Search Results (3,615)

This study examines Malaysian consumers’ perceptions toward purchasing electric vehicles (EVs) in the context of increasing global efforts to promote sustainable mobility and reduce carbon emissions. Despite government initiatives and incentives to encourage EV adoption in Malaysia, uptake remains relatively slow compared to more developed markets, highlighting the need to understand consumer attitudes and concerns. A qualitative research design was adopted using semi-structured interviews with five purposively selected participants who had awareness or experience related to vehicle ownership decisions. Thematic analysis was used to analyse the data in relation to financial considerations, charging infrastructure, social influence, and service reliability. The findings indicate that financial concerns, particularly high purchase costs and battery-related uncertainties, are the most significant barriers to EV adoption. Charging infrastructure limitations, including insufficient charging stations, long charging times, and lack of home charging access, further reduce consumer confidence. Social influence from peers, family, and online platforms plays a dual role in shaping both positive and negative perceptions, while service reliability concerns, especially regarding battery durability and after-sales support, affect trust in EV ownership. The study concludes that EV adoption in Malaysia is influenced by interconnected economic, technological, and social factors, suggesting that coordinated improvements in affordability, infrastructure, consumer awareness, and service support are essential to accelerate adoption. This study contributes novel qualitative evidence by revealing how financial, infrastructural, social, and service-related factors collectively shape Malaysian consumers’ EV purchase perceptions, extending previous survey-based research in the Malaysian context. Full article

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources. Full article

Climate risks drive dynamic adjustments to global climate policies, creating significant climate policy uncertainty (CPU). This uncertainty profoundly affects enterprises’ survival and sustainable development. A key question emerges: how do enterprises’ perception of CPU influence their resilience? To answer this, this study adopts a capability perspective and empirically examines the impact of climate policy uncertainty perception (CPUP) on enterprise resilience (RESI) and the underlying mechanism. Using panel data on Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2023, the study defines CPUP as the interaction between a news-based provincial CPU index and the frequency of climate risk words in annual report texts, and measures RESI with the entropy weight method across four dimensions (business volatility, long-term growth, short-term performance, and enterprise survival). Panel regression with fixed effects indicates that CPUP significantly enhances RESI. A one-standard-deviation increase in CPUP raises RESI by approximately 0.0019 index units, equivalent to about 2.2% of the standard deviation of RESI. This effect is more pronounced for enterprises in the eastern and central regions and in high-carbon industries. Mechanism tests confirm that CPUP boosts RESI by optimizing management capabilities and strengthening development capabilities, revealing a capability-driven path between CPUP and RESI. This study enriches the theoretical understanding of CPU’s economic consequences and RESI antecedents from a capability perspective. It also provides empirical references for enterprises to build resilience amid policy fluctuations and for policymakers to formulate regionally differentiated climate policies. Full article

With the increasing penetration of wind and photovoltaic generation, microgrids are playing an increasingly important role in promoting renewable energy accommodation, enhancing operational flexibility, and enabling low-carbon energy management. However, the strong uncertainty of renewable generation and load demand, together with the coupling effects of electricity, hydrogen, and carbon markets, poses significant challenges to the optimal planning and operation of microgrid energy storage systems. To address these issues, this paper proposes a bi-level optimal sizing framework for an electric–hydrogen hybrid energy storage system (EHH-ESS) in a microgrid under multi-market coupling. First, typical wind–solar–load scenarios are generated using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP), so as to capture the stochastic characteristics and temporal correlations of renewable generation and load demand. Then, a multi-market coupling index (MCI), integrating electricity price, hydrogen price, and carbon price signals, is constructed to characterize time-varying economic and low-carbon operating incentives and to guide coordinated dispatch decisions. On this basis, a bi-level multi-objective optimization model is established. The upper level determines the optimal capacities of battery storage, electrolyzers, fuel cells, and hydrogen tanks, while the lower level performs hourly coordinated operation of the microgrid under multi-market conditions. The model considers annual equivalent total cost, renewable energy curtailment rate, and carbon emissions as objective functions, and is solved using the NSGA-III algorithm. Compared with the no-storage benchmark, the proposed scheme improves the annual operating economics and renewable-energy accommodation under the studied market conditions. The proposed method significantly reduces annual operating cost and improves renewable energy accommodation. However, under the current carbon price and grid emission factor settings, the optimal economic solution increases carbon emissions relative to the baseline, indicating a trade-off between economic arbitrage and low-carbon operation. Full article

Amid growing global economic uncertainty, strengthening tourism economic resilience is critical to the economic sustainability of tourism destinations. Using panel data for 288 Chinese prefecture-level cities from 2010 to 2024, this study uses the staggered designation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to examine whether artificial intelligence (AI) development promoted by the pilot-zone initiative enhances tourism economic resilience. Results show that the pilot-zone initiative significantly enhances city-level tourism economic resilience, and this finding remains robust across a series of endogeneity and robustness checks. Mechanism analysis identifies data factor utilization, technological innovation, and industrial structure upgrading as three parallel channels. Moderation analysis shows that the resilience-enhancing effect of the pilot-zone initiative is stronger in cities with more developed digital infrastructure, higher levels of marketization, and greater human resources. Heterogeneity analysis reveals overall regional heterogeneity and a stronger effect in resource-based cities. These findings clarify the mechanisms and boundary conditions linking AI development promoted by the pilot-zone initiative to tourism economic resilience and provide implications for technology-enabled sustainable tourism development. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty. Full article

Modern power systems are increasingly exposed to uncertainties and face rising demands for operational resilience. To address this challenge, this paper investigates the optimal placement of thyristor-controlled series compensation (TCSC) devices within flexible AC transmission systems (FACTS). A comprehensive vulnerability evaluation index is developed by integrating network structure, load impact, and branch disconnection factors, enabling a holistic identification of vulnerable transmission links. Subsequently, a multi-objective TCSC optimization model is formulated to simultaneously minimize the system-wide comprehensive vulnerability index and the total investment cost. To solve this model, an improved multi-objective particle swarm optimization (MOPSO) algorithm is devised, incorporating chaotic initialization and adaptive inertia weight adjustment to enhance both global exploration and local exploitation capabilities. The proposed method is validated using the IEEE 39-bus and IEEE 118-bus test systems. The results demonstrate that the optimized placement significantly reduces system vulnerability, maintains a favorable economic balance and improves the system security margin. Furthermore, uncertainty tests involving load variations, line parameter perturbations, and wind power fluctuations, as well as malicious attacks, confirm the robustness of the proposed placement strategy. This work provides a practical and effective framework for resilience-oriented TCSC planning, contributing to mitigating cascading failure risks and enhancing power system security. Full article

Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems. Full article

Mothers of preterm infants (PT; <37 weeks gestational age) face economic barriers to mother’s own milk (MOM) provision, including lack of paid maternity leave and unpaid workload that may prevent sustained MOM provision. The objective of this study was to understand how mothers of PT infants navigate decisions about MOM provision and paid and unpaid work. Semi-structured interviews were conducted with mothers of PT infants between 5 days and 10 weeks postpartum. Interviews included questions about responsibilities in the home, pre-delivery work experience and plans to provide MOM, and postpartum work experience and MOM provision. Data were analyzed using reflexive thematic analysis, following Braun and Clarke’s method. This study included 18 mothers, who were predominantly non-White (79%) and covered by Medicaid (72%) with infants born at a median gestational age of 32 weeks. Three themes emerged: (1) unexpected prenatal events create postpartum job uncertainty; (2) breastfeeding intentions are often derailed after PT delivery; and (3) the unpredictable reality of having a PT infant shifts maternal priorities and obligations related to paid and unpaid workload. PT delivery creates emotional and financial stress and uncertainty for new mothers. Although federal and employment-based paid leave may alleviate some financial stress for mothers employed prior to delivery in the United States, mothers who leave the workforce before delivery will not benefit from these policies. Policies are needed to support all mothers of PT infants in facilitating long-duration MOM provision, independent of employment status. Full article

Photorespiration and drought-induced stomatal closure are two important physiological constraints that reduce carbon assimilation and productivity in C₃ forest trees under Mediterranean climatic conditions. Türkiye’s two dominant commercial pine species, Pinus brutia Ten. (Calabrian pine) and Pinus nigra J.F. Arnold subsp. pallasiana (Anatolian black pine), together cover approximately 8.15 million hectares. This study integrated published gas-exchange measurements, radiation-use efficiency estimates from MODIS, official forest inventory data, and dendrochronological growth records into a counterfactual accounting framework and propagated parameter uncertainty by Monte Carlo simulation (N = 40,000 draws). The two constraints jointly reduced weighted-mean net primary productivity (NPP) from a radiation-limited potential of 5.61 to an actual 3.46 Mg C ha⁻¹ yr⁻¹, a reduction of 37.9% (95% CI 32.2–43.4%). Decomposition shows that 47.7% of this loss is the obligate metabolic cost of C₃ carboxylation, which no silvicultural intervention can address, while 52.3%—20.1 of the 37.9 percentage points—is drought-attributable. Nationally, the deficit corresponds to 64.3 Mt CO₂ yr⁻¹ of forgone sequestration (47.8–81.2) and 34.4 Mm³ yr⁻¹ of forgone stemwood-volume equivalent (24.9–44.5), of which approximately 20.6 Mm³ would be merchantable, giving an annual economic deficit of USD 3.37 billion (2.40–6.48). Filtering the drought-attributable component for eligible area, recovery efficiency, additionality, leakage, and permanence yields approximately 1.0 Mt CO₂ yr⁻¹ of potentially issuable credits, fewer than two per cent of the headline figure. Eco-physiological suppression of this magnitude is currently invisible in national forest carbon accounting, and its recognition bears directly on dynamic baseline design and on the credibility of offsets generated from Mediterranean conifer forests. Full article

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns. Full article

This paper examines how the body of research and innovation on electric cooking for low- and middle-income countries has evolved to the extent that electric cooking can now be argued to have the potential to influence energy system performance. Methods: The paper synthesises recent evidence on electric cooking from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly argued to be acting as a system-strengthening source of demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption can improve utility and mini-grid viability and support further investment, and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which has great potential to improve load factors and align demand with generation. However, a persistent planning blind spot remains, with growth in electric cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward tangible system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should increasingly be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential. Full article

This study protocol presents a pre-specified framework for assessing whether Farm Machinery-as-a-Service (FMaaS) can support inclusive access to Agriculture 4.0 in fragmented agrarian systems. The aim of the protocol is to define the conceptual framework, research questions, hypotheses, target population, sampling strategy, data collection procedures and planned analytical approach for a future empirical study using Poland as a case of a fragmented, family-farm-based agricultural system in which many small and medium-sized farms face difficulties in keeping pace with Agriculture 4.0. The manuscript does not report empirical results or human-subject data. The planned study will use a sequential mixed-methods design in which each component has a specific role. Secondary data analysis will diagnose farm structural conditions, machinery-use patterns, productivity gaps and innovation gaps. Expert and stakeholder interviews will refine the typology of FMaaS arrangements and identify relevant service attributes. A farm survey and a discrete choice experiment will assess farmers’ willingness to use FMaaS, perceived transaction costs, trust, digital readiness and preferences for specific service characteristics. Farm-level economic modeling and Monte Carlo simulation will evaluate the viability of ownership-based and service-based modernization scenarios under uncertainty. Structural equation modeling will examine social-sustainability mechanisms, including adaptive capacity, perceived resilience, perceived inclusion in digital agriculture and the perceived ability to continue farming under technological change. The expected output of the protocol is an integrated analytical framework for evaluating the economic, institutional, resource-use and social conditions under which FMaaS may become a viable and socially acceptable pathway to Agriculture 4.0. By combining transaction-cost theory, market-design reasoning, stated-preference methods, farm-level modeling and social-sustainability assessment, the protocol provides a transparent basis for future empirical research on access-based digital mechanization. Full article