99 cm. By 2100, GMSLR is expected to contribute to 60–70% of total RSLC. Spatial variability in RSLC across 15 tide gauge stations reflects differing geological conditions and anthropogenic influences such as groundwater withdrawal and construction-induced subsidence. These findings highlight the critical need for adaptive strategies to mitigate the impact of rising sea levels on coastal communities and infrastructure in the Chesapeake Bay region. Continued monitoring, improved modeling, and targeted resilience planning are essential to address the accelerating threats posed by sea-level rise and to ensure the sustainability of vulnerable coastal areas. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

Symbiosis and synergy among urban uses are key determinants of spatial quality, liveability, and resilience. While symbiosis denotes the coexistence of users and functions within specific places, synergy refers to the collective benefits emerging from their interaction. These dynamics are especially relevant in city centres and main streets, which serve as structural and social backbones of urban life. This article applies the SyM_SyN Method to Zwycięstwa Street in Gliwice, Poland, to assess the intensity and distribution of symbiotic and synergistic relations. The analysis identified significant spatial deficiencies that weaken the coherence and attractiveness of the street. The results demonstrate how a systematic, data-driven evaluation can expose hidden weaknesses in urban structures. Importantly, from the perspective of the smart city paradigm, liveability and responsiveness of urban spaces cannot be reduced to technology-driven systems of sensors and devices. They must also be understood in terms of human-scale interactions and the ability of urban form to support them. Beyond its methodological contribution, the study emphasises the practical implications for urban renewal: reinforcing positive interactions between adjacent uses enhances street vitality, improves social inclusiveness, and supports more sustainable development strategies. The SyM_SyN Method thus provides both an analytical framework and a decision-support tool for designing user-oriented, high-quality urban spaces within the broader smart and sustainable city paradigm. Full article

The rapid proliferation of e-commerce has reshaped the spatial logic and facility typologies of urban logistics. While the literature on logistics facility location selection is extensive, there is limited understanding of how the relative importance of location criteria varies across facility types shaped by e-commerce. This study addresses this gap by analyzing the location criteria of logistics facilities of different sizes using a multi-criteria decision-making (MCDM) approach. Twenty-five criteria, identified through a literature review and feedback from seven experts in the Istanbul e-commerce logistics sector, were analyzed using the Fuzzy Simple Additive Weighting (SAW) method. The relative weights of criteria were calculated for three facility scales, macro-, meso-, and micro-scales, to reveal how location priorities vary across scales. Proximity to main arteries ranks first across all scales (macro: 0.317, meso: 0.431, micro: 0.409). Land rental values are highly prioritized at both the macro- and meso-scale, while population density ranks prominently at the macro- and micro-scale. At the meso-scale, shopping mall proximity gains notable weight, whereas intermediate arteries stand out as a key factor at the micro scale. These findings advance the understanding of scale-sensitive dynamics in urban logistics and provide a framework for more adaptable and sustainable logistics planning. Full article

Green belts in metropolises face a significant contradiction between ecological protection constraints and urban sprawl, necessitating effective planning and management. Existing studies have primarily focused on a single dimension, while the factors influencing the spatial evolution of green belts are complex and diverse. This study establishes a multi-objective quantitative analysis framework, utilizing quantitative analysis methods such as average nearest neighbor analysis, landscape ecological index analysis, land–use transition matrix, kernel density estimation, and spatial autocorrelation models. Taking the green belt area of Shijiazhuang as a case study, this research systematically analyzes the spatial evolution characteristics of the region from 2015 to 2024. The findings reveal spatial patterns such as the small-scale and dispersed expansion of industrial land, increasing fragmentation of ecological spaces, ongoing encroachment on agricultural land, differentiated growth of service industry spaces, and the uncontrolled sprawl of residential areas in villages and towns during rapid urbanization. These patterns lead to increased ecological risks, imbalanced urban–rural development, and lagging infrastructure. To address these challenges, this study proposes a planning strategy of “adjusting the primary industry, restricting the secondary industry, and promoting the tertiary industry,” aiming to resolve the conflict between ecological protection and urban expansion in metropolitan green belts, ensuring their orderly development. This research provides insights for the sustainable development of green belts in Metropolises of developing countries during the rapid urbanization process. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

Yield prediction in longline aquaculture is essential for evaluating environmental impacts, facilitating risk assessment, and promoting sustainable management in fisheries. However, since cultured organisms in longline aquaculture are submerged and cannot be directly observed, existing yield prediction approaches are mostly based on indirect environmental proxies, which often lead to unsatisfactory accuracy. The Shadow Geometry Inversion for Aquaculture (SGIA) method enables direct and accurate yield estimation in longline aquaculture by utilizing the submergence state of buoys to infer load, which is determined by the weight of the cultured organisms and estimated by shadow lengths combined with solar altitude angles and buoy physical parameters in high-resolution unmanned aerial vehicle (UAV) imagery. Experiments have been conducted in a water body located in Shanghai and Fuding to validate the effectiveness of the SGIA method. The best results were achieved under solar altitudes of 10–25° and calm water conditions. Under these conditions, the SGIA-predicted yields closely matched the measured loads in the Shanghai controlled experiment (R² = 0.985, MAPE = 9.19%). In the Fuding field application, the model effectively captured spatial variations in buoy loads across the farming area, demonstrating its practicality and scalability for large-scale yield mapping in real aquaculture environments. Full article

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article

Coal mine gas with methane concentrations below 8% cannot sustain stable self-combustion, posing significant challenges for safe utilization and greenhouse gas mitigation. To address this limitation, we developed a large-scale industrial square rotary regenerative thermal oxidizer (RTO) capable of high-efficiency oxidation under ultra-low methane conditions. This work integrates multi-scale computational fluid dynamics (CFD) modeling, laboratory and pilot-scale physical experiments, and multi-physics coupled simulations to capture the complex interactions of fluid flow, species transport, and thermal response in regenerative ceramics. Compared with conventional circular or three-bed RTOs, the proposed square rotating design achieves 13% higher heat storage utilization, 15% smaller floor area, and enhanced spatial uniformity of the temperature field. Multi-scale simulations reveal that increasing methane molar fraction (CH₄) from 0.012 to 0.017 raises the peak temperature from 1280 K to 1350 K, reduces the burnout height from 1.18 m to 1.15 m, and, under constant oxygen supply, extends the high-temperature zone to 1450 K with a stabilized burnout position at 1.06 ± 0.01 m. Incorporating a 15° conical expansion combustion chamber increases local turbulent kinetic energy by 17.4%, accelerating oxidation while maintaining methane removal rates > 98% within an optimized bottom blowing time of 30–90 s. This study not only provides validated design thresholds for ultra-low concentration methane oxidation—such as temperature windows, buffer zones, and switching cycles—but also offers an engineering framework for scaling RTO systems to industrial coal mine applications. This advances both energy recovery efficiency and methane emission control, demonstrating clear advantages over existing RTO configurations. Full article

Understory vegetation plays a pivotal role in enhancing forest biodiversity, and its restoration is crucial for sustainable forest development, energy flow, and nutrient cycling. However, the dynamics of the biomass, diversity, and species composition of understory vegetation in plantations in south China, along with their key drivers, remain poorly understood. This study investigated four mature plantation types (Pinus massoniana, Pinus caribaea, Cunninghamia lanceolata, and mixed Chinese fir–broadleaf forests) in south China through plot surveys, environmental factor measurements, and structural equation modeling (SEM) to explore the diversity, biomass allocation patterns, and driving mechanisms of understory vegetation. The results demonstrated the following. (1) The introduced Caribbean pine forests exhibited higher shrub biomass than native Masson pine forests, which was driven by their high canopy openness favoring light-demanding species (e.g., Melicope pteleifolia, IV = 33.93%), but their low mingling degree limited herb diversity. (2) Masson pine forests showed superior shrub diversity due to their random spatial distribution and higher soil total potassium (TK) content. (3) Mixed Chinese fir–broadleaf forests achieved 24.50–66.06% higher herb biomass compared to coniferous monocultures, supported by high mingling degree, random spatial configuration, and phosphorus-potassium-enriched soil, with concurrently improved herb diversity. SEM revealed that stand structure (DBH, density, mingling degree) directly drove shrub diversity by regulating light availability, while herb biomass was primarily governed by soil total phosphorus (TP) and pH. Canopy-induced light suppression negatively affected herb diversity. We recommend optimizing stand density and canopy structure through thinning and pruning to enhance light heterogeneity alongside supplementing slow-release P fertilizers in P-deficient stands. This study provides theoretical support for the multi-objective management of south China plantations, emphasizing the synergistic necessity of stand structure optimization and soil amendment. Full article

Forest structure, including trees, deadwood and tree-related microhabitats, is a key determinant of forest biodiversity. Their relative contribution in shaping local (alpha) biodiversity and its variation (beta) between sites remains unclear. We assessed how forest characteristics shape alpha and beta diversity of beetle communities in mixed silver fir–beech forests within the Vallombrosa Nature Reserve (Tuscany, Italy). We sampled 47 circular plots recording single-tree attributes, deadwood volume and decay stage, and the occurrence of tree-related microhabitats. Beetle assemblages were surveyed using window flight traps, yielding over 11,000 individuals belonging to 187 species, 20% of those known from central-southern Italian forests, 58% of which were listed in the Italian Red List of Saproxylic Beetles and 10% of which were threatened. Statistical models (GLMs and GDMs) revealed that alpha diversity was driven by fine-scale features, including tree species composition, microhabitats (cavities, bark, epiphytes) and deadwood diversity. In contrast, beta diversity was shaped by stand structure and inter-stand heterogeneity. Our results highlight the need for conservation strategies that simultaneously maintain tree-level heterogeneity and secure variation across the landscape. Management should therefore combine retention of microhabitats and diverse deadwood substrates with promotion of structurally diverse, mixed stands to sustain beetle diversity at multiple spatial scales. Full article

Railway hazardous chemical transportation, a high-risk activity that endangers personnel, infrastructure, and ecosystems, directly undermines the sustainability of the transportation system and regional development. Traditional risk management algorithms, which rely on empirical rules, result in sluggish emergency responses (with an average response time of 4.8 h), further exacerbating the environmental and economic losses caused by accidents. The standalone DBSCAN algorithm only supports static spatial clustering (with unoptimized hyperparameters); it lacks probabilistic reasoning capabilities for dynamic scenarios and thus fails to support sustainable resource allocation. To address this gap, this study develops a DBSCAN–Bayesian network fusion model that identifies risk hotspots via static spatial clustering—with ε optimized by the K-distance method and MinPts determined through cross-validation—for targeted prevention; meanwhile, the Bayesian network quantifies the dynamic relationships among “hazardous chemical properties-accident scenarios-material requirements” and integrates real-time transportation and environmental data to form a “risk positioning-demand prediction-intelligent allocation” closed loop. Experimental results show that the fusion algorithm outperforms comparative methods in sustainability-linked dimensions: ① Emergency response time is shortened to 2.3 h (a 52.1% improvement), with a 92% compliance rate in high-risk areas (e.g., water sources), thereby reducing ecological damage. ② The material satisfaction rate reaches 92.3% (a 17.6% improvement), and the neutralizer matching accuracy for corrosive leaks is increased by 26 percentage points, which cuts down resource waste and lowers carbon footprints. ③ The coverage rate of high-risk areas reaches 95.6% (a 16.4% improvement over the standalone DBSCAN algorithm), with a 27.5% reduction in dispatch costs and a drop in resource waste from 38% to 11%. This model achieves a leap from static to dynamic decision-making, providing a data-driven paradigm for the sustainable emergency management of railway hazardous chemicals. Its “spatial clustering + probabilistic reasoning” path holds universal value for risk control in complex systems, further boosting the sustainability of infrastructure. Full article