Water-Energy Nexus Tool: A complete energy assessment model for wastewater treatment plants

Navigating the water-energy nexus in bioenergy production: A resilience framework for sustainable biofuel supply networks

Perspectives of electrochemical and photocatalytic technologies for the water-energy nexus potential of water splitting of brines

Protonic ceramic electrochemical cells (PCECs) have emerged as a versatile platform for enabling integrated energy solutions that go beyond conventional fuel cell or electrolyzer operation. This work presents a unified perspective on PCECs as a foundation for synergistic energy systems—systems that co-generate power, produce chemicals, and enable efficient energy storage by coupling electricity, heat, and fuel pathways. Drawing from multiple recent studies, we demonstrate how dual-function protonic ceramic fuel cells can directly utilize methane-rich resources, such as flare gas, for simultaneous electricity generation and production of value-added chemicals like hydrogen and aromatics. Complementary systems operating in electrolysis mode also enable reversible methane synthesis for energy storage and grid balancing.These technologies are modeled and evaluated within techno-economic and water-energy nexus frameworks to assess performance, sustainability, and integration potential. Results highlight that while individual operating modes offer unique advantages, their combined implementation significantly amplifies system value—demonstrating the concept of synergy: the whole is greater than the sum of its parts. By co-optimizing these electrochemical processes in a single material and system platform, PCECs can serve as a key enabler for distributed, flexible, and sustainable energy networks.

A multi-parametric approach for bi-level optimization within the energy–water nexus

Dynamic interactions in energy-water nexus: risk-based framework for balancing electricity expansion and water resource sustainability

Hierarchical energy management for heterogeneous multi-energy microgrid community: Integrating hydrogen into the water-energy nexus

Towards urban energy independence: Case study of residential hidden energy-water nexus

Integrated pathways for sustainable energy-water nexus management in resource-dependent cities: A scenario-based assessment of Tangshan, China

Climate-resilient development: A sustainability analysis of regional programs through the water-energy nexus model and impacts of climate change

Optimizing water management: Identifying strategies to enhance irrigation efficiency under drought conditions.

This study presents a transformative advancement in water-energy nexus management by developing a hydrostatic pressure wheel (HPW) system that simultaneously optimizes water level regulation and hydropower generation in open-channel irrigation systems (OCISs)—a dual functionality not achieved by existing technologies. While conventional waterwheels focus solely on energy production, our HPW design leverages hydrostatic pressure dominance to provide precise hydraulic control while extracting renewable energy, addressing two critical needs in irrigation infrastructure with a single integrated solution. The research introduces key innovations beyond current literature: (1) a variable-speed HPW operation strategy that dynamically adjusts to flow conditions, achieving superior performance (45% efficiency, 3.5 kW power output) while maintaining water level deviations below 2.7%—a 40–50% improvement in control accuracy compared to fixed-speed systems and (2) the first coupled numerical framework integrating OCIS hydraulics with HPW dynamics and multi-objective optimization (NSGA-II) to resolve the inherent trade-off between energy maximization and hydraulic stability. The results revealed that variable-speed operation considerably outperforms conventional fixed-speed designs in both energy yield and regulation precision with respectively up to 25% and 60% improvement. These advancements establish HPWs as a new class of smart hydraulic structures that convert traditionally wasteful energy dissipation into renewable power generation while enhancing irrigation management—a capability absent in all prior waterwheel applications documented in literature.

Solar-thermal distillation offers immense and hitherto untapped potential for energy-efficient, sustainable freshwater production. However, critical limitations in material and system design continue to hinder water-evaporation rate (Rw) and specific water productivity (SWP), posing significant roadblocks for scalable and stable solar desalination. An ultrathin, porosity-tuned high internal phase emulsion polymer (polyHIPE, PH) scaffold is integrated with nanostructured hard-carbon florets (NCF) as a black-body absorber to create the thinnest interfacial evaporator, delivering best-in-class solar-thermal energy conversion (η-STC = 84%), rapid Rw (6.5 kg m-2 h-1), and efficient SWP of 18 L m-2 day-1 from seawater. The Janus-faced NCF@PH confines solar-thermal energy at the interface, which also promotes extensively convex water menisci, to deliver high Rw. The continuously interconnected 3D capillary channels within NCF@PH ensure continuous water transport, while their hydrophobic surfaces prevent salt deposition for robust long-term seawater distillation with high salt rejection (>90%). Furthermore, a system-level prototype termed SunSpring (SS) works to decouple the light-admitting, water-evaporation, and water-condensation steps to produce environmental protection agency (EPA)-standard freshwater (<10ppm salinity) from highly saline seawater (35 000ppm) for over ≈225h of real-time continuous usage. It is also estimated that SS has the lowest energy (<3 W L-1) and CO2 (≈3 g L-1) footprints, representing a paradigm shift in the water-energy nexus.

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

Urban water utilities in rapidly developing regions face growing challenges in ensuring continuous supply. Intermittent public water supply leads to unreliable and inequitable access, compelling households to adopt energy-intensive coping strategies. This creates a nexus between water and energy demand at the household level. Few econometric analyses of household water demand have explicitly addressed this demand-side nexus in developing regions. Using survey data from the city of Pimpri-Chinchwad, India, where intermittent water supply is prevalent, we analyze household expenditures related to water access and estimate a piped water demand function with a Discrete-Continuous Choice model. We find that electricity expenditures for accessing water exceed water bills for approximately one-third of households. Including these costs in affordability calculations reveals hidden financial burdens, particularly for middle-income households. Water and electricity prices, income, and household size significantly influence water demand, with an income elasticity of 0.177 and water price elasticities ranging from 0 to −0.876. The cross-price elasticity of −0.097 indicates weak complementarity between electricity and piped water, suggesting electricity price changes do affect water use but are insufficient to drive substantial behavioral shifts. Targeted price increases in high-consumption blocks are more effective at curbing overuse, while simultaneous increases in water and electricity prices may heighten household vulnerability. These findings highlight the need for integrated, nexus-aware demand management strategies, particularly in regions with intermittent supply.

Water-Energy nexus and GHG emissions of cropping systems under varying field management practices in arid India

Saline alkaline water splitting enabled by a Co3 + -rich iron hydroxide electrocatalyst: Advancing the water–energy nexus

A literature review of the food–energy–water nexus trade-offs and synergies research at the household level

Water management in arid regions, such as Basra, Iraq, faces escalating challenges due to water scarcity and increasing energy demand. This study investigates the integration of machine learning with renewable energy technologies to optimize water and energy efficiency in such environments. A multi-scenario approach was employed, combining advanced water treatment technologies, energy recovery systems, and smart grid integration to assess their impact on sustainability. This study evaluated a comprehensive techno-economic analysis of the integration of machine learning models and renewable energy technologies, marking a significant step toward more sustainable and efficient water-energy nexus management in arid climates. The solar-powered UV disinfection system reduced energy consumption by 30%, while membrane filtration techniques minimized water loss by 20%. The adoption of pressure recovery turbines improved energy efficiency by 25%, resulting in significant energy savings of 800 kWh annually and a reduction of 400 kgCO2 emissions. Smart grid systems enhanced operational efficiency, reducing energy wastage by 15% and improving water distribution by 25%. Machine learning models, including the M5 model tree and recurrent neural networks (RNN), were applied to predict and optimize system performance, highlighting their ability to handle complex, non-linear relationships between energy and water variables. The results proposed a scalable framework for integrating machine learning-driven renewable solutions into water-energy systems in water-stressed regions, addressing global challenges in water management, supporting climate adaptation strategies, and contributing to the United Nations Sustainable Development Goals (SDGs).

Exploring urban resilience through the food–water–energy nexus during the COVID-19 pandemic: Insights from the Great Pause in Tokyo