The West-East Electricity Transmission Project plays a vital role in China's low-carbon energy transition by facilitating the transfer of electricity from the resource-abundant West to the energy-deficient East. However, the environmental implications of electricity consumption in eastern China remain insufficiently understood from a dual water–carbon perspective, especially along interregional supply chains. To address these challenges, a Carbon-Water Nexus Input-Output Analysis (CWNIA) model is first developed in this study to investigate the key sectors and pathways of water-carbon nexus (WCN) flows embodied in WEET and support the formulation of effective electricity consumption policies in eastern China. Results show that local electricity consumption dominates environmental pressures in eastern provinces. In 2017, local electricity consumption in eastern provinces contributed to 91.94% of the region's total embodied carbon emissions (1518.92 Mt) and 83.68% of its total embodied water consumption (629.27 million m3). At the provincial level, Shandong ranks highest in embodied carbon emissions at 268.22 Mt. (17.66% of the eastern region's total), while Anhui leads in embodied water consumption with 189.86 million m3, (30.17% of the regional total). Sectoral analysis further identifies other service sectors, construction, and other manufacturing industries as key nodes for embodied WCN inflows. The pathway “HEB_EWPT→HEB_OSE” represents the largest WCN transfer route, involving 30.56 Mt. CO₂ and 12.76 million m3 of water flows. Scenario analysis indicates that simultaneously adjusting the demand of high-WCN sectors and key transmission sectors can substantially alleviate environmental pressures associated with electricity consumption in eastern China. The proposed CWNIA model provides policy insights for improving electricity consumption strategies and supporting regional sustainable development.

This study investigates the spatiotemporal variationsSpatiotemporal Variations of the Normalized Difference Vegetation Index (NDVI)Normalized Difference Vegetation Index (NDVI) in the Jiziwan region of the Yellow River Basin from 2003 to 2023 and explores the impacts of water and thermal conditionsWater and thermal conditions on vegetation dynamics. Results show that NDVINormalized Difference Vegetation Index (NDVI) values are highest in summer and lowest in winter, with a general increasing trend from north to south across the region. Over the study period, NDVINormalized Difference Vegetation Index (NDVI) exhibited an overall upward trend, particularly in the southern and central areas, indicating improved vegetation conditions. Correlation analysis revealed that groundwater storage is a consistent limiting factor for vegetation growth throughout the year, while the effects of precipitation and soil moisture vary seasonally. Thermal factors, including temperature and net solar radiation, also exhibit complex relationships with NDVINormalized Difference Vegetation Index (NDVI), with temperature generally promoting vegetation growth in spring and summer. These findings highlight the importance of considering both water and thermal conditions in vegetation management and ecological restoration efforts in the region.

This study advances an integrated Bayesian support vector machine-based two-step factorial analysis (abbreviated as BSVM-TFA) method for revealing the influences of human activities on water demand. The developed method can capture complex nonlinear relationships between human activities and water demand by calibrating SVM hyperparameters through Bayesian optimization, which helps prevent overfitting. BSVM-TFA can also identify the individual and interactive effects of multiple factors on water demand and screen key influencing factors. The BSVM-TFA is then applied to Central Asia, and the results show that by 2050, water demand would range from 75.66 x 109 m3 to 113.23 x 109 m3 under different scenarios, indicating an uncertainty of about 33.18 % driven by human activities. The key factors influencing water demand in Central Asia are GDP and agricultural irrigation efficiency (AIE), with a total contribution of 47.98 %; the water demand would be reduced by 16.42 x 109 m3 with low-growth GDP and increasing AIE.

Energy, water, and carbon are tightly interconnected, and balancing these linkages is critical for Canada's low-carbon transition and sustainable development. A major challenge lies in capturing the socio-economic and environmental (SEE) impacts of green alternatives while addressing regional disparities, interregional interactions, and uncertainties within the energy-water-carbon nexus system. Accordingly, this study develops a Canadian Energy-Water-Carbon Nexus (CEWCN) planning model to support strategic decision-making for achieving net-zero emissions by 2050. Built on a mixed-integer interval chance-constrained programming framework, the model addresses multidimensional uncertainties and regional heterogeneity under multi-risk conditions. Scenario analysis and Sustainable Development Goals assessments are further integrated to examine how climate policies, technological innovation, socio-economic development, and interregional cooperation affect system sustainability. Results indicate that by 2050, renewable energy will supply over 90% of Canada's electricity, primarily from hydropower, wind, and solar. Natural gas with carbon capture and storage (CCS) and small modular reactors are expected to replace conventional natural gas and nuclear. Water consumption will remain highest in Quebec, British Columbia, and Ontario due to hydropower, biomass, and nuclear generation, though total withdrawals will decline by [16–20]% compared to 2030. National emissions are expected to fall sharply, reaching net-zero by 2050 through large-scale renewable deployment and negative emission technologies such as bioenergy with CCS. Moreover, regional carbon cooperation can redistribute abatement efforts, easing mitigation burdens in high-emission provinces and lowering system costs. Overall, the findings offer strategic insights for shaping resilient, low-carbon transition pathways and managing multidimensional risks in the CEWCN system.

Hydrological modeling is subject to significant uncertainties arising from complex parameter interactions and dynamic processes. Parameter calibration involving interacting variables remains a persistent challenge, often leading to low sampling efficiency, particularly in high-dimensional spaces with strong parameter dependence. To address this issue, this study proposes a composite copula Bayesian-inferred hydrological model (CBIH). Specifically, a hybrid copula-based adaptive Metropolis algorithm (HCopAM) is developed by combining a copula-based Metropolis-Hastings scheme (CopMH) with adaptive Metropolis (AM), improving sampling efficiency while reducing sample concentration. The method is evaluated using two benchmark distributions, one synthetic case, and two real-world hydrological simulations. Results indicat that, compared with AM and differential evolution adaptive Metropolis (DREAM), the copula-based algorithms improve the acceptance rate, increase the number of effective samples, and reduce the autocorrelation of markov chain due to sampling based on joint probability density. Relative to CopMH, the HCopAM further improves sampling efficiency and reduces sample concentration in high-dimensional settings. In hydrological applications, HCopAM accurately identifies parameter values and significantly enhances simulation performance. For example, during HYMOD validation in the Ruihe Watershed, the Nash-Sutcliffe efficiency increases from 0.264 (AM) to 0.804 (HCopAM), while the root mean square error decreases from 6.726 to 3.410 (m3/s). These improvements highlight the importance of capturing parameter interdependence for reliable uncertainty quantification. The Sensitivity analysis indicates that the hybrid proportion strongly influences performance of HCopAM, reflecting a trade-off between sampling efficiency and posterior accuracy; a CopMH proportion greater than 0.5 is not recommended. Overall, this study provides an effective extension of copula-based Bayesian inference and offers a scalable approach for posterior estimation in complex hydrological models.

Groundwater storage changes in regions with scarce observations are increasingly being inferred from satellite gravimetry, but different institutional datasets can lead to inconsistent conclusions. Focusing on the U-shaped Meander Region of the Yellow River Basin, we examine the dependence of groundwater storage trends on the choice of satellite gravimetry datasets and implement a Bayesian three-cornered hat fusion strategy to obtain more robust groundwater storage anomaly (GWSA) estimates. This fusion strategy estimates institution-specific noise levels relative to a potential truth and uses them for Bayesian weighted fusion of the three GRACE-derived TWSA products. The results show that the five GWSA datasets give different conclusions about groundwater changes when trends are segmented around 2007. The GWSA dataset estimated using the Bayesian-TCH framework reduces regional mean uncertainty by about 50-80 % and raises the signal-to-noise ratio above 20 dB. The Bayesian-TCH time series shows a near steady declining trend before 2007 and, afterward, weakened seasonal coherence and a stronger role of human activities. This pattern is more consistent with independent understanding that the region is shifting from mainly climate driven variability to increasing impacts of sustained groundwater pumping and coal mining dewatering, whereas some GWSA datasets show stable recovery before the breakpoint and others still display climate dominated variability after the breakpoint.

To address the lack of research on the multidimensional characteristics and combined risks of humid heatwave-heavy precipitation compound events, this study proposes a copula-based compound humid heatwave and heavy precipitation joint risk analysis method (CHHRA) following the identification of such events. This method systematically assesses the multidimensional combined risks of humid heatwave-heavy precipitation compound events in East China under both 'simultaneous occurrence (AND)' and 'at least one occurrence (OR)' scenarios. The main conclusions are as follows: (1) Under return periods of 20, 50 and 100 years, both the average intensity and duration of compound events increase significantly with global warming. Under high-emission scenarios (SSP370 and SSP585), the increase is approximately double the historical level. (2) Spatially, compound events with higher average intensity and longer duration will concentrate in eastern coastal and southern regions. Meanwhile, under both 'AND' and 'OR' scenarios, extreme compound events with shorter return periods will predominantly occur in western inland and northern areas. (3) The combined risk under the SSP585 scenario is substantially higher than under SSP126; the recurrence intervals for 50-year 'AND' and 'OR' events are reduced to within 30 and 5 years, respectively. These findings provide data support for the optimisation of extreme weather monitoring and early warning systems, and offer a scientific basis for policy formulation by emergency management authorities.

Previous studies have examined the effects of various policies (e.g., taxes and subsidies) on water resource management. However, limited research has systematically analyzed the socio-economic and environmental (SEE) effects of water supply constraints in water-scarce regions from an industry-wide perspective. In this study, a water-constrained computable general equilibrium (WCGE) modelComputable general equilibrium model is developed by integrating water supply limitations as a key production factor into an extended CGE framework. The model is applied in Inner MongoliaInner Mongolia (China) to evaluate the SEE effects of different levels of water supply reduction across industries. It is found that water supply constraints negatively impact the economy and reshapes industrial trade structures. Agriculture (AGR) and primary manufacturing (PRM) increase their value-added by investing more in labor and capital, whereas energy extraction (ENC) and services (SRV) experience declines in value-added due to their high dependence on water input. The water productivity of ENC, advanced manufacturing (ADM), and SRV improves significantly. AGR shows the largest reduction in physical water consumption but only a modest increase in water productivity (e.g., a reduction of 6.85 × 10⁹ m3 in physical water consumption and an increase of 12.31 CNY/m3 in water productivity under a 50

Hydrological models are essential for simulating watershed processes, yet uncertainties in parameters often lead to discrepancies between simulations and observations. This study introduced an integrated framework coupling the Soil and Water Assessment Tool (SWATSoil and Water Assessment Tool (SWAT)) with Variational Bayesian Monte CarloVariational Bayesian Monte Carlo (VBMC) and Factorial AnalysisFactorial analysis (FA) to address parameter uncertainty in hydrological modeling. The framework not only obtains the parameter posterior distributions at a low computational cost, but also quantifies the independence and interaction of the parameter factors. An application of the framework is made to the Wuding River basin in the Jiziwan region of the Yellow River to analyze the effects of parameter uncertainty. Results indicate: (i) SWATSoil and Water Assessment Tool (SWAT) model can accurately simulate runoff processes in the study area, with NSE values of 0.67 (calibration) and 0.63 (validation); (ii) posterior distributions showed cn2 with low uncertainty versus latq_co with higher uncertainty; (iii) factorial analysisFactorial analysis identified cn2 as the primary driver of increased mean and peak runoff, moderated by interactions with awc and ovn, respectively. This framework enhances computational efficiency and parameter identifiability, offering a rigorous approach to uncertainty characterization and improved hydrological predictions in complex scenarios.

The impact of compound climate extremes on transportation infrastructure is destructive and long-lasting, yet it remains unclear whether these impacts are equitably distributed across countries with different income levels. This study assesses global inequalities in exposure to compound precipitation extremes (CPEs), defined as the concurrent occurrence of short-duration, high-intensity 1-day precipitation (RX1D) and prolonged 5-day precipitation (RX5D), in the late 21st century under multiple climate scenarios. Under SSP585, approximately two-thirds of global transportation infrastructure is projected to experience increased exposure, affecting about 12.6 million km, with at least a 25% reduction in joint return periods, indicating more frequent extremes. Most exposed assets are concentrated in the Global North (10.9 vs. 1.3 million km in the Global South), with the largest absolute exposure in high-income regions (4.9 vs. 0.6 million km in lower-income regions). In contrast, adaptation needs show a clear inverse income gradient. Low-income countries require the highest safety factors (mean 1.31 for 50-year events), followed by lower-middle (1.20), upper-middle (1.17), and high-income (1.15) countries. This pattern remains robust across scenarios and infrastructure types (except motorways), revealing systematic inequality and underscoring the need for income-sensitive adaptation strategies in future infrastructure planning.

Valorizing water treatment residual (WTR) aligns with circular-economy objectives by transforming water treatment sludge waste into value-added products. For the first time, we evaluated the performance and mechanisms of aluminum-based WTR as a cost-effective and environmentally benign filtration medium for MP removal. Our column experiments removed up to 97.47% of 10-µm carboxylated polypropylene microplastics (PP-COOH MPs) from synthetic water. MP removal increased with ionic strength and the presence of mono/multivalent cations (Na+, Ca2+), primarily through charge neutralization, double-layer compression, and pore straining. WTR-based filtration also effectively removed MPs from the primary influent (95.6%) and final effluent (86.9%) of a full-scale wastewater treatment plant, as well as from the influent of a full-scale water treatment plant (97.1%). In addition, dissolved Al leached from WTR enhanced electrostatic attraction and MP aggregation, thereby promoting MP deposition on WTR surfaces. Overall, these findings demonstrate that WTR can be repurposed as a valuable material to control MP pollution, offering attractive opportunities to turn this waste into a beneficial resource. Given its potential to remove MP and other pollutants, WTR can be used as a filter medium to polish WWTP effluent, improve stormwater quality, or serve as a functional layer in landfill sites to mitigate leachate pollution.

Comprehensively identifying key nodes in virtual waterVirtual water network and predicting variations in virtual waterVirtual water characteristics remains a significant challenge. In this study, an input–output analysisInput-output analysis CNN-LSTM (IOACL) model is developed to identify the sources of virtual waterVirtual water consumption within the industrial chain and predict future water usage. IOACL is applied to Ningxia, one of the most water-scarce provinces in China. The major findings are as follows: (i) agriculture, food tobacco and construction have high virtual waterVirtual water consumption, collectively accounting for 70

Sustainable agricultural development increasingly emphasizes the synergistic management of water-energy-carbon-ecosystem services. However, existing optimization frameworks for the water-energy-food-carbon nexus lack integration of ecosystem services and simultaneous consideration of algorithm performance, strategy sustainability, and robustness. To fill this gap, this study develops a decision-making framework for sustainable grain production based on the water-energy-carbon-ecosystem services nexus, incorporating a nondominated sorting genetic algorithm, hypervolume indicator, hybrid multi‑criteria decision‑making, and Monte Carlo simulation. Application in China’s major grain-producing areas demonstrates that systematic cropping structure optimization serves as an effective lever to break the low-sustainability lock-in and achieve a multi-win outcome. By implementing structural adjustments to moderately reduce total sown area, decreasing maize cultivation and increasing the potato proportion, these strategies maintain system coordination and stable grain output while delivering substantial sustainability gains: cutting grey and blue water footprints by 192–1,444 and 878–8,738 million m3, reducing GHG emissions by 1.42–15.14 Mt CO2-eq, saving 901–4,507 million MJ of energy, increasing green water occupancy by 1.74 %-2.03 % and alleviating water stress by 7.2 %-26.9 %. Moreover, the optimization strategies exhibit high robustness across 10,000 Monte Carlo runs (coefficient of variation < 0.08). The proposed integrated framework circumvents the limitations of traditional multi-objective methods that may exclude potential optimal solutions or yield suboptimal outcomes. It provides a transferable, data-driven decision-support tool for regions worldwide facing complex resource and ecological constraints, contributing to sustainable agricultural transformation and the advancement of multiple SDGs.

MXene-based Fenton-like systems have been extensively studied for degrading recalcitrant organic pollutants. While environmental conditions such as pH and coexisting anions significantly affect catalytic performance, comprehensive and systematic evaluations of multiple influencing factors remain limited. Herein, we performed a multi-factor multi-impact meta-analysis comprising 82 articles and 758 data points to elucidate the performance of various MXene-based catalysts under diverse environmental conditions. The results show that interfering ions, humic acid, strong acidity, and alkaline conditions significantly suppress degradation efficiency. Strong acidic conditions and carbonate ions exert the most pronounced inhibition in PMS-based systems, whereas alkaline conditions mainly inhibit Fenton-based systems. Typically, the unique surface charges of certain organic pollutants can considerably diminish the impact of environmental conditions. Additionally, bimetallic or carbon-nitrogen-doped MXene catalysts demonstrate robust resistance to a variety of environmental conditions. Consequently, this research provides valuable insights into the catalytic performance of MXene-based catalysts for the degradation of organic pollutants via Fenton-like reactions under various environmental conditions.

Clean energy transition increases management challenges of energy transition metals (ETMs). To support collaborative governance of carbon mitigation, energy transition, and material management, this study develops a factorial non-deterministic carbon-energy-metal nexus optimization model that explicitly quantifies nexus interactions, trade-offs and synergies, and multidimensional uncertainties. Through integrating non-deterministic optimization, dynamic material flow analysis, and factorial design, the model analyzes spatiotemporal metal dynamics under various transition scenarios and examines interactive effects of mixed-level material management strategies on carbon mitigation and resource savings. It is applied to Canada's electricity and passenger transport sectors over 2031-2050. By 2050, Canada's electricity capacity would reach 1.4-1.5 times its 2023 level, driven by expansion of wind, solar and small modular reactors. Vehicle stocks would increase by 1.3-1.7 times, alongside near-complete electrification. In-use 35 ETM stocks would rise tenfold, reaching 6.9-9.1 Mt under the net-zero scenario, with metal inflows consistently exceeding outflows (with a ratio > 2). Significant provincial disparities in metal stock changes indicate higher material pressures in Saskatchewan, Alberta, and New Brunswick. Material strategy analysis reveals that extending electric vehicle lifetime could avoid 47.8-55.8 Mt of rebound emissions, whereas reducing material intensity could lower per capita demand for cobalt, dysprosium, nickel, and indium by 27.9-56.3%. Combining lower material intensity, higher recycling, and longer lifetimes could achieve additional emission reductions of 68.3-83.6 Mt, while lowering per capita metal demand by nearly 50%. These findings enhance understanding of carbon-energy-metal nexus complexities and support decision-making toward a net-zero future.

Metal-organic framework-derived carbon materials (MDCMs) hold broad prospects for the removal of organic contaminants (OCs) from water through adsorption and advanced oxidation processes, owing to their high specific surface area, tunable pore structure and excellent electrical conductivity. However, the stability of these materials during long-term operation and under complex aquatic environments has become a critical bottleneck that restricts their translation from laboratory research to practical engineering applications. This review focuses on the stability of MDCMs in removing various OCs, especially emerging organic contaminants (EOCs), and systematically elaborates the definitions and evaluation methods of structural stability, chemical stability and performance stability. Based on the research advances in adsorption and various advanced oxidation systems, the influence mechanisms of intrinsic material structure, active component design and environmental interference factors on stability are analyzed in depth. Furthermore, the strategies for enhancing the stability of MDCMs are comprehensively summarized, and the underlying mechanisms governing stability are systematically analyzed. Finally, the key challenges and future development directions in this field are critically discussed. Overall, this review aims to provide theoretical guidance for the rational design of high-stability MDCMs for practical applications and promote their effective transition from laboratory research to water pollution control engineering.

Accurate quantification of moisture transport between the ocean and the land is essential for understanding the global water and energy cycles. Existing methods based on flux divergence or trajectory analysis are computationally demanding and not well suited to irregular coastlines. Here we develop a zonal-meridional projection (ZMP) method that directly measures water-moisture fluxes across global coastal interfaces. Vertically integrated moisture fluxes derived from ERA5 reanalysis (0.25 degrees, 1979-2020) are projected onto coastal-normal directions and multiplied by a signed land-side boundary length derived from sub-segmented grid edges. This line-integral formulation preserves the physical consistency of the flux direction and automatically distinguishes inflow and outflow through the signs of wind and coastline orientation. It also improves computational efficiency by avoiding conditional checks of wind direction or coastal type. The results reveal coherent ocean-to-land inflow corridors along monsoonal and intertropical regions and a statistically significant upward trend in total inflow since 1979, consistent with an intensifying global hydrological cycle. The proposed ZMP method provides a geometrically precise, scalable and computationally efficient approach for quantifying land-ocean moisture exchange in both reanalysis and climate-model applications.

Tropical cyclones (TCs) are critical components of the atmospheric system, with their attributes (intensity, trajectory, and frequency) primarily influenced by large-scale climate modes/patterns. However, the relationships between global TC attributes and combinations of different phases of multiple climate modes, especially within the latest CMIP6 HighResMIP models, remain underexplored. Additionally, the underestimation of wind speeds in these climate models complicates the accurate representation of TC intensity. Hence, in this study, a composite clustering and discriminant analysis (CDA) method is developed to robustly reclassify global TC intensity using the latest CMIP6 models and project future changes in various TC categories. Following a global assessment to identify key regions with robust signals, the main analysis focuses on the Northern Hemisphere (NH), particularly the North Atlantic (NA), where statistically significant changes are observed. Results indicate a consistent northward shift of high-intensity TCs in the NH during both historical and future periods, with the NA recording more northward-moving, intensified landfalling TCs. The study further explores how individual climate modes, and their interactions influenced observed TC attribute changes in the NA region within the historical period. Beyond the known modulation effects of individual El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Western Hemisphere Warm Pool (WHWP) on Atlantic TCs, their complex interactions and nonlinear relationships significantly increase the frequency and northward shift of high-intensity TCs in the NA region. However, these nonlinear statistical relationships between climate modes and TCs are reflected in only a few CMIP6 HighResMIP models, though they exhibit similar trends of climate mode-TC interactions as observed; this highlights the need for future model improvements focusing on climate mode interactions. These findings offer new insights into the multifaceted impacts of climate change on TCs and suggest potential directions for enhancing seasonal TC predictions.

Virtual water trade planning magnifies the scope of regional water resources utilization, and becomes a burgeoning strategic choice for water management. In this study, a bi-level stochastic model coupling virtual and physical water optimization (BS-VPWO) is designed for quantifying the effects of virtual water trade on water trading as well as optimizing decisions for conjunctive management of physical-virtual water systems. BS-VPWO integrates water resources assessment with hydrological simulation, physical water trading modeling coupled with virtual water trade, and interval stochastic bi-level programming (ISBP) technique. Virtual water export and import processes as well as water right initial allocation and reallocation are elaborated in BS-VPWO framework. A case study of Dagu River Basin with water shortage, East China, is conducted for application of BS-VPWO. The impacts of virtual water trade on water system and spatial heterogeneity of basin's water shortage are quantified through mechanism analysis. The optimal virtual water trade scheme is also identified. Results reveal that coupling virtual and physical water optimization can increase system efficiency by [0.35, 0.451% and benefits by [5.01, 5.031%, meanwhile decrease basin's water shortage volume. This study discloses the driving forces of virtual water trade in physical water right trading and water resources management systems. It also examines virtual water trade decisions to alleviate water scarcity and enhance utilization efficiency in virtual waterexporting basins.

Assessing flood mitigation strategies is crucial on a global scale, where Large-scale Hydraulic Projects (LHPs) are essential in enhancing socio-economic resilience and mitigating flood impacts. However, the failure to recognize the spatial distribution patterns of these direct and indirect benefits can lead to a substantial underestimation of the positive effects that LHPs can have on various socio-economic activities and regions susceptible to flooding. This study develops a Spatial Footprint Impact Assessment framework (SFIA), which is applied to the Three Gorges Project (TGP), evaluating the spatial heterogeneity of indirect flood-retention benefits induced by the TGP across 31 provinces in China. Our findings demonstrate that while Hubei and Hunan gain the largest direct reductions in GDP and welfare losses, more distant provinces benefit indirectly through stabilized supply chains supported by the TGP. Quantitatively, the TGP’s flood-retention capacity helped reduce China’s flood-induced GDP losses by 28–37 billion Chinese Yuan. By introducing the concept of “hydraulic project’s footprints,” this study demonstrates that overlooking such indirect benefits substantially underestimates the value of LHPs. Our findings can offer detailed, region-specific insights into formulating flood management strategies and provide a transferable framework for assessing other large-scale hydraulic projects worldwide.