Search Results (57)

Coordinated management of drip irrigation water demand and lateral canal supply is a critical strategy for improving water use efficiency in arid irrigation districts; however, under water-deficit conditions, the efficient and equitable allocation of limited canal water among multiple drip irrigation systems remains largely unresolved. This study developed a bi-objective cooperative water allocation and scheduling model for a lateral canal serving 11 subordinate drip irrigation systems. Subject to canal diversion flow balance and total deficit constraints, the model simultaneously minimized (i) the mean coefficient of variation (CV) of water allocation duration within rotation irrigation groups, targeting temporal uniformity, and (ii) the sum of squared deviations of the water supply satisfaction rate across systems, targeting distributional equity. Water demand inputs were derived from a localized FAO-56 Penman–Monteith irrigation schedule for Jinghe County with stage-specific crop coefficients. A hybrid binary–continuous NSGA-II encoding with a dynamic intra-group flow allocation mechanism was employed. For the baseline deficit scenario (Early May, supply-to-demand ratio β = 67.76%), the model partitioned the 11 systems into four rotation groups with a mean CV of 3.15 × 10⁻³, while the sum of squared deviations of the satisfaction rate decreased from 4.366 under the empirical scheme to 1.50 × 10⁻⁴, confining all systems to 67.38–68.45% and eliminating the coexistence of over-supply and complete deprivation (Wilcoxon signed-rank test, p < 0.001; Cohen’s d = −1.698). The Pareto front revealed a significant efficiency–equity trade-off (Spearman’s ρ = −0.9999), and NSGA-II outperformed SPEA2 by approximately 29-fold and 22-fold in the two objectives. Robustness was confirmed across three deficit scenarios and algorithm parameter sensitivity analyses (CV < 2%). The study offers methodological support for refined water allocation management of terminal canal systems in arid regions. Full article

Closed-loop habitats in space-analogue and other extreme environments must sustain life and performance within tightly bounded constraints on water, energy, food, air revitalisation, waste processing, space, and crew time. Existing assessments emphasise engineering reliability, resource throughput, and human factors, but less often address everyday practices—meals, hygiene, privacy, social rituals, sensory comfort, and care work—as variables relevant to sustainability. This conceptual paper develops closed-loop hospitality as an integrated sustainability assessment framework for analysing how such practices mediate between resource constraints and environmental, social, and operational outcomes. Drawing on sustainability assessment, circular resource use, resilience, socio-technical systems, habitability, and hospitality theory, the framework links constraint variables—including resupply interval, closure ratio, bottlenecks, redundancy, volumetric capacity, crew time scarcity, intercultural crew composition, and health-support capacity—to hospitality design choices and measurable indicators. It identifies outcomes such as resource intensity, recycling and waste burden, system reliability, labour distribution, equity and legitimacy of access, psychosocial wellbeing, compliance, and adaptive resilience. The paper supports threshold setting, scorecards, and protocol design in analogue habitats, arguing that sustainable habitation requires balancing resource stewardship with minimum viable hospitality: preserving dignity, care, privacy, cohesion, and operational continuity within closed-loop limits. Full article

Urban fire prevention is shifting from reactive response to proactive risk governance, yet current approaches often overlook risk-type heterogeneity, spatial dependencies, and underlying behavioral mechanisms, especially equitable risk distribution among vulnerable groups. To address this, this study integrates the Pressure–State–Response (PSR) model with environmental criminology theories (Routine Activity Theory (RAT) and Crime Pattern Theory (CPT)) to couple macro social causal chains with micro behavioral–spatial mechanisms. Using data from the digital urban management system of Shenzhen’s Guangming District in 2019, four fire risk event types are examined: electric bike charging violations (EB), unauthorized power wiring (PW), water heater misuse (WH), and aging gas pipelines (GP). Spatial error models explain 82–89% of the variance across fire risk event types, and spatial 5-fold cross-validation shows minimal performance decline (ΔR² = 0.03–0.08), confirming robust prediction without overfitting. Key findings include: (1) elderly proportion is significantly positively associated with WH and PW (coefficients = 2.64 and 3.06, p < 0.01); (2) restaurant density has a consistently positive association with all four risk types (coefficients = 0.24–0.60, p < 0.01); (3) functional diversity and connectivity exhibit dual patterns, showing negative associations with more visible, easily detectable violations (PW, GP) but positive relationships with relatively concealed behaviors (EB); (4) reported safety deficiencies display strong positive associations with all fire risk event types and can therefore serve as an effective early-warning indicator for broader fire risk. These results support risk-specific, equity-oriented prevention strategies that prioritize vulnerable groups and high-risk environments. The validated PSR–RAT/CPT framework provides a novel theoretical basis for targeted fire risk governance and advances safe, resilient, inclusive cities aligned with Sustainable Development Goal 11. Full article

Nature-based solutions (NBS) are increasingly promoted in environmental management to address water, climate, biodiversity, and pollution challenges while delivering social and economic co-benefits. Yet decision-makers still face uncertainty about what works where, for whom, and how reliably over time. This narrative review synthesizes cross-cutting, peer-reviewed evidence on three decision-critical domains: NBS effectiveness for key environmental management objectives; co-benefits, trade-offs, and equity (including distributional risks across groups and places); and monitoring and evaluation (M&E). This review is not a systematic review, not a semi-systematic review with a fixed, protocol-driven study inventory, and not a meta-analysis; “comprehensiveness” refers to breadth of themes and management objectives addressed, not to exhaustive capture of all published sources. A distinguishing contribution is an intervention–pathway–endpoint typology oriented to measurement and M&E: it links broad NBS categories to dominant biophysical mechanisms and to concrete indicator families. Unlike criteria-first verification frameworks, this typology is organized around measurement logic (what to monitor, and how endpoints chain from processes to management decisions). It complements criteria- and process-oriented NbS quality frameworks (e.g., the IUCN Global Standard’s criteria and indicators for verification, design, and scaling) by foregrounding an explicit indicator logic chain for appraisal, monitoring, and cross-project comparability. The review assesses effectiveness for water quality, flood and flow regulation, heat mitigation, biodiversity, and carbon/climate mitigation; consolidates social, economic, and ecological co-benefits; reviews recurring M&E weaknesses; proposes a pragmatic minimum indicator set and feasible evaluation designs; and outlines an implementation-oriented NBS environmental management cycle. The aim is to strengthen transparent, climate-aware, evidence-based, and equity-aware environmental management. Full article

Transboundary river basins (TRBs) sustain billions of livelihoods, yet they face enduring systemic challenges of cooperative water governance. Although collaborative governance models consistently yield acceptable outcomes, adversarial dynamics and zero-sum approaches continue to dominate transboundary water management. This systematic review synthesizes the peer-reviewed literature (2000–2026) to evaluate how four major governance dimensions—and the cross-cutting integration of the water–energy–food (WEF) nexus—shape the effectiveness of water diplomacy in international basins. Socio-economic analysis reveals that benefit-sharing arrangements grounded in joint investment outperform zero-sum volumetric allocation, though implementation remains constrained by institutional fragmentation and governance lock-in. Power relations analysis demonstrates that material, institutional, knowledge-based, and narrative-framing asymmetries systematically define the range of achievable agreements and the reliability of cooperative commitments, with case analysis from the Nile, Mekong, Tigris–Euphrates, and Central Asian basins showing that comparable hydrological conditions yield divergent diplomatic outcomes depending on how power is distributed. Stakeholder engagement findings indicate that formal participatory mechanisms frequently produce symbolic rather than substantive inclusion, particularly where structural imbalances limit procedural access. Gender analysis provides that women’s inclusion improves agricultural productivity, water-use efficiency, and adaptive capacity—functioning as a governance variable with measurable system-performance effects rather than solely an equity objective. The WEF nexus operates as the integrative mechanism binding these dimensions, reframing diplomacy from volumetric allocation toward adaptive benefit arrangements that coordinate interdependent services across sectors. This review concludes that effective transboundary governance emerges from the concurrent integration of socio-economic benefit-sharing, power-responsive institutions, meaningful stakeholder participation, gender equity, and nexus-based coordination in global TRBs. Full article

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component analysis with Ward’s hierarchical clustering (86.58% variance explained, cophenetic correlation = 0.951), and qualitative synthesis. The results reveal exponential growth from 4 publications (2018) to 167 (2025) with geographic concentration in China (30.2%), the USA (9.7%), and India (8.0%). Collaboration networks exhibit pronounced fragmentation (density = 0.04, modularity = 0.78) with minimal North–South partnerships (12%). Critically, keyword analysis identifies five thematic clusters dominated by machine learning methodologies, whereas governance and equity dimensions appear fewer than eight times, revealing a fundamental gap wherein technical optimization proceeds without the institutional frameworks necessary for equitable water access. Multivariate analysis suggests that technological infrastructure capacity is a stronger correlate of research output than geographic water stress, based on the observed geographic distribution of high-output nations rather than direct operationalization of scarcity indicators. The qualitative synthesis revealed that 68% of the studies remained pilot-scale studies, 82% were concentrated in developed nations, and 66% cited data quality as the primary constraint. The bibliometric patterns suggest a pronounced orientation toward computational approaches, alongside paradoxical AI infrastructure water consumption that may partially offset conservation benefits. (Note: 2025 figures reflect early-access articles retrieved before the November 2024 search date and should be interpreted as partial-year estimates.) Achieving sustainable water management requires a reorientation emphasizing measurement infrastructure in data-poor contexts, North–South partnerships, and the integration of socioinstitutional dimensions as constitutive elements within technical development frameworks. Full article

At the current stage, water resource shortages and significant regional disparities in resource distribution severely restrict China’s food security. Existing research primarily focuses on resource use efficiency, while lacking a systematic framework to distinguish between equality and equity in the coupled distribution of irrigation water, grain production, and nitrogen pollution across major river basins. The core objective of this study is to utilize the Concentration Index (CI) to construct a unified equity assessment framework, quantify the evolution of equality and equity in irrigation water use, grain production, and nitrogen loss to surface water in different river basins in China from 1992 to 2017, and determine the key influencing factors. For positive production resources, a distribution that benefits low-income groups is equity, while for pollution burdens, this distribution pattern is inequity. The results show that water shortages in Northern China have intensified, and higher income groups have obtained excessive benefits. The distribution of grain production has shifted from favoring higher income groups to favoring low-income groups, with the Concentration Index changing from 0.214 to −0.052, indicating an enhancement in equity. Irrigation water use has shown a certain degree of improvement, with the CI dropping from 0.023 to 0.017. However, nitrogen loss to surface water has exacerbated environmental inequality, with the CI dropping from 0.10 to 0.03, indicating that pollution burdens have shifted to low-income groups. Changes in equity across the country are driven by a small number of high-intensity grain production areas, and the key influencing factors include food security policies, urbanization, population size, and nitrogen fertilizer application. An asymmetric coupling relationship exists between water resource shortages and equity, and the regional economic foundation determines the formation of synergy or trade-offs. The findings underscore the necessity of transitioning from efficiency-focused to equity-focused agricultural governance in China. Targeted policies should include cross-basin ecological compensation mechanisms, differentiated technology promotion strategies, and integrated water–food-pollution management systems to balance food security, environmental protection, and social justice. Full article

Ecological resettlement (ER), or conservation-led displacement, is widely implemented to safeguard biodiversity but often produces complex socio-ecological outcomes. This study assessed the environmental justice (both social and ecological) impacts of ER in Nepal’s Terai Arc Landscape (TAL) using an enhanced (including social, ecological, and environmental aspects) environmental justice (EJ) framework. Data were collected from 240 households across all resettled villages within the Chitwan and Parsa National Parks (NPs) of Nepal through household interviews, key informant interviews, focus groups, and field observations, supplemented by policy reviews, reports, and unpublished documents. Household demographics indicated an average family size of 5.5, gender parity (664 females, 658 males), and diverse caste/ethnic composition (ethnic: 146 households; higher caste: 64; lower caste: 6). Wealth distribution and literacy were uneven, with disparities in land ownership, assets, and social positions. Social and ecological justice outcomes were analysed using chi-square and McNemar tests. We observed a significant difference (p < 0.05) in substantive justice (food, shelter, clothing, and security) attributes before and after the resettlements. Similarly, significant improvements post-resettlement were observed in procedural and recognition justice: participation in decision-making increased from 43% to 62% (χ² = 12.34, p < 0.05). However, recognition of Indigenous knowledge and FPIC rights remained low, with 93% of households reporting inadequate acknowledgment (χ² = 198.5, p < 0.05). Distributive justice indicators, including access to compensation and forest resources, showed mixed outcomes, with 52% reporting fair compensation and 48% citing inequities (p < 0.05). Ecological outcomes also shifted significantly: forest cover decreased in 65% of surveyed areas post-resettlement, while grassland extent increased in 28% (χ² = 27.4, p < 0.05). Water source accessibility declined for 48% of households (χ² = 21.6, p < 0.05), and bushfire incidence decreased by 15% (χ² = 9.8, p < 0.05). Composite scoring revealed strong linkages between social justice deficits and ecological downturn in the resettled areas, suggesting that inadequate participation, recognition, inequitable compensation, and ecological degradation shift the issues from parks to the outside and exacerbate environmental vulnerability. These findings demonstrate that ER can achieve partial ecological objectives inside the parks but often perpetuates social inequities and ecological downturn in the resettled areas, undermining the long-term sustainability of the socio-ecological landscape. The study highlights the critical need to integrate social justice, participatory governance, and ecological monitoring into resettlement planning. Future policies should be grounded in the understanding that conservation effectiveness and social equity are mutually reinforcing, and that ignoring justice dimensions risks undermining both biodiversity outcomes and human wellbeing. Full article

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m³, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification. Full article

Water distribution pipe systems (WDPSs) are critical for delivering irrigation water in an irrigation district. Ensuring the WDPS operates with good operational performance has become a key focus for irrigation water management. Assessing the operational performance of WDPS from both water delivery capacity and pressure changes is compatible with the system, but such an approach has rare been proposed systematically. This paper establishes an integrated framework to assess the performance of WDPS by combining a performance indicator system with a simulation model. The performance indicator system encompasses Adequacy, Efficiency, Equity, and Pressure Reliability, capturing both water delivery capacity and pressure changes. The simulation model is established for providing required data for calculating the performance indicators. Applied to a case study, the framework demonstrates its applicability. Under the specified conditions of the case study, the overall assessment results of WDPS are Adequacy (0.99), Efficiency (0.993), Equity (0.0484), and Pressure Reliability (0.82). Meanwhile, the assessment results at different outlet nodes of the WDPS indicate that there may be insufficient pressure and excessive water supply at some outlet nodes of the WDPS. Further interpretation of the results indicates that the proposed method has the potential to be beneficial for irrigation water management, as it enables strategic assessment of the future operational performance of water distribution pipe systems (WDPSs) and provides effective management guidance for irrigation district managers. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

Urban ecosystems are increasingly shaped by multiple environmental stressors, which may threaten both biodiversity and human well-being. We summarised the current knowledge on the ecological and social consequences of seven major urban pressures: air pollution, freshwater degradation, biological invasions, noise pollution, habitat fragmentation, soil pollution and climate crisis. Air and soil pollution, largely driven by traffic and industrial activities, compromises vegetation functions, reduces ecosystem services, and affects human health. Urban freshwater systems face contamination from stormwater runoff, wastewater, and microplastics, leading to biodiversity loss, altered ecosystem processes, and reduced water availability. Biological invasions, facilitated by human activities and habitat disturbances, reshape ecological communities, outcompete native species, and impose socio-economic costs, while management requires integrated monitoring and citizen engagement. Noise pollution disrupts animal communication, alters species distributions, and poses significant risks to human physical and mental health. Simultaneously, habitat fragmentation and loss reduce ecological connectivity, impair pollination and dispersal processes, and heighten extinction risks for both plants and animals. Collectively, these stressors interact synergistically, amplifying ecological degradation and exacerbating health and social inequalities in urban populations. The cumulative impacts highlight the need for systemic and adaptive approaches to urban planning that integrate biodiversity conservation, public health, and social equity. Nature-based solutions, ecological restoration, technological innovation, and participatory governance emerge as promising strategies to enhance urban resilience. Furthermore, fostering citizen science initiatives can strengthen monitoring capacity and create community ownership of sustainable urban environments. Addressing the combined pressures of urban environmental stressors is thus pivotal for building cities that are ecologically robust, socially inclusive, and capable of coping with the challenges of the climate crisis and global urbanization. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

Urban Heat Islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks for socially vulnerable populations. While the role of urban environmental features in shaping UHI patterns is well recognized, their differential impacts on diverse social groups remain underexplored—limiting the development of equitable, context-sensitive mitigation strategies. To address this challenge, we employ an interpretable ensemble machine learning framework to quantify how vegetation, water proximity, and built form influence UHI exposure across social strata and simulate the outcomes of alternative urban interventions. Drawing on data from 1660Dissemination Areas in Vancouver, we model UHI across seasonal and diurnal contexts, integrating environmental variables with socio-demographic indicators to evaluate both thermal and equity outcomes. Our ensemble AutoML framework demonstrates strong predictive accuracy across these contexts (R² up to 0.79), providing reliable estimates of UHI dynamics. Results reveal that increasing vegetation cover consistently delivers the strongest cooling benefits (up to 2.95 °C) while advancing social equity, though fairness improvements become consistent only when vegetation intensity exceeds 1.3 times the baseline level. Water-related features yield additional cooling of approximately 1.15–1.5 °C, whereas built-form interventions yield trade-offs between cooling efficacy and fairness. Notably, modest reductions in building coverage or road density can meaningfully enhance distributional justice with limited thermal compromise. These findings underscore the importance of tailoring mitigation strategies not only for climatic impact but also for social equity. Our study offers a scalable analytical approach for designing just and effective urban climate adaptations, advancing both environmental sustainability and inclusive urban resilience in the face of intensifying heat risks. Full article