Search Results (559)

To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO₂ emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities. Full article

This study investigates the evaluation of housing policies for migrants in China, focusing on the interplay between rental and purchase decisions under the rent-and-purchase policy (RPP) framework. Employing a system dynamics model, we simulate migrant housing choices from 2001 to 2023 and forecast market trends from 2024 to 2030. The results indicate that RPPs significantly improve housing quality and reduce costs for migrants by mitigating institutional disparities and market distortions. Scenario analyses demonstrate that a coordinated approach combining supply-side interventions (e.g., affordable housing expansion) with rights-based policies (e.g., equalizing renter and buyer rights) effectively balances affordability and demand stability. The findings emphasize the critical role of addressing rights inequalities and advocate for a holistic policy framework to tackle migrant housing challenges, offering actionable insights for policymakers in system science and urban planning. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

Preventing food waste is a pressing global policy concern, with households being the main producers of food waste along the food supply chain. This study aims to analyze consumers’ food waste behavior and identify how different consumer profiles and sociodemographic characteristics influence food waste. A survey was carried out in Spain with a representative sample of 717 participants, and the Theory of Planned Behavior (TPB) was applied to understand the influence of consumers’ attitudes, subjective norms, and perceived behavior control on their intention to reduce food waste and to find out the main drivers of their food waste behaviors. Results demonstrated that food waste reduction is mainly predicted by attitudes, followed by perceived behavior control, and lastly subjective norms. Finally, characteristics such as responsibility in food purchasing and cooking at home as well as sociodemographic factors played a relevant role in how much the intention to reduce food waste affects the final behavior. Our results suggest the potential of communication to reshape individual preferences towards valuing food conservation. Tailored strategies are recommended for specific groups, emphasizing the importance of targeted approaches in addressing food waste at various levels of society. Full article

Social media influencers (SMIs) have become pivotal stakeholders in digital marketing. This study examines how SMIs influence consumer decision-making and investigates the role of trust in this process. Drawing on the theory of planned behavior (TPB), we developed a research model with testable hypotheses. Using partial least squares structural equation modeling (PLS-SEM), we analyzed survey data from 232 social media users in Greater London, UK. Our results indicated that SMIs significantly enhance purchase intentions, yet these intentions exhibited only a weak conversion into actual purchasing behavior. Contrary to expectations, trust in SMIs demonstrated a significant negative relationship with purchase intention, suggesting that higher trust may paradoxically diminish purchase likelihood. This counterintuitive finding underscores the complexity of trust dynamics in influencer marketing, where perceived commercialization or consumer skepticism may counteract its positive effects. Furthermore, while SMIs strongly foster trust, our analysis reveals that trust does not mediate the relationship between SMIs and actual purchases. These findings contribute to literature by elucidating the nuanced role of trust and highlighting the intention–behavior gap in influencer marketing. Future research could explore contextual and psychological moderators to deepen our understanding of trust dynamics. Full article

This article proposes an optimization methodology to address the joint placement as well as the capacity design of PV units and D-STATCOMs within unbalanced three-phase distribution systems. The proposed model adopts a mixed-integer nonlinear programming structure using complex-valued variables, with the objective of minimizing the total annual cost—including investment, maintenance, and energy purchases. A leader–followeroptimization framework is adopted, where the leader stage utilizes the Generalized Normal Distribution Optimization (GNDO) algorithm to generate candidate solutions, while the follower stage conducts power flow calculations through successive approximation to assess the objective value. The proposed approach is tested on 25- and 37-node feeders and benchmarked against three widely used metaheuristic algorithms: the Chu and Beasley Genetic Algorithm, Particle Swarm Optimization, and Vortex Search Algorithm. The results indicate that the proposed strategy consistently achieves highly cost-efficient outcomes. For the 25-node system, the cost is reduced from USD 2,715,619.98 to USD 2,221,831.66 (18.18%), and for the 37-node system, from USD 2,927,715.61 to USD 2,385,465.29 (18.52%). GNDO also surpassed the alternative algorithms in terms of solution precision, robustness, and statistical dispersion across 100 runs. All numerical simulations were executed using MATLAB R2024a. These findings confirm the scalability and reliability of the proposed method, positioning it as an effective tool for planning distributed energy integration in practical unbalanced networks. Full article

Premium fashion brands are increasingly adopting Generative Artificial Intelligence (GenAI) to reduce costs and enhance creativity. However, consumers have mixed perceptions of clothing with AI-generated patterns (CAGPs) launched by premium fashion brands, especially in online shopping contexts where consumers cannot examine physical products firsthand. This study integrates the Theory of Planned Behavior (TPB) with Customer Perceived Value (CPV) to investigate Chinese Millennials’ attitudes and purchase intentions toward online purchases of CAGPs launched by premium fashion brands. Using a purposive sampling approach, the study collected 471 valid responses from Chinese Millennials. Structural equation modeling (SEM) was then employed to test the proposed model and hypotheses. The results reveal that perceived brand design effort and perceived price value are primary drivers of purchase intention for CAGPs from premium fashion brands, while perceived aesthetic value significantly shapes consumer attitudes. The subjective norm and attitude positively influence purchase intention. This study sheds light on the roles of aesthetic, emotional, monetary and social factors in driving purchase intention, offering practical suggestions for premium brands’ product design and marketing strategies. Full article

With the rapid development of China’s electric vehicle industry, the influence mechanism of government policies on consumers’ purchase intentions has become a research focus. This study integrates the technology acceptance model (TAM) and SOR theory to propose four key driving factors: policy incentive, perceived usefulness, perceived ease of use, and test drive experience. Through stratified random sampling of 400 valid questionnaires in Shanghai, Beijing, Shenzhen, and Guangzhou, four cities with a high penetration rate of electric vehicles, the structural equation model (SEM) was used for empirical analysis. The results show that policy incentives have a significant impact on purchase intentions and play a mediating role through perceived usefulness and perceived ease of use; driving experience moderates the effects of perceived usefulness and perceived ease of use on purchase intentions. Based on the research results, this paper proposes a three-stage policy optimization path: strengthening the accuracy of fiscal and tax incentives in the short term, improving the visual construction of the charging network in the medium term, and establishing a network of test drive experience centers in the long term. The research conclusions provide a theoretical basis for the government to formulate differentiated electric vehicle promotion strategies and propose a “policy-technology-service” three-dimensional implementation plan for enterprises to optimize product design and improve user experience, so as to help the sustainable development of China’s electric vehicle market. Full article

Energy-efficient consumption has become a strategic priority to mitigate global climate change and enhance national energy security. While social media has reshaped online consumption behavior, the mechanisms through which these digital platforms influence energy-efficient purchasing remain underexplored. This study extends the Theory of Planned Behavior (TPB) by integrating price perception variables and applies multi-group structural equation modeling to examine how social media shapes Chinese consumers’ intentions to purchase energy-efficient air conditioning. The results show that (1) social media exposure strengthens energy-efficient purchasing intentions indirectly via behavioral attitude, subjective norm, and perceived behavioral control; (2) price perception is negatively associated with purchase intention; and (3) these effects vary by age cohort, gender, and income—Generation Z and female consumers are more susceptible to social media influence, while low-income groups exhibit heightened price sensitivity. These findings advance TPB theory and offer guidance for digital platform policies aimed at promoting energy-efficient consumption. Full article

Addressing the pressing global challenge of environmental sustainability, this study investigates novel pathways through which egoistic motivations—specifically personal health benefits, economic advantages, and perceived social status—influence green purchasing behavior among Chinese consumers. Employing an integrated approach that combines the theory of planned behavior (TPB) and self-identity theory (SIT), the research analyzes data from 361 Chinese consumers using advanced statistical techniques, including structural equation modeling (SEM). The findings reveal unique insights: personal health benefits and economic advantages emerge as significant drivers of green purchasing behavior, while perceived social status exerts an indirect effect through symbolic benefits. This study breaks new ground by demonstrating the dual mediating role of symbolic and functional benefits in linking egoistic motivations to green purchasing behavior. The results underscore the importance of developing marketing strategies that highlight personal health and economic savings, complemented by symbolic benefits, to effectively promote green products. Policymakers are encouraged to incorporate these nuanced motivations when designing incentives and regulations to enhance sustainable consumption practices. Full article

Commuting plays a critical role in shaping sustainable transport systems, yet understanding the diverse preferences of commuter groups remains a challenge for policymakers. As cities aim to promote sustainable transport, it is essential to better understand the factors influencing travel behaviors. This study investigates the commuting preferences and behaviors of urban employees in Italy, focusing on identifying distinct user profiles and their implications for policy development. Using a dataset of 2301 participants from Italian cities, the research analyzed transport mode choices, willingness to adopt sustainable transport options, and perceptions of public transport (PT) services, including factors such as travel time, proximity to PT stops, cost, and comfort, rated on a four-point Likert scale. K-modes clustering was employed to segment participants into three clusters based on their travel behaviors. The results revealed three distinct user profiles: (1) car-dependent users with negative perceptions of PT, driven by family obligations and dissatisfaction with PT services; (2) individuals who primarily use cars but are somewhat open to improvements in PT; (3) individuals willing to adopt alternative mobility options, including active and shared transport modes. Significant differences were found across clusters in terms of mode choices, willingness to use sustainable transport, and satisfaction with PT services. Notably, employees showed limited interest in alternative sustainable transport modes such as e-scooters and walking, with 73% and 66% of participants expressing little or no interest, respectively. Despite incentives such as company subsidies for purchasing bicycles or e-scooters, 58% of employees remained uninterested in adopting these alternatives. Additionally, employees’ perceptions of PT services revealed dissatisfaction with factors such as travel time, comfort, and punctuality, with over 70% rating these aspects as “Poor” or “Fair”. These findings suggest that improving the quality of PT services, particularly in terms of travel time, punctuality, comfort, and cost, should be a priority for enhancing user satisfaction. This research provides valuable insights for policymakers seeking to reduce car dependence and promote sustainable urban transport planning. Full article

Willingness to pay (WTP) measurements often contain a hypothetical bias (HB) when participants’ responses result from ‘fictitious’ survey scenarios rather than actual purchasing behavior or field studies. This discrepancy usually leads to inaccurate WTP values, which affect pricing strategies. Our quantitative online survey with German consumers (N = 215) examines the HB of WTP for different mobile phone plans as an example of a widespread consumer good. The aim is to focus on the correlation between hypothetical and actual WTP and the influence of socio-demographic factors on the HB. We used the Certainty Approach to correct hypothetical WTP data to reflect actual payment behavior. The findings show that hypothetical WTP values are generally higher than current expenditure, which demonstrates that HB significantly affects WTP measurements in the context of mobile communications products. The applied Certainty Approach successfully reduced this discrepancy. We found a moderate negative correlation between actual WTP and the extent of the HB, indicating that higher actual WTP is associated with lower bias. Moreover, socio-demographic factors such as age and income do not significantly influence the HB. This study suggests pricing strategies should consider HB-adjusted WTP values to avoid management decisions based on inflated hypothetical data. Full article

Electric vehicles are transforming urban and metropolitan transportation, providing significant benefits to both the environment and society. However, the integration of electric vehicles necessitates a well-planned infrastructure, including a sufficient number of charging stations distributed at the local level, policies that encourage the purchase and operation of electric vehicles, and the active participation of local governments and the automotive industry. Investments in improved car technologies, as well as renewable energy sources, will be critical in the shift to more sustainable metropolitan regions that have reduced pollution. Computer simulation based on virtual models performs an important role in the optimization of urban and metropolitan traffic by allowing for the rapid prototyping of real vehicle models, as well as the implementation of a wide range of test scenarios in real time. Assisted driving functions are critical in adjusting optimal driving behaviors to each of the particular scenarios of urban and metropolitan traffic. The situations discussed in this study were derived from real-world traffic and implemented and simulated on virtual models in the CarMaker version 12 application. To calibrate electricity consumption in each of the metropolitan area’s sectors, driving cycles were embedded in the virtual model. These were allocated to component sectors based on the average travel speed and its variation. Full article

This study evaluates four hybrid infrastructure scenarios for supporting battery electric buses (BEBs) and fuel cell electric buses (FCEBs), analyzing different combinations of grid power, solar energy, battery storage, and fuel cell systems. A multi-stage framework—comprising energy demand forecasting, infrastructure capacity planning, and multi-criteria decision-making (MCDM) evaluation incorporating total cost of ownership (TCO), carbon emissions, and energy resilience—was developed and applied to a real-world transit depot. The results highlight critical trade-offs between financial, environmental, and operational objectives. The limited rooftop solar configuration, integrating solar energy through a Solar Power Purchase Agreement (SPPA), emerges as the most cost-effective near-term solution. Offsite solar with onsite large-scale battery storage and offsite solar with fuel cell integration achieve greater sustainability and resilience, but they face substantial cost barriers. The analysis underscores the importance of balancing investment, emissions reduction, and resilience in planning zero-emission bus fleets. Full article