Search Results (9)

Extreme random events will interfere with the inversion analysis of energy and environment systems (EES) and make the planning schemes unreliable. A Copula-based interval cost–benefit stochastic programming (CICS) is proposed to deal with extreme random uncertainties. Taking Yulin city as an example, there are nine constraint-violation scenarios and six coal-reduction scenarios are designed. The results disclose that (i) both system cost and pollutant emission would decrease as the industrial energy supply constraint-violation level increases; (ii) when the primary and secondary energy output increases by 9% and 13%, respectively, and industrial coal supply decreases by 40%, the coal-dependent index of the system would be the lowest, and the corresponding system profitability could reach [29.3, 53.0] %; (iii) compared with the traditional chance-constrained programming, Copula-based stochastic programming can reflect more uncertain information and achieve a higher marginal net present value rate. Overall, the CICS-EES model offers a novel approach to gain insight into the tradeoff between system reliability and profitability. Full article

The purposed longitudinal study analyzes the evolution of students’ Entrepreneurial Intentions (EI) and its antecedents over the COVID-19 pandemic period and explores the inter-individual differences and the intra-individual changes. Our main contribution consists of proposing two-panel estimations techniques: first, a Latent Change Score model (LCS) approach to analyze the stability of our constructs, and second, a Generalized Least Squares (GLS) Random-Effect estimation of a complex network of relationships that we have identified within the Theory of Planned Behavior (TPB) framework. Our empirical results show that students’ EI enrolled in an entrepreneurship education (EE) program increased during the COVID-19 pandemic. Individuals with initial higher scores for EI have changed less than those with lower initial scores, and a gender difference exists in the initial level of EI and its antecedents. Our results also document the moderation effects of entrepreneurial self-efficacy and the importance of inspiration and resources in building students’ EI and provide valuable policy recommendations for universities regarding the design of EE programs to contribute to the economic recovery in the post-pandemic era. Full article

Low-altitude cellular-enabled Unmanned Aerial Vehicles (UAVs) provide potential supplementary platforms to assist air-to-ground cooperative communication. This paper investigates a joint safety information interaction scheme for a UAV-enabled network, which involves the complex constraints of three-dimensional trajectory planning, average energy efficiency optimization, and physical-layer security. Specifically, by modeling the UAV and the Ground Station (GS) as the transmit sources, we define the secure Energy Efficiency (EE) as the ratio of the total secrecy rate to the energy consumption of the whole system. Then, to achieve secure and energy-efficient communication in eavesdropping scenarios, we formulated the optimization problem as maximizing both the uplink/downlink secure EE of the system, subject to the constraints of the UAV’s mobility and the allowable transmit power. For this highly coupled non-convex problem, a composite solution of joint fractional programming, alternate optimization, the bisection method, and the interior-point method is proposed to obtain the achievable EE. Simulation and performance analysis gave the conclusions that the joint optimization of trajectory and power allocation is capable of maximizing the information secure EE. Additionally, the secure EE increases with the increase of the average transmit power, which finally tends to be stable. Full article

The European Union 2050 climate neutrality goal and the climate crisis require coordinated efforts to reduce energy consumption in all sectors, and mainly in buildings greatly affected by the increasing temperature, with relevant CO₂ emissions due to inefficient end-use technologies. Moreover, the old building stock of most countries requires suited policies to support renovation programs aimed at improving energy performances and optimize energy uses. A toolbox was developed in the framework of the PrioritEE project to provide policy makers and technicians with a wide set of tools to support energy efficiency in Municipal Public Buildings. The toolbox, available for free, was tested in the partners’ communities, proving its effectiveness. The paper illustrates its application to the Potenza Municipality case study in which the online calculator DSTool (the core instrument of the toolbox) was utilized to select and prioritize the energy efficiency interventions in public buildings implementable in a three-year action plan in terms of costs, energy savings, CO₂ emissions’ reduction and return on investments. The results highlight that improvements in the building envelopes (walls and roofs), heating and lighting and photovoltaic systems allow reducing CO₂ emission approximately 644 t/year and saving about 2050 MWh/year with a total three-year investment of 1,728,823 EUR. Full article

Electrochemical energy storage (EES) is a promising kind of energy storage and has developed rapidly in recent years in many countries. EES planning is an important topic that can impact the earnings of EES investors and sustainable industrial development. Current studies only consider the profit or cost of the EES planning program, without considering other economic criteria such as payback period and return on investment (ROI), which are also important for determining an optimal EES planning program. In this paper, a new hybrid multi-criteria decision-making (MCDM) method integrating the Bayesian best-worst method (BBWM), the entropy weighting approach, and grey cumulative prospect theory is proposed for the optimal EES planning program selection with the consideration of multiple economic criteria. The BBWM and entropy weighting approach are jointly employed for determining the weightings of criteria, and the grey cumulative prospect theory was utilized for the performance rankings of different EES planning programs. Five EES planning programs were selected for empirical analysis, including 9MW PbC battery EES, 2MW LiFePO lithium ion battery EES, 3MW LiFePO lithium ion battery EES, 2MW vanadium redox flow battery EES, and 3MW vanadium redox flow battery EES. The empirical results indicate the 2MW LiFePO lithium ion battery EES is the optimal one. The sensitivity analysis related to different risk preferences of decision-makers also shows the 2MW LiFePO lithium ion battery EES is always the optimal EES planning program. The proposed MCDM method for the optimal EES planning program selection in this paper is effective and robust, and can provide certain references for EES investors and decision-makers. Full article

This work presents a multi-course project-based learning (MPL) approach implemented using two electrical engineering (EE) interdisciplinary undergraduate courses at Qatar University. Implementing an MPL approach helps in the development of critical thinking and collaborative decision-making skills. The attainment of these skills is also the outcome of education for sustainable development (ESD); the skills help students acquire the knowledge, attitudes, and values necessary to shape a sustainable future. The participating students’ worked on a design project, which was used to assess the fulfillment of a set of student learning outcomes (SLOs), focusing on engineering soft skills and project management skills. The skills include the ability to communicate effectively, to work collaboratively in a team, to think both critically and creatively, and to manage projects efficiently with realistic constraints and standards. The challenges of implementing the MPL method are the organization of pedagogical activities that are planned for each of the courses involved, the coordination of the materials delivered by each course, and the supervision of around 90 students per year performing the MPL method. The experience of MPL deployment in the EE program was rated using student surveys. It was assumed that the MPL approach would be beneficial to the students based on the instructors’ and students’ feedback from the same courses in previous years. This was verified using chi-square statistics of the survey results. The implementation of the MPL also helped in increasing the average marks scored by the students in the design project. Some interesting feedback, statistical analyses, and improvement actions are reported for future upgrades. This work also contributes to the MPL pragmatic body of knowledge by exploring a successful initiative and its outcomes, which can help in attaining the skills needed for ESD. Full article

Over the past twenty years, many countries have been looking for alternative procurement models in providing public sector energy efficiency (EE) projects because of high public sector debt and budget deficit. These projects have traditionally been procured and financed by the public sector. While the majority of EU funding resources will be realized in more traditional, purely grant-funded procurement models, a new Investment Plan for Europe for the programming period 2014–2020 has focused on supporting higher private sector involvement in infrastructure investments for achieving the EU Strategy 20/20/20 goals. Seeing that the fundamental purpose of investing in infrastructure is investing in providing public services, the involvement of the private sector is only possible through some alternative procurement models. In these initiatives both the public and the private sector retain their own identities and responsibilities, while their co-operation is based on clearly defined divisions of tasks and risks. The main objective of this paper is to assess the effectiveness of blending alternative procurement models with available EU funding mechanisms in EE sector by applying case study simulation. The findings will show the effectiveness of alternative options and the use of blending solutions in EE investments. Full article

Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization—energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties. Full article

Increasing coastal space resource utilization (CSRU) activities and their impact on coastal environments has been recognized as a critical coastal zone stressor. Consequently, the need for sustainable and valid CSRU management has been highlighted. In this study, a highly-intelligent prototype decision-aided system for CSRU was developed. In contrast with existing coastal decision-aided systems, this system is aimed at the management of CSRU, providing reliable and dynamic numerical simulation, analysis, and aided decision making for real coastal engineering based on a self-developed fully automatic numerical program. It was established on multi-tier distributed architecture based on Java EE. The most efficient strategies for spatial data organization, automatic coastal numerical programs, and impact assessment modules are demonstrated. In addition, its integrated construction involving the addition of a new coastal project on the webpage, its one-click numerical prediction of coastal environmental impacts, assessments based on numerical results, and its aided decision-making capabilities are addressed. The system was applied to Ningbo Sea, China, establishing the Ningbo CSRU Decision Support System. Two projects were demonstrated: one reclamation project and one land-based outlet planning case. Results indicated that these projects had detrimental effects on local coastal environments. Therefore, the approvals of these projects were not recommended. Full article