Search Results (4,686)

Climate change is impacting ecosystems and infrastructure worldwide, prompting the construction industry to adopt sustainable practices to reduce its carbon footprint. Nature-based Solutions (NbS), such as algae-based materials, are emerging as beneficial alternatives to traditional construction materials. However, research on these innovations in South Africa is limited. This study presents findings from a preliminary assessment of algae-based materials in the country’s construction sector. A post-positivist stance was employed, using a quantitative research design with a questionnaire survey targeting built environment experts. Data analyses were performed using frequency counts, mean item scores, the Kruskal–Wallis H-test, and Fuzzy Synthetic Evaluation (FSE). The findings show low awareness and usage of algae-based materials, with bio-carbon cement as the only widely recognized option. However, the study identifies potential applications of algae in masonry, wall finishes, and concrete for structural elements. FSE indicates that respondents perceived algae-based materials as having significant environmental, social, and economic implications for sustainable construction. Based on responses from 63 built environment professionals from one South African province, this study provides context-specific insights into the potential integration of algae-based materials in the construction sector and identifies avenues for future research on practical applications, regulatory challenges, and the potential for scaling in other developing countries. Full article

This study quantifies long-term surface urban heat island (SUHI) dynamics (2001–2016) in three climatically diverse South American metropolises—Buenos Aires (temperate), Bogotá (tropical highland), and Lima (arid coastal)—using MODIS data (0.05° resolution), comparing Streutker’s Gaussian model with a quantile-based approach (SUHI = 95th urban LST percentile—rural LST median). Method performance varied with surface heterogeneity: Gaussian modeling underestimated SUHI in coastal cities (nocturnal bias = +1.5 °C in Buenos Aires; R² ≤ 0.17 in Lima) due to non-Gaussian LST distributions driven by oceanic moisture advection, while quantile-based estimation proved robust in complex terrains (Bogotá agreement: day R² = 0.67, night R² = 0.53). Distinct SUHI signatures emerged: Buenos Aires exhibited stronger nocturnal SUHI (max 3.79 °C, linked to reduced rural evaporative cooling), Lima showed winter daytime Urban Cool Islands (−1.5 °C) from higher heat absorption in semi-desert surroundings, and Bogotá peaked in October (7.27 °C daytime SUHI) with altitudinal suppression of convective rainfall. Vegetation strongly modulated surface fluxes, with urban NDVI/EVI correlations (R² ≤ 0.29) confirming evaporative regulation’s role in halving thermal amplitudes (10 °C urban vs. 5 °C rural), while rural coupling varied with moisture availability (R² ≤ 0.65). Coastal proximity and elevation were key divergence drivers, disrupting surface energy partitioning. We recommend quantile methods for moisture-heterogeneous regions and advocate hydrometeorologically informed mitigation: optimizing green infrastructure for latent heat flux (Buenos Aires), leveraging nocturnal drainage flows (Bogotá), and integrating natural cool-island dynamics into water-sensitive design (Lima). Full article

The digital industrial cluster policy is a key policy instrument for fostering the digital economy and facilitating urban low-carbon transition. Given that no official carbon quota allocation system has yet been established at the city level in China, this paper constructs a multidimensional urban carbon quota index that integrates social, economic, and ecological dimensions, and uses it to measure the carbon quota level of 297 prefecture-level-and-above cities in China over the period 2006–2024. Treating China’s digital industrial cluster policy as a quasi-natural experiment, we employ a staggered difference-in-differences (DID) design to systematically evaluate the policy’s effect on urban carbon quota and the underlying mechanisms. The results show that ① the digital industrial cluster policy significantly raises the urban carbon quota level; ② mechanism tests indicate that this effect is channeled through three paths—stimulating green technological innovation, promoting industrial structure advancement, and improving electricity energy efficiency; ③ heterogeneity analysis reveals that the effect is more pronounced in western regions, in central cities, and in areas with more advanced digital infrastructure and higher carbon quota endowment; ④ spatial econometric results further show that the policy not only enhances local carbon quota but also generates a significant positive spatial spillover effect, raising the carbon quota level of neighboring cities. These findings offer insights for optimizing urban carbon quota allocation through spatial coordination, path coupling, and targeted policy design. Full article

Histopathology and immunohistochemistry (IHC) are central to COVID-19 tissue evaluation. However, conventional manual scoring is limited by its semiquantitative nature and potential observer variability. In this retrospective study of experimentally infected mice, we implemented a semi-automated digital pathology workflow using commercially available software to quantitatively assess SARS-CoV-2 nucleocapsid (N) immunolabeling in lung (n = 135) and brain (n = 67) tissues. The performance of digital quantification was evaluated against conventional manual scoring, and its association with virological and pathological parameters was assessed across different stages of disease progression. Digital IHC quantification demonstrated strong agreement with manual scoring in both lung [R = 0.94, p < 0.0001, concordance correlation coefficient (CCC) = 0.95] and brain [R = 0.98, p < 0.0001; CCC = 0.977], demonstrating concordance between the two approaches. In addition, digital SARS-CoV-2 N immunolabeling quantification showed significant positive correlations with viral RNA levels, infectious viral titers, and histopathological scores, supporting its association with independent measures of SARS-CoV-2 infection and lesions. Although computational image analysis requires additional infrastructure, technical expertise, and increased analysis time, it provides an objective continuous quantitative measure of SARS-CoV-2 N immunolabeling that complements conventional manual assessment and may reduce observer-dependent variability. Full article

Water infrastructure underpins progress toward Sustainable Development Goal 6 and SDG 9, yet financing shortfalls constrain investment in many developing economies. This paper examines whether market-oriented water allocation reform—specifically water rights trading (WRT)—can mobilize sustainable infrastructure financing. Exploiting China’s 2014 provincial WRT pilot as a quasi-natural experiment, we estimate difference-in-differences models using province-level panel data from 2010 to 2023 (an effective sample of 434 province–year observations across 31 provinces), supplemented by event-study analysis, mediation modeling, and heterogeneity tests. WRT raises per capita water infrastructure investment by approximately 18.3% (p < 0.01), with effects materializing gradually over three to four years and persisting thereafter. Three channels mediate the relationship: water resource value realization (average causal mediation effect, ACME = 0.062), financing cost reduction (ACME = 0.048), and marketization-driven investment incentives (ACME = 0.071). Treatment effects are more pronounced in water-scarce provinces and regions with stronger market institutions. These findings indicate that water market reform can unlock sustainable infrastructure financing, particularly when complemented by pricing reform, financial market development, and regionally tailored implementation. Full article

Heat waves and cold snaps affect public health, energy demand, and urban infrastructure, yet separating human influence from natural climate variability remains difficult. We quantify changes in temperature extremes over the contiguous United States (CONUS) using daily climate simulations under natural-only, greenhouse-gas-only, and all-forcings historical scenarios and the future SSP3-7.0 scenario. At each land grid cell, a heat wave is six or more consecutive days with daily maximum temperature above the 99th percentile. In CMIP5 CCSM4 simulations (1950–2005), anthropogenic forcing increases heat wave frequency by 5.02 events per year relative to natural forcing (p = 0.000028), and yields an effective amplification ratio of 3.51 (all-forcing relative to natural-only). The CMIP6 CanESM5 simulations (1950–2014) show the same warm shift, and extending them to 2045 under SSP3-7.0 roughly quintuples the rate of hot day increase (95.5 versus 20.6 days per year) and raises heat wave frequency by 5.66 events per year, with an effective amplification ratio of about 2.72. Both ratios exceed one, but are not directly comparable in magnitude. The largest changes fall on regions and cities growing fast in population from 2000 to 2025, underlining the need for heat mitigation planning in these cities. Full article

Surface deformation monitoring plays a crucial role in various fields, including geology, geodesy, and environmental studies. This study provides insights into the application of non-linear PSInSAR techniques for studying surface deformation associated with urban growth and groundwater pumping. A Persistent Scatterer InSAR (PSInSAR) analysis employing a time-segmented (non-linear) velocity model was applied to 119 Sentinel-1A ascending acquisitions spanning 10 February 2017 to 27 December 2020, yielding 38,594 persistent scatterers. The 33,238 scatterers were retained at a temporal coherence threshold of 0.70 and a mean density of 18.6 PS/km² across the district, rising to 57 PS/km² within the urban core, with annual mean line-of-sight (LOS) velocities measured for each calendar year. Temporal validation identifies a modest, origin-dependent forecasting benefit from segmentation in the urban core but no consistent advantage under contiguous block testing. Land use/land cover (LULC) was mapped from Landsat imagery for 2000, 2007, 2014, and 2020 using a random forest classifier in Google Earth Engine. LOS velocities span approximately +40 to −60 mm/yr over the mapped range, with the most negative values concentrated in the central urban core. Paired annual comparisons, accounting for spatial dependence and Holm adjustment across 15 tests, did not establish significant changes in the 0–2 km core. Significant contrasts occurred in two comparison annuli, but their physical origin remains unresolved. Land use classification maps for the years 2000, 2007, 2014, and 2020 show a significant expansion in built-up areas, coupled with a reduction in vegetation and, after adjustment for classification accuracy, in water bodies, which indicates the increasing pressure of urban growth on the environment. Lahore has witnessed a substantial increase in population due to urban migration, natural growth, and economic opportunities. Subsidence can lead to structural damage to buildings, infrastructure, and underground utilities. It is a serious concern in Lahore, particularly in areas where groundwater pumping is excessive. Full article

Binary oppositions used in scientific practice—such as male/female for classifying organisms, natural/social for delimiting causal explanations, signal/noise as variations recorded by measurements, and normal/anomalous for sorting meaningful observations—do not merely describe a preclassified world. They shape what researchers expect to find, what instruments and databases are designed to record, what counts as reliable evidence, and what is relegated to error or exception. This exploratory, theory-building study draws on 10 in-depth interviews (2 per participant) with 5 researchers in paleoecology, microbial ecology, astronomy, glaciology, and human geography/climate science. Each participant’s interviews were integrated into a field-specific case, yielding five cases for cross-case analysis. I examined reported binary distinctions and related classificatory boundaries across three interconnected moments—observation, instrumentation, and data production—and distinguished their evidentiary status in each case. Binary thinking constitutes a particular form of categorization: it organizes phenomena into two mutually exclusive positions, often treated as exhaustive. When embedded in research procedures and infrastructures, these distinctions can shape what becomes observable and recordable, while rendering phenomena that do not fit either position harder to recognize, preserve, or investigate. Through abductive comparison, I conceptualize observations, ambiguities, and possibilities that do not fit previously established categories as epistemic excess and propose epistemic accountability as the practice of documenting classificatory decisions, preserving uncertainty and the production history of data, and keeping categories open to revision. The findings generate propositions for field-specific research rather than estimates of disciplinary prevalence and suggest that making classificatory choices visible may support more inclusive and reliable knowledge and policy. Full article

Next-generation gaseous fuel infrastructure for low- and zero-carbon gaseous fuels (hydrogen, ammonia, and hydrogen-blended natural gas) is planned to expand, but most announced hydrogen pipeline and storage projects have not reached a final investment decision. Hydrogen infrastructure safety research has centered on material integrity, risk assessment, and standards, while systematic syntheses of ontologies and knowledge graphs for this infrastructure remain relatively limited. This review classifies approaches to safety knowledge by their basis for risk judgment into traditional risk assessment, data-driven, and knowledge-structure-based approaches. Across these approaches, the basis for judgment tends to be fixed in advance, largely during design, training, or knowledge encoding. Computer science, by contrast, has a methodologically mature line of research reasoning only over the parts a query needs. Comparing the two lines reveals a direction that appears promising, in which the ontology remains the knowledge structure and only the necessary analyses run at query time. Prerequisite challenges include confirmation of response times, a shared safety vocabulary, scarce accident data, and verification methods. Close applications were found only outside gaseous fuel infrastructure. These observations rest on abstract-level screening and hold only within the search scope of this review, and the direction has not been demonstrated. Full article

Artificial Intelligence (AI) systems are increasingly being applied to complex social and environmental challenges, but their implementation across different application domains requires appropriate combinations of computational techniques, data sources, deployment environments, user interfaces, and responsible-AI mechanisms. This study proposes an AI Integration Framework for examining and configuring AI-enabled social innovation systems in support of the Triple-Zero Agenda: zero poverty, zero unemployment, and zero net carbon emissions. The framework is derived from a qualitative multi-case analysis of five social enterprises operating in agriculture, healthcare, education, clean energy, and employment. The analysis examines the AI technologies employed, their development and deployment environments, accessibility mechanisms, data-collection approaches, privacy mechanisms, explainability practices, human-in-the-loop controls, ethical review mechanisms, and measurable impact indicators. The cases demonstrate the use of heterogeneous AI configurations, including predictive machine learning in mobile and cloud environments, natural language processing in application and SMS-based systems, adaptive learning algorithms in web platforms, AI-based energy optimization integrated with IoT and edge devices, and AI-driven skill-job matching through mobile applications. The cross-case analysis indicates that effective AI deployment depends not only on the choice of AI technique but also on the configuration of supporting data, infrastructure, interaction, privacy, explainability, and human-oversight mechanisms. Based on these observations, the study organizes these dimensions into an AI Integration Framework that provides a structured basis for analyzing and configuring AI systems for socially oriented applications. The framework also connects system configurations with operational, social, economic, and environmental outcomes, thereby providing a computer science-oriented perspective on the design, deployment, and scalability of AI-powered social innovation. Full article

Liquefied natural gas (LNG) has long been regarded as a transition fuel, yet growing concerns over life-cycle greenhouse gas emissions have intensified interest in retrofit options for existing LNG infrastructure. This review examines whether hydrogen and ammonia can be introduced into the LNG value chain through blending, co-firing, and partial repurposing of existing assets. It synthesizes evidence across five infrastructure segments: liquefaction, marine transport, regasification and storage, pipeline distribution, and end-use combustion. The review finds that hydrogen is currently the more mature option for pipeline transmission and distribution, with pilot and demonstration projects supporting blends of around 20% by volume. However, material compatibility, leakage, metering, and end-use constraints remain important barriers. Ammonia offers advantages in storage and shipping because of its established handling legacy, but its combustion performance, toxicity, and retrofit requirements limit near-term deployment, especially in gas turbine applications. Liquefaction terminals appear to be the least suitable segment for either fuel because existing cryogenic process trains are optimized for methane and show limited compatibility with hydrogen or ammonia. Overall, the evidence suggests that retrofit strategies should be segment-specific rather than uniform: hydrogen is more promising for pipeline-oriented repurposing, ammonia is more suitable for storage- and logistics-oriented applications, and both fuels face substantial technical and regulatory barriers in LNG liquefaction systems. Full article

Land capitalization has become a key mechanism in China’s rural transformation, converting dispersed natural villages into centralized resettled communities for landless peasants and reshaping their everyday life through urbanized housing, infrastructure, and governance arrangements. However, these visible spatial gains do not necessarily translate into equitable long-term outcomes. This paper examines the structural tension between spatial improvement and contested justice through a case study of Guange Village, China. It investigates how land capitalization reshapes rural resettlement after spatial allocation has been completed. Drawing on long-term ethnographic fieldwork, semi-structured interviews, local socio-economic data, and documentary materials, the study reinterprets the distributional, procedural, and recognition dimensions of spatial justice through a temporal lens, incorporating insights from the Impoverishment Risks and Reconstruction model to examine how justice disputes emerge and accumulate after resettlement. The findings show that spatial improvement did not eliminate justice disputes, but displaced them into the long-term processes of livelihood reconstruction, community governance, and social reproduction. Distributionally, compensation and land-rent payments were insufficient to offset rising living costs and employment mismatches. Procedurally, conflicts shifted from land acquisition to property management, community operation, and collective responsibility. In terms of recognition, standardized apartment living disrupted rural practices, while family-based inheritance and intergenerational support absorbed part of the institutional tension. The paper concludes that spatial justice in land capitalization should be understood as a dynamic post-allocation process rather than a one-off outcome of compensation, and calls for rural resettlement policies that integrate livelihood support, participatory governance, and recognition of rural social relations. Full article

Strategic landscape planning requires spatially explicit methods for assessing peri-urban mining landscapes. This study develops a multi-scale geospatial framework to assess 1284 mining ponds in Hungary through land-cover patterns, spectral indicators, ring-zone analysis and mining-pond spatial linkage patterns. The study shifts assessment from individual pond units to spatially grouped mining-pond complexes and distinguishes water surface expansion from changes in terrestrial vegetation, wetlands, and reedbeds. A detailed case study of the Délegyháza gravel mining-pond system illustrates how recreational use, shoreline transformation and suburbanization pressure are spatially associated with landscape changes. Mining ponds frequently occur in spatially grouped mining-pond complexes with distinct zonal structures. Shoreline zones show higher vegetation regeneration potential, while outer ring zones are more strongly affected by agricultural, urban and infrastructural pressures. In Délegyháza, the declining Normalized Difference Vegetation Index (NDVI) and increasing Bare Soil Index (BSI) values during 2016–2024 indicate reduced vegetation activity and more artificial or disturbed surface characteristics. Open water surface expansion should not be interpreted automatically as ecological regeneration. Differentiated buffer zoning, mining-pond-complex-level planning and post-mining landscape monitoring can support the integration of mining ponds into blue-green infrastructure networks and EU Nature restoration objectives. Full article

Oilfield subsidence threatens infrastructure and sustainable development. Although facility detection, factor-association analysis, and deformation forecasting have each advanced considerably, their systematic integration remains insufficiently explored for oilfields in agricultural plains, where C-band SAR decorrelation over cropland and wetland yields sparse and uneven deformation observations. This constraint complicates facility extraction, quantitative analysis of associations among interacting factors, and robust temporal prediction. To address these challenges, this study develops and applies an integrated modular workflow for the monitoring, analysis and prediction of surface subsidence in a typical oil production area of Songyuan City, China. We first used PS-InSAR for high-precision deformation monitoring and validated the results with leveling survey data. Subsequently, the YOLOv12 model was utilized for automated oil well identification, and the Geodetector model was applied to quantify the spatial associations between subsidence and the examined natural and anthropogenic factors. Finally, a hybrid LSTM–Transformer model was developed for time-series prediction and applied to forecast deformation through November 2025, twelve months beyond the final SAR acquisition. Results indicate significant spatial differentiation in deformation, with a maximum subsidence rate of −26.0 mm/y. Analysis identifies oil well density and surface elevation as the leading spatial factors among those examined, and the buffer zone analysis indicates a distance-related association of approximately 700 m between oil wells and subsidence rates. The hybrid model demonstrated superior overall accuracy compared to standalone LSTM and Transformer models. The findings provide concrete scientific support for risk mitigation in oil cities. Full article

This review examines how the hydrological performance of implemented, ground-based urban green and blue infrastructure (GBI) and nature-based solutions (NBSs) is monitored and assessed. It combines bibliometric mapping, a targeted systematic review, and an indicator crosswalk. The bibliometric dataset included 2426 publications, while the targeted review analyzed 12 empirical-monitoring articles and 7 assessment-framework publications. Empirical studies covered catchment and sewershed monitoring, practice-scale inlet–outlet monitoring, and local infiltration or media-hydraulic characterization. Frameworks instead interpreted hydrological evidence alongside broader planning, management, resilience, and governance objectives. The crosswalk compared thirteen water-related functions, of which six were shared between the two corpora. Infiltration was the only function for which the two corpora shared a closely related primary measurand, although even here they relied on different test designs and measurement resolutions. The other five shared functions corresponded functionally, but diverged in measurand, temporal basis, denominator, or comparator. The remaining seven functions were represented in one corpus only, accounting for 26 of the 66 empirical and 23 of the 42 framework indicator records. Full article