Search Results (247)

As a potential contributor to urban sustainability, the commercialization of Urban Air Mobility depends on social license rather than mere technical feasibility. To address the limitations of the Technology Acceptance Model (TAM) in high-risk contexts, this study integrates trust, perceived risk, personal innovativeness, and governance expectation to construct an extended acceptance model tailored to China’s policy-driven institutional environment. Based on 567 valid samples analyzed via Structural Equation Modeling (SEM) and Multi-Group Analysis (MGA), the results reveal three core mechanisms. First, perceived risk positively enhances perceived usefulness (β = 0.765, p < 0.001) via risk-induced cognitive reframing, indicating that the public rationalizes threats by amplifying the technology’s functional value. Second, trust exhibits a strong compensatory effect on perceived ease of use (β = 0.885, p < 0.001) and directly drives behavioral intention (β = 0.549, p < 0.001), serving as a heuristic to reduce perceived complexity in risky decisions. Third, governance expectation directly drives behavioral intention (β = 0.225, p = 0.004) and attitude (β = 0.201, p = 0.007), confirming the primacy of institutional trust in China. The model explains 50.5% of the variance in behavioral intention (R² = 0.505) and 48.6% in attitude (R² = 0.486). Notably, the direct effects of perceived ease of use on intention were non-significant, redefining TAM boundaries where safety supersedes efficiency. Finally, gender moderates the covariance between innovativeness and governance expectation (female β = 0.765 vs. male β = 0.720, C.R. = 3.196, p = 0.001), revealing higher institutional dependency among females. These findings elucidate the risk–trust institution nexus in UAM, offering empirical evidence for differentiated sustainable transport policies and marketing strategies. Full article

Water-intensive industries in the Mediterranean face supply stress and decarbonisation pressure simultaneously. We develop an operations-research decision-support framework that treats the firm as one node of a small industrial resource cluster and prices the cost and carbon-equivalent emissions of five alternative supply trains—municipal water, river water, groundwater, rainwater harvesting and brewery wastewater reuse—within a multi-objective Linear Program. Each train carries engineering-grounded expenditures, energy intensities and grid emissions, and a weighted-sum scalarisation is solved daily for 365 days under three managerial scenarios. On a Cretan microbrewery whose 2022 demand of 5250 m³ is met from the municipal network, the balanced and cost-focused scenarios coincide on a single optimum that cuts the Levelised Cost of Water by 25.3% and emissions by 40.7%, while the eco-friendly scenario yields a 19.3% cost and 51.7% emissions reduction. LP duality, shadow prices and an extended sensitivity programme (diversification, capacity, grid factor, discount rate, RO recovery and demand profile) turn the optimisation into a decision-support package: optimal daily allocations, shadow-price signals on capacity and demand, and robustness diagnostics for capital planning, dispatch and risk management. Results are site-specific, but the framework and its diagnostics transfer in structure to clusters sharing the same convex-polytope source geometry; transposition to energy cooperatives is future work. Full article

The decarbonization of essential water supply infrastructure is a critical challenge for water-stressed and geographically constrained regions, particularly islands where both water and electricity systems are highly dependent on external or fossil-based resources. In Northern Cyprus, approximately 70% of domestic water demand is met through imported water via pipeline, while electricity generation relies predominantly on fuel oil, resulting in high greenhouse gas emissions and environmental burden. This study evaluates an integrated renewable energy-based supply system using a medium-scale concentrating solar power (CSP) plant with parabolic trough collectors coupled to thermal desalination. The proposed configuration is assessed as an alternative energy-driven infrastructure option for reducing dependence on imported water and fossil-based electricity. System performance was evaluated by estimating electricity and freshwater production under local climatic conditions, demonstrating that the proposed configuration can meet both the associated electrical energy requirements and domestic water demand in the selected region. A cradle-to-gate life cycle assessment (LCA) was conducted to quantify the environmental impacts of the integrated system and support sustainability-oriented decision-making. The LCA results identify residual fossil-based electricity, phosphoric acid consumption, and brine discharge as the main environmental hotspots. Overall, the findings show that CSP-driven desalination can provide a viable and more sustainable option for integrated energy and water supply in water-scarce coastal regions with high solar potential, highlighting its relevance for renewable energy integration, water-energy nexus planning, and resource-efficient infrastructure development. Full article

This study aims to systematically evaluate the balancing mechanism between road performance, carbon emissions, and economic cost when selecting asphalt materials for severe cold regions, filling the gap in multi-criteria decision-making for composite chemical modifications. To address alternating temperatures, heavy traffic, and modified asphalt transport difficulties, this study presents a novel evaluation framework focusing on the performance–environmental–cost nexus of a desulfurized rubber powder composite SBS-modified asphalt mixture, which provides a clear technological breakthrough for high-ratio scrap tire recycling in seasonal frost zones. Two reference mixtures serve as comparisons: a conventional rubber powder composite SBS (styrene–butadiene–styrene triblock)-modified asphalt mixture (CR-SBS) and an SBS-modified asphalt mixture (SBS). A comparative experiment was conducted between the two materials and the SBS-modified asphalt mixture (ACR-SBS) compounded with desulfurized rubber powder. High-temperature stability was tested by the rutting test, low-temperature crack resistance by the beam bending test, and water stability by the immersion Marshall and freeze–thaw splitting tests. Life cycle carbon emissions and economic costs were quantified from raw material acquisition to construction. The results show that desulfurized rubber powder composite with ACR-SBS delivers the most superior overall road performance. However, it also generates the highest life cycle carbon footprint. Its total carbon emission reaches 162,800 kgCO₂eq, which is 13.7% (19,600 kgCO₂eq) higher than SBS (143,200 kgCO₂eq) and 7.7% (11,600 kgCO₂eq) higher than CR-SBS (151,200 kgCO₂eq). The total cost of ACR-SBS is 391,000 CNY, which is 1.5% (6000 CNY) higher than SBS (385,000 CNY) and 1.3% (5000 CNY) lower than CR-SBS (396,000 CNY). These findings provide a basis for the selection of high-performance, low-carbon, and economical composite-modified asphalt in severe cold regions. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Purpose: This study investigates the multi-dimensional nature of corporate risk-taking by examining how governance mechanisms exert differing pressures on accounting-based stability versus market-perceived volatility in the Saudi context, as the biggest emerging market in the Middle East. Moreover, the research uses accounting conservatism as a critical moderating variable and the sample is partitioned into high-conservative and low-conservative groups. Design/methodology/approach: The research analyzed data from 69 non-financial listed firms from 2017 to 2024 using four statistical models. Corporate risk-taking values have been captured from both accounting-based and market-based perspectives. Moreover, managerial, institutional, and concentration ownership have been used to capture ownership structure. However, board size, independence, and CEO power have been used to capture board structure. Findings: The research findings reported three main results: (1) Ownership structures have an asymmetric impact on accounting-based corporate risk-taking, as managerial and institutional ownership take a U-shaped curve, but ownership concentration has a positive impact. Moreover, from market-based corporate risk-taking, managerial and institutional ownership have a negative impact, but ownership concentration has a positive impact. (2) Board structures have an asymmetric impact on accounting-based corporate risk-taking, as managerial and institutional ownership have a negative impact, but ownership concentration has an inverted U-shaped impact. Moreover, from market-based corporate risk-taking, managerial and institutional ownership have no significant impact, but ownership concentration has a negative impact. (3) Accounting conservatism can change the nexus between ownership structure, board structure, and corporate risk behavior. Research limitations/implications: The research has many implications. For policymakers, the results discovered the role of ownership and board structures in shaping corporate risk-taking behavior in the Saudi context. Moreover, we have provided evidence-based guidance for governance reforms and firm-level decision-making. Moreover, the results can be incorporated by investors and creditors into their risk assessment frameworks, improving portfolio allocation and credit evaluation. Originality/value: The research captured corporate risk-taking behavior in the Saudi context from two perspectives at the same time. Likewise, it provides new empirical evidence that accounting conservatism can have a role in risky behavior. Full article

Climate change continues to threaten global food security. Climate-smart agriculture (CSA) offers a solution to addressing this challenge in urban agriculture (UA). This paper addresses a gap in the empirical literature on decision-making about the adoption of CSA practices by examining the determinants of CSA adoption among small-scale urban crop (SSUC) farmers in eThekwini (ETH) Municipality, South Africa. Grounded in a utility theory framework, the paper draws on 412 respondents (Cochran-estimated) from a multi-stage sample design across four wards, providing reasonable coverage of SSUC farmers in ETH Municipality. While the sample size is statistically representative of SSUC farmers in ETH Municipality, it is a single metropolitan case rather than universal. The results show strong complementarities among these CSA practices, for example, between OM and CD (r ≈ 0.70, p < 0.001) and M and CD (r ≈ 0.61, p < 0.001). The multivariate probit (MVP) model predicts that the socio-economic and institutional factors age, gender, marital and employment status, education, credit access, extension contact, land tenure, and location (distance from home to farm plots) (p < 0.05) were significant determinants of adopting CSA practices by SSUC farmers. The findings contribute to the global literature on the UA–CSA nexus, demonstrating that socio-economic and institutional factors shape the adoption of bundled CSA practices. While the findings underscore the need for integrated, custom, and UA context-specific policy and extension interventions to strengthen urban food system resilience, UA farmers, practitioners, researchers, and policymakers should apply these insights elsewhere with caution. Full article

This paper examines how the growth and decline of agglomerations and peripheries in metropolitan regions can be understood in the context of Advanced Producer Service (APS) firm decisions, regional conditions and institutional policies. Focusing on Germany, it responds to divergent quantitative findings for Munich and Dresden and to outcome-oriented studies documenting spatial patterns, leaving underlying mechanisms under-specified. This study adopts an embedded qualitative case study design, analysing Munich and Dresden as contrasting metropolitan subunits within a shared national framework. Drawing on documentation, archival records and expert interviews with economic development and regional governance actors, it uses explanation building and template analysis to link empirical material to an analytical framework integrating firm, location and public authority perspectives. The results identify four recurrent configurations in the firm–location–policy nexus: reinforcing agglomeration, emerging limits to agglomeration, balancing peripheral growth and reinforced peripheral decline. These configurations show how the same metropolitan region can simultaneously exhibit core growth, constraints on further concentration, selective peripheral upgrading and cumulative peripheral disadvantage. Conceptually, this paper develops a mechanism-based account of APS-driven metropolitan development and proposes refined propositions that help reinterpret outcome-based studies on Munich and Dresden. More broadly, the configurations offer an analytical lens for analysing APS location dynamics and metropolitan governance challenges in other polycentric and federal contexts. Full article

The article is devoted to developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects, considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches, which consider ecological, resource, and economic aspects of the project implementation effectiveness, is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms, in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, in dry years (p = 70%), the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing ecological risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine. Full article

The present study seeks to synthesize the scientific evidence from the last decade (2015–2025) regarding the transition from inequality in technological access toward social stratification mediated by automated decision-making systems. Following PRISMA 2020 guidelines and the SPIDER model, a corpus of 74 high-impact records from Scopus, Web of Science, ProQuest, and PsycINFO was examined. The results reveal an exponential growth in scientific production since 2018, marking a shift from infrastructure-based inequality toward a systemic stratification mediated by algorithmic opacity. Three critical sectors of exclusion are categorized: the socio-health nexus, the labor market, and the educational ecosystem. Methodologically, quantitative algorithmic auditing predominates (58%), although mixed sociotechnical approaches have increased by 25% since 2021 to capture experiences of intersectional vulnerability. The study concludes that AI acts as an active agent of social reproduction, necessitating a transition toward “Algorithmic Justice” and “Human-Centric Governance.” Finally, a “Reinstating AI” framework is proposed to democratize technological development and mitigate systemic biases, offering a roadmap for researchers and policymakers in the pursuit of technological sovereignty. Full article

The accelerating global transition toward sustainable energy necessitates a more profound comprehension of the behavioral and contextual determinants influencing household adoption intentions, particularly in policy-driven economies. This research analyses household renewable energy adoption in Saudi Arabia employing a Theory of Planned Behavior-informed, attitude-centric framework. Awareness, environmental concern, and perceptions of governmental policy are evaluated as contextual precursors of attitude, while perceived financial cost is conceptualized as a moderating constraint on the attitude–intention nexus. Data were amassed through a cross-sectional survey of 300 household decision-makers in Saudi Arabia and analyzed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The outcomes reveal that perceptions of governmental policy, environmental concern, and awareness are positively correlated with attitudes toward renewable energy, with policy perception demonstrating the most robust relationship. Attitude, in turn, is strongly correlated with adoption intention. Furthermore, perceived financial cost negatively moderates the attitude–intention relationship, indicating that financial apprehensions diminish the transference of favorable evaluations into adoption intentions. By situating the Theory of Planned Behavior within a policy-oriented energy framework, this study underscores the pivotal roles of institutional perception and affordability in shaping household renewable energy intentions through attitudinal mechanisms. The findings furnish practical insights for policymakers by accentuating the significance of policy credibility, financial accessibility, and targeted communication strategies aligned with Saudi Vision 2030’s sustainability objectives. Full article

In recent years, the industrial decarbonization in the cement sector has introduced secondary environmental impact due to an increase in power and water demand. Deploying carbon capture, utilization, and distributed storage requires an uninterrupted supply of power and water to achieve net-zero targets. However, the traditional static optimization algorithms seem insufficient in addressing the high-frequency and dynamic renewable networks. To overcome these issues, this work develops a dynamic water-energy-carbon trade-off optimization model for industrial decarbonization, with the deployment of Carbon Capture, Utilization, and Storage system in the cement sector within a United Kingdom industrial cluster. The key objective is to quantify and control the secondary burden that low-carbon interventions can impose on electricity systems and local water resources. Firstly, the Water-Energy-Carbon problem is treated as a tri-lemma, which is formulated as a continuous Markov Decision Process. Then the optimization problem is solved via a Soft Actor-Critic Deep Reinforcement Learning algorithm under coupled and resource-constrained abstraction inputs. This work further introduces the Water-Carbon Mitigation Penalty Index as a diagnostic metric for measuring the marginal increase in water burden associated with carbon mitigation. The results show that unmanaged distributed carbon-mitigation pathways increase local hydrological stress by 2.15–5.17% relative to baseline operating conditions. Although the proposed algorithm successfully reduces the nexus cost by up to 70.5% and achieves 13.83% carbon reduction by shifting from freshwater abstraction to reclaimed municipal wastewater and by coordinating operation with low-carbon hydropower availability. These results show that dynamic AI-based scheduling can support net-zero transitions while reducing pressure on regional hydro-ecological systems. Full article

Sustainable economic development involves reducing heavy reliance on fossil energy resources and their associated environmental impacts. The complexity of this task increases significantly in oil-producing countries, given the hydrocarbons’ role in economic growth, GDP, and exports. In such cases, decoupling economic growth, energy consumption and emissions should be achieved gradually to ensure a smooth transition, which will require the development of a reliable approach. This study aims to develop a strategy to identify potential pathways for economic growth and energy decoupling in the oil industry. Given the characteristics of the transition process, the feasibility of long-term solutions remains uncertain, and special measures are needed to enhance the reliability of decisions. An approach that combines assessing the economic–environment–emissions nexus, developing fuzzy transition scenarios, and applying multi-criteria and probabilistic decision-making methods has been designed to identify reliable pathways for the energy transition and sustainable development in oil-dependent countries. This allows us to create reliable and compromise scenarios that consider social, technological, environmental, economic and political factors. This study employed Azerbaijan as a case study. Analysis of key indicators revealed strong correlations between country GDP, energy production, and emissions. The MCDM calculations of the obtained feasible scenarios show the optimality of the scenario assuming a decrease in oil production while maintaining natural gas as usual, significantly increasing solar, and moderately increasing wind and hydro energy production. Decisions reflect global economic and energy-sector trends, expert opinions, and the current realities of Azerbaijan’s economy. Full article

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

This article develops a rigorous and reproducible systematic review of the integration of artificial intelligence (AI) in financial education during the period 2020–2025, structured in accordance with -5.3-PRISMA and explicitly oriented toward detecting narrative and perception. The search was conducted in three complementary databases (Scopus, ScienceDirect, and Taylor & Francis), using search strings equivalent to those of the platform and a selection workflow that begins with 388 records and culminates in 50 included studies, prompting a narrative synthesis given the methodological heterogeneity. From a methodological contribution perspective, the study combines bibliometric mapping with text mining and an NLP process that triangulates sentiment using lexicon-based approaches (VADER, TextBlob) and a multilingual transformer model (XLM-RoBERTa), producing continuous indicators (sentiment index) and reproducible research artifacts. The results position AI as an integrative nexus linking financial literacy, decision-making, sustainability, and language technologies (including ChatGPT-5.3.), highlighting its potential for personalization, virtual tutoring, and immediate gains in comprehension and motivation; however, evidence of sustained behavioral change remains nascent. Critical gaps remain, such as a shortage of longitudinal/controlled studies, a lack of standardized metrics, limited transparency and validation of models, and constraints in terms of geographic and cultural diversity, while privacy, fairness, and algorithmic bias emerge as structural conditions for responsible adoption. Full article