Search Results (201)

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

This study investigated the integration of advanced Atmospheric Water Generators (AWGs) within the design process of building energy systems, focusing on the water–energy nexus in the context of a real-life hospital building. It is based on a simulation approach, recognised as a viable means to analyse and enhance AWG potentialities. However, the current state of research does not address the issue of AWG integration within building plant systems. This study contributes to fill such a research gap by building upon an authors’ previous work and proposing an enhanced methodology. The methodology describes how to incorporate a multipurpose AWG system into the energy simulation environment of DesignBuilder (DB), version 7.0.0116, through its coupling with AWGSim, version 1.20d, a simulation tool specifically developed for atmospheric water generators. The chosen case study is a wing of the Mondino Hospital in Pavia, Italy, selected for its complex geometry and HVAC requirements. By integrating AWG outputs—covering water production, heating, and cooling—into DB, this study compared two configurations: the existing HVAC system and an enhanced version that includes the AWG as plant support. The simulation results demonstrated a 16.3% reduction in primary energy consumption (from 231.3 MWh to 193.6 MWh), with the elimination of methane consumption and additional benefits in water production (257 m³). This water can be employed for photovoltaic panel cleaning, further reducing the primary energy consumption to 101.9 MWh (55.9% less than the existing plant), and for human consumption or other technical needs. Moreover, this study highlights the potential of using AWG technology to supply purified water, which can be a pivotal solution for hospitals located in areas affected by water crises. This research contributes to the atmospheric water field by addressing the important issue of simulating AWG systems within building energy design tools, enabling informed decisions regarding water–energy integration at the project stage and supporting a more resilient and sustainable approach to building infrastructure. Full article

Water Supply and Distribution Networks (WSDNs) offer underexplored potential for energy recovery. While many studies confirm their technical feasibility, few assess the long-term operational compatibility and economic viability of such solutions. This study evaluates the energy recovery potential of the Brussels Capital Region’s WSDN using four years (2019–2022) of operational data. Rather than focusing on available technologies, the analysis examines whether the real behavior of the network supports sustainable energy extraction. The approach includes network topology identification, theoretical power modeling, and detailed flow and pressure analysis. The Brussels system, composed of a Water Supply Network (WSN) and a Water Distribution Network (WDN), reveals strong disparities: the WSN offers localized opportunities for energy recovery, while the WDN presents significant operational constraints that limit economic viability. Our findings suggest that day-ahead electricity markets provide more suitable valorization pathways than flexibility markets. Most importantly, the study highlights the necessity of long-term behavioral analysis to avoid misleading conclusions based on short-term data and to support informed investment decisions in the urban water–energy nexus. Full article

The water–energy nexus (WEN) is of great significance due to the strong interdependence between the energy and water sectors. Nevertheless, water leakage in water distribution networks (WDNs), which is often ignored in existing WEN operation models, causes notable water and energy losses. In this research, a cooperative operation model for WEN considering WDN water leakage is put forward. A hybrid stochastic–information gap decision theory (IGDT) method was tailored in this study to properly manage the probabilistic uncertainties associated with renewable generation, electrical and water demand in the WEN, and water leakage with limited information to enhance the robustness of the operation strategies of the WEN under complex operational conditions. The proposed model and method were validated on a modified IEEE 33-bus system integrated with a 15-node commercial WDN. The co-optimization model reduced the operational cost by 23.01% compared to the independent operation model. When considering water leakage, the joint optimization resolved the water supply shortage issue caused by ignoring leakage and reduced the water purchase volume by 94.44 cubic meters through coordinated optimization. These quantitative results strongly demonstrate the effectiveness of the proposed framework. Full article

Automated external defibrillator resources (AEDRs) are the crux of out-of-hospital cardiac arrest (OHCA) responses, enhancing safe and sustainable urban environments. However, existing studies failed to consider the nexus between built environment (BE) features and AEDRs. Can explainable machine-learning (ML) methods reveal the BE-AEDR nexus? This study applied an Optuna-based extreme gradient boosting (OP_XGBoost) decision tree model with SHapely Additive exPlanations (SHAP) and partial dependence plots (PDPs) aiming to scrutinize the spatial effects, relative importance, and non-linear impact of BE features on AEDR intensity across grid and block urban patterns in Tianjin Downtown, China. The results indicated, that (1) marginally, the AEDR intensity was most influenced by the service coverage (SC) at grid scale and nearby public service facility density (NPSF_D) at block scale, while synergistically, it was shaped by comprehensive accessibility and land-use interactions with the prioritized block pattern; (2) block-level granularity and (3) non-linear interdependencies between BE features and AEDR intensity existed as game-changers. The findings suggested an effective and generalizable approach to capture the complex interplay of the BE-AEDR and boost the AED deployment by setting health at the heart of the urban development framework. Full article

When education in STEM, social science, and the humanities are disconnected from each other and from place, it is inauthentic and nonresponsive to the lived experiences of people and communities. In rural spaces, the Food–Energy–Water (FEW) Nexus, a framework for problem solving and decision-making around these central resources, is salient because of the concentration of FEW resource production and extraction present. Storying the FEW Nexus is an interdisciplinary pedagogical framework that is theoretically rooted in a critical pedagogy of place and socio-ecological systems. Storying the FEW Nexus brings together these two related but distinct frameworks, calling attention to the need for relevant, place-based, and rural-focused narratives within STEM instruction. Developed for K-12 learners in rural places, Storying the FEW Nexus positions STEM knowledge and skills as resources that, alongside local narratives, are vital to the sustainability and viability of communities with unique and intertwined environmental justice histories and current realities. The FEW Nexus is leveraged to support rural learners in developing sustainable solutions to local socio-ecological systems issues. In this conceptual paper, we review the literature base supporting this integrated approach, describe the framework within the context of these aims, and make suggestions for researchers and practitioners. Full article

Climate change disrupts global food systems by affecting water, energy, ecosystems, and agricultural productivity. Building climate resilience demands integrated approaches that recognize interdependencies among water, energy, food, and environmental (WEF-E) systems. This review synthesizes current research on how the WEF-E nexus can guide climate adaptation strategies. It highlights interdisciplinary solutions—such as solar-powered irrigation, agrivoltaics, agroforestry, conservation agriculture, and nature-based water management—that enhance resource efficiency, stabilize yields, and reduce environmental degradation. Effective implementation requires governance innovation, stakeholder participation, and coherent cross-sector policies. The paper also outlines research priorities, including the development of resilience metrics, modeling tools, and inclusive decision-making mechanisms. Emphasizing both adaptation and mitigation, the WEF-E nexus offers a transformative lens for sustainable, equitable, and climate-resilient food systems. As climate pressures intensify, advancing this integrated framework presents both an urgent necessity and a strategic opportunity to align food security with environmental stewardship. Full article

Urban agriculture is key to sustainable city development, particularly through public engagement with the Water–Energy–Land–Food–People (WELFP) Nexus. This study examines the effectiveness of serious games in enhancing WELFP understanding and promoting pro-environmental and pro-social behaviors. A game-based learning model was developed using the Stimulus–Organism–Response (SOR) and Easy–Attractive–Social–Timely (EAST) frameworks, along with the Revised New Ecological Paradigm (NEP) Scale. The model simulates real-world urban agriculture challenges to foster participatory decision-making. A survey of 200 urban agriculture practitioners, analyzed via structural equation modeling (SmartPLS 4.0), found that perceived timeliness (PT) and perceived usefulness (PU) significantly influenced both the perceived sustainable livelihood value (PT: p = 0.000; PU: p = 0.006) and users’ attitudes toward the game (PT: p = 0.000; PU: p = 0.038). While enjoyment positively affected attitude (p = 0.002), it negatively impacted perceived value (p = 0.002), revealing a trade-off between fun and practical relevance. Perceived ease of use improved perceived value (p = 0.000) but did not affect attitude, suggesting emotional engagement matters more. Both attitude and perceived value strongly predicted users’ intention to engage with the game. Post-game reflections highlighted the need for cross-sector collaboration, strategic resource use, access to real-time data, and responsive crisis management. Participants also stressed the importance of public awareness, civic responsibility, and volunteerism in advancing community-driven sustainable agriculture. These findings highlight the need to balance engagement and educational depth in game-based learning for sustainability. Full article

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges. Full article

The study examines the nexus between AI-driven technology, i.e., robo-advisors, and the behavioural intention of investors towards sustainable investment decisions considering government regulations and sustainable investment awareness as the moderating variables. A total of 372 responses were collected from across India through a structured questionnaire along identified variables from the TAM and UTAUT theories under the select constructs, i.e., trust, perceived risk, user-friendliness, perceived usefulness, and emotional arousal. This is with reference to the use of robo-advisors to unearth the extent to which they influence the behavioural intention and finally the sustainable investment decisions taking into account government regulations and sustainable investment awareness as the moderating variables. The results derived by using PLS-SEM reveal that all the five factors are having a significant impact on the behavioural intention for sustainable investment decisions of the investors. Further, both sustainable investment awareness and government regulations have been found to have a moderating impact on shaping the behavioural intention of the investors with respect to most of the variables. The results of the study come up with significant suggestions for the government, financial institutions, and the investors as well as the academicians, and therefore, have policy implications, managerial implications, and theoretical implications. The constructs and moderating variables considered here can further be used for studying the behavioural intentions. The robo-advisory service providers may emphasize developing the algo ensuring trust, usability, and friendly interface in a manner that tends to minimize the perceived risk and emotional arousal leading to the use of robo-advisors pushing the intention of the investors towards sustainable investment. Full article

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water–energy–food–ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in Türkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting Genetic Algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors. Full article

Understanding the socioecological nexus between urbanization and ecosystem health (EH) is crucial for formulating sustainable development policies. While prior research has focused on this topic, critical gaps persist in characterizing distributional polarization and decomposing inequality drivers within coupled human–environment systems—particularly in China’s Yellow River Basin (YRB), a strategic region undergoing concurrent ecological restoration and urbanization. The integration of the kernel density estimation and Theil index establishes a robust analytical framework to effectively overcome spatial heterogeneity limitations in regional disparity research. Therefore, this study combines the coupling coordination degree (CCD), nonparametric kernel density estimation, and Theil decomposition to examine the complex interactions between urbanization and the ecosystem health index (EHI) across 538 county-level units from the perspective of spatial heterogeneity. The key findings reveal the following: (1) Urbanization exhibited phased enhancement yet maintained elementary developmental stages overall, with a distinct spatial gradient descending from the eastern/central riparian counties to the western hinterlands. (2) The EHI showed a marginal upward trend, yet 80.29% of the counties persisted in the suboptimal ecological health categories (EHI-1 to EHI-3), with gains concentrated in high-vegetation mountainous areas (45.72%) versus declines in economically developed areas. (3) The CCD evolved from a mild imbalance (II-1) to low coordination (III-1) but with significant special differences—the midstream and downstream CCD improved markedly, while the upstream counties remained the weakest. (4) Intragroup disparities, particularly among the counties in the middle reaches, were the primary drivers of CCD disequilibrium across the YRB, contributing 87.9% to the overall inequality. In contrast, the downstream regions exhibited significant improvements in the coordination levels, accompanied by the emergence of distinct “multi-polarization” patterns. The findings provide refined and differentiated decision-making references for effectively narrowing the gap in coordinated development in the YRB. Full article

This study focused on exploring the influence of infrastructural development on foreign direct investment (FDI) in BRICS countries, including Brazil, Russia, India, China, and South Africa, using a fixed-effects approach. Secondary data ranged from 1991 to 2021. Existing theoretical and empirical literature on the subject (the infrastructural development-led FDI nexus) is quite mixed, which therefore makes it difficult for policy makers to make decisions. Internet penetration and fixed telephone subscriptions had a significant enhancing effect on FDI, whilst renewable energy infrastructure’s effect was found to be minimal and non-significant. In the fixed-effects model, the interaction term produced results showing that financial development enabled infrastructural development and significantly enhanced FDI inflows into BRICS countries. To improve FDI inflows, BRICS nations should implement policies with the aim of enhancing Internet penetration, fixed telephone subscriptions, and financial development. A threshold analysis of the infrastructural development levels that significantly improve FDI inflows is recommended to provide more clarity and specificity for policy making. Full article

Water, energy, food, and ecosystem (WEFE) components constitute fundamental dimensions contributing to human well-being, poverty alleviation, and sustainable development. Despite the prevalent specialization among WEFE professionals, there is a lack of multidisciplinary approaches in their work, with limited attention given to carbon footprint management. Against this backdrop, this study aims to explore the potential role of standardization and multi-criteria decision analysis (MCDA) in implementing the WEFE approach within the food sector. The research entails a comprehensive examination of the International Standard Organization (ISO) 22000 certifications in Balkan countries, coupled with an analysis of the ISO 14067 standard and its alignment with food safety requirements. Finally, this study proposes a novel MCDA framework for integrating food safety considerations with criteria, factors, and indicators aimed at addressing both food safety and carbon footprint management. A hierarchical structure composed of influential criteria and factors was used to rank activities in sustainable, preferably carbon-neutral food production. Group decision making was applied in the fuzzy domain using triangular numbers, and the influence of experts was determined based on their experience. Practical recommendations aimed at managing trade-offs between the requirements of two elaborated standards are provided, emphasizing key environmental, societal, and economic insights to identify critical indicators for addressing biases in food safety and carbon footprint management. Full article