Search Results (73)

A Life cycle assessment (LCA) is widely used to evaluate the carbon reduction potential of polycrystalline silicon photovoltaic systems. However, in existing LCA methods, most studies use static attenuation models and fixed lifecycle boundary frameworks. Therefore, this study proposes a dynamic LCA framework that considers the attenuation rate changes in photovoltaic systems and the energy gain during the recovery phase. The innovation of this method lies in its ability to more accurately reflect the carbon emissions and energy recovery period (EPBT) of photovoltaic systems under different operating and attenuation scenarios. In addition, this article expands the application scope of the LCA by introducing new boundary conditions, providing a new perspective for the lifecycle assessment of photovoltaic systems. A practical carbon emission calculation model was established using the full lifecycle data within this boundary, and the quantitative relationship between the EPBT and power generation was derived. A three-dimensional dynamic coupling model was developed to integrate these three key parameters and continuously characterize the dynamic behavior of the system throughout its entire lifecycle. This model explicitly addresses the attenuation of photovoltaic modules in three scenarios: low (1%), baseline (3%), and high (5%) attenuation rates. The results show that under low attenuation, the average EPBT is 4.14 years, which extends to 6.5 years under high attenuation and only 2.37 years under low attenuation. Sensitivity analysis confirmed the effectiveness of the model in representing the dynamic evolution of photovoltaic systems, providing a theoretical basis for subsequent environmental performance evaluations. Full article

The activation and dissociation of O₂ molecules play a key role in the oxidation of toxic gas molecules and the oxygen reduction reaction (ORR) in hydrogen–oxygen fuel cells. The interactions between O₂ molecules and the surfaces of Fe-doped γ-graphyne were systematically explored, mainly adopting the combined method of the density functional theory with dispersion correction (DFT-D3) and the climbing image nudged elastic band (CI-NEB) method. The order of the formation energy values of these defective systems is E_f(Fe_C2) < E_f(Fe_C1) < E_f(Fe_D1) < E_f(V_C1) < E_f(V_D1) < E_f(V_C2) < E_f(Fe_D2) < E_f(V_D2), which indicates that the process of Fe dopant atoms substituting single-carbon atoms/double-carbon atoms is relatively easier than the formation of vacancy-like defects. The results of ab initio molecular dynamics (AIMD) simulations confirm that the doped systems can maintain structural stability at room temperature conditions. Fe-doped atoms transfer a certain amount of electrons to the adsorbed O₂ molecules, thereby causing an increase in the O-O bond length of the adsorbed O₂ molecules. The electrons obtained by the anti-bonding 2π* orbitals of the adsorbed O₂ molecules are mainly derived from the 3d orbitals of Fe atoms. There is a competitive relationship between the substrate’s carbon atoms and the adsorbed O₂ molecules for the charges transferred from Fe atoms. In the C1 and C2 systems, O₂ molecules have a greater advantage in electron accepting ability compared to the substrate’s carbon atoms. The elongation of O-O bonds and the amount of charge transfer exhibit a positive relationship. More electrons are transferred from Fe-3d orbitals to adsorbed O₂ molecules, occupying the 2π* orbitals of adsorbed O₂ molecules, further elongating the O-O chemical bond until it breaks. The dissociation process of adsorbed O₂ molecules on the surfaces of GY-Fe systems (C2 and D2 sites) involves very low energy barriers (0.016 eV for C2 and 0.12 eV for D2). Thus, our studies may provide useful insights for designing catalyst materials for oxidation reactions and the oxygen reduction reaction. Full article

To effectively address the issues of curtailed wind and photovoltaic (PV) power caused by the high proportion of renewable energy integration and to promote the clean and low-carbon transformation of the energy system, this paper proposes a “chemical–mechanical” dual-pathway synergistic mechanism for the reversible solid oxide cell (RSOC) and flywheel energy storage system (FESS) electricity–hydrogen hybrid system. This mechanism aims to address both short-term and long-term energy storage fluctuations, thereby minimizing economic costs and curtailed wind and PV power. This synergistic mechanism is applied to regulate system operations under varying wind and PV power output and electricity–hydrogen load fluctuations across different seasons, thereby enhancing the power generation system’s ability to integrate wind and PV energy. An economic operation model is then established with the objective of minimizing the economic costs of the electricity–hydrogen hybrid system incorporating RSOC and FESS. Finally, taking a large-scale new energy industrial park in the northwest region as an example, case studies of different schemes were conducted on the MATLAB platform. Simulation results demonstrate that the reversible solid oxide cell (RSOC) system—integrated with a FESS and operating under the dual-path coordination mechanism—achieves a 14.32% reduction in wind and solar curtailment costs and a 1.16% decrease in total system costs. Furthermore, this hybrid system exhibits excellent adaptability to the dynamic fluctuations in electricity–hydrogen energy demand, which is accompanied by a 5.41% reduction in the output of gas turbine units. Notably, it also maintains strong adaptability under extreme weather conditions, with particular effectiveness in scenarios characterized by PV power shortage. Full article

Existing CO₂ leakage risk assessment frameworks for CO₂ capture, geological storage and utilization (CCUS) projects face limitations due to subjective biases and poor adaptability to long-term scale sequestration. This study proposed a dynamic risk assessment method for CO₂ leakage based on a timeliness analysis of different leakage paths and accurate time-dependent numerical simulations, and it was applied to the first CO₂ enhanced gas recovery (CCUS-EGR) pilot project of China in the Maokou carbonate gas reservoir in the Wolonghe gas field. A 3D geological model of the Maokou gas reservoir was first developed and validated. The CO₂ leakage risk under different scenarios including wellbore failure, caprock fracturing, and new fracture activation were evaluated. The dynamic CO₂ leakage risk of the CCUS-EGR project was then quantified using the developed method and numerical simulations. The results revealed that the CO₂ leakage risk was observed to be the most pronounced when the caprock integrity was damaged by faults or geologic activities. This was followed by leakage caused by wellbore failures. However, fracture activation in the reservoir plays a neglected role in CO₂ leakage. The CO₂ leakage risk and critical risk factors dynamically change with time. In the short term (at 5 years), the project has a low risk of CO₂ leakage, and well stability and existing faults are the major risk factors. In the long term (at 30 years), special attention should be paid to the high permeable area due to its high CO₂ leakage risk. Factors affecting the spatial distribution of CO₂, such as the reservoir permeability and porosity, alternately dominate the leakage risk. This study established a method bridging gaps in the ability to accurately predict long-term CO₂ leakage risks and provides a valuable reference for the security implementation of other similar CCUS-EGR projects. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability. Full article

Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in effectively capturing the multifractality inherent in carbon market prices. Inspired by the self-similarity and scale invariance inherent in fractal structures, this study proposes a novel multifractal-aware model, MF-Transformer-DEC, for carbon market price prediction. The multi-scale convolution (MSC) module employs multi-layer dilated convolutions constrained by shared convolution kernel weights to construct a scale-invariant convolutional network. By projecting and reconstructing time series data within a multi-scale fractal space, MSC enhances the model’s ability to adapt to complex nonlinear fluctuations while significantly suppressing noise interference. The fractal attention (FA) module calculates similarity matrices within a multi-scale feature space through multi-head attention, adaptively integrating multifractal market dynamics and implicit associations. The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. The proposed model achieved an average R² of 0.9777 and 0.9942 for 7-step ahead predictions on the Shanghai and Guangdong carbon price datasets, respectively. This study pioneers the interdisciplinary integration of fractal theory and artificial intelligence methods for complex engineering analysis, enhancing the accuracy of carbon market price prediction. The proposed technical pathway of “multi-scale deconstruction and similarity mining” offers a valuable reference for AI-driven fractal modeling. Full article

Aiming at the problems of serious pavement temperature diseases, low efficiency and high loss of ice-breaking methods, high occupancy rate of waste tires and the low utilization rate and insufficient durability of rubber particles, this paper aims to improve the service level of roads and ensure the safety of winter pavements. A pavement material with high efficiency, low carbon and environmental friendliness for active snow melting and ice breaking is developed. Firstly, NaOH, NaClO and KH550 were used to optimize the treatment of rubber particles. The hydrophilic properties, surface morphology and phase composition of rubber particles before and after optimization were studied, and the optimal treatment method of rubber particles was determined. Then, the optimized rubber particles were used to replace the natural aggregate in the polyurethane gel mixture by the volume substitution method, and the optimum polyurethane gel dosages and molding and curing processes were determined. Finally, the influence law of the road performance of RPGM was compared and analyzed by means of an indoor test, and the ice-breaking effect of RPGM was explored. The results showed that the contact angles of rubber particles treated with three solutions were reduced by 22.5%, 30.2% and 36.7%, respectively. The surface energy was improved, the element types on the surface of rubber particles were reduced and the surface impurities were effectively removed. Among them, the improvement effect of the KH550 solution was the most significant. With the increase in rubber particle content from 0% to 15%, the dynamic stability of the mixture gradually increases, with a maximum increase of 23.5%. The maximum bending strain increases with the increase in its content. The residual stability increases first and then decreases with the increase in rubber particle content, and the increase ranges are 1.4%, 3.3% and 0.5%, respectively. The anti-scattering performance increases with the increase in rubber content, and an excessive amount will lead to an increase in the scattering loss rate, but it can still be maintained below 5%. The fatigue life of polyurethane gel mixtures with 0%, 5%, 10% and 15% rubber particles is 2.9 times, 3.8 times, 4.3 times and 4.0 times higher than that of the AC-13 asphalt mixture, respectively, showing excellent anti-fatigue performance. The friction coefficient of the mixture increases with an increase in the rubber particle content, which can be increased by 22.3% compared with the ordinary asphalt mixture. RPGM shows better de-icing performance than traditional asphalt mixtures, and with an increase in rubber particle content, the ice-breaking ability is effectively improved. When the thickness of the ice layer exceeds 9 mm, the ice-breaking ability of the mixture is significantly weakened. Mainly through the synergistic effect of stress coupling, thermal effect and interface failure, the bonding performance of the ice–pavement interface is weakened under the action of driving load cycle, and the ice layer is loosened, broken and peeled off, achieving efficient de-icing. Full article

The regional integrated energy system, centered on electro-hydrogen technology, serves as a crucial mechanism for advancing the utilization of a high proportion of renewable energy and achieving the low-carbon transition of the energy system. In this context, a multi-time scale optimization model for distributed electro-hydrogen coupling systems is proposed, utilizing an enhanced deep reinforcement learning (DRL) method. Firstly, considering the comprehensive operation cost and real-time deviations, the optimization model of day-ahead and real-time multi-time scale electro-hydrogen coupling system is constructed. Secondly, A dynamic perception model of environmental information is established based on a time convolutional network (TCN) to achieve multi-time scale feature capture of the coupling system and to improve the ability of the agents to perceive the environment of the coupling system. Then, the proposed optimization model is transformed into the Markov decision process (MDP), and a modified Proximal Policy Optimization (PPO) algorithm is introduced to achieve optimal solutions. Finally, case studies are conducted to analyze the electro-hydrogen coupling system in a specific region. The case studies verify the effectiveness of deep reinforcement learning and the electro-hydrogen coupling system in new energy consumption. Full article

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article

This study explores the crucial role of the banking industry in addressing climate change and promoting environmental sustainability. As climate change increasingly threatens global economies, ecosystems, and public health, financial experts recognize the potential for the banking sector to contribute to a low-carbon economy. By incorporating green-banking practices, which blend traditional financial services with environmental, social, and economic considerations, banks can foster sustainable development while reaping financial benefits. The research examines the dynamics of sustainable banking, focusing on its ability to drive efficiency improvements through eco-friendly programs and enhance profitability. Furthermore, the study discusses the challenges in adopting and implementing environmental sustainability, comparing the command-and-control regulatory model with voluntary approaches. The findings emphasize the importance of effective regulation and incentives for ensuring that banks adopt sustainable practices, ultimately contributing to a more resilient and low-carbon economic system. Through this analysis, this study underscores the banking industry′s pivotal role in shaping the transition towards a sustainable future. Full article

The global energy crisis and the pursuit of carbon neutrality have introduced significant challenges to the optimal dispatch of power systems. Despite advancements in optimization techniques, existing methods often struggle to efficiently handle the uncertainties introduced by renewable energy sources and the dynamic behavior of plug-in electric vehicles (PEVs). This study presents a multi-energy collaborative optimization approach based on a dynamic opposite level-based learning optimization swarm algorithm (DO3LSO). The methodology explores the impact of integrating PEVs and renewable energy sources, including photovoltaic and wind power, on unit commitment (UC) problems. By incorporating the bidirectional charging and discharging capabilities of PEVs and addressing the volatility of renewable energy, the proposed method demonstrates the ability to reduce reliance on traditional fossil fuel power generation, decrease carbon emissions, stabilize power output, and achieve a 7.01% reduction in costs. Comparative analysis with other optimization algorithms highlights the effectiveness of DO3LSO in achieving rapid convergence and precise optimization through hierarchical learning and dynamic opposite strategies, showcasing superior adaptability in complex load scenarios. The findings underscore the importance of multi-energy collaborative optimization as a pivotal solution for addressing the energy crisis, facilitating low-carbon transitions, and providing essential support for the development of intelligent and sustainable power systems. Full article

The use of supercritical carbon dioxide (ScCO₂) as a replacement for volatile organic solvents in coatings has the potential to reduce air pollution. This paper presents the findings of a molecular dynamics simulation study investigating the dissolution behavior of polyvinylidene fluoride (PVDF) in ScCO₂ assisted by five co-solvents. On the basis of solubility parameters, interaction binding energy, and radial distribution functions, the impacts of temperature, pressure, and co-solvents on the compatibility of ScCO₂ and PVDF were investigated at the microscopic level. The simulation results demonstrated that low-temperature and high-pressure conditions facilitate the dissolution of PVDF in ScCO₂, where the optimal conditions are 308.15 K and 16 MPa. The enhancement of the solubility performance of ScCO₂ slowed down with increasing pressure, but was more sensitive to changes in temperature. The weak attraction between PVDF and ScCO₂ was synergized by van der Waals and electrostatic forces, making it challenging to achieve complete and homogeneous mixing. The use of co-solvents with strong polarity can enhance the solvent system’s solubility. Ethanol and 2-butoxy-1-ethanol have obvious solubilizing abilities due to the hydrogen bond donors, which can generate hydrogen bonding interactions with ScCO₂, increase the polarity of the solvent system, and promote the compatibility of ScCO₂ with PVDF. Full article

With the increasing and intensifying effects of global climate change and the rapid development of higher education, energy and resource consumption at university campuses has been rising drastically. This shift has been worsened by campuses’ expanded role in addressing extreme weather hazards and taking on additional cultural and community functions. This article carries out a comprehensive literature review of the low-carbon measures and resilient behaviors implemented on university campuses based on publications published in two major databases, the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Results show that: (1) most existing studies only focus on campus carbon emission reduction from a single perspective, without considering the correlation between carbon emissions in different dimensions on campuses and without analyzing the causes of excessive campus carbon emissions from the perspective of the built environment; (2) current studies have not constructed an assessment system for campus carbon resilience and lack the tools and methods for assessment. After summarizing and analyzing, this study proposes the concept of campus “carbon resilience”, which refers to the ability of campuses to cope with the risks of disasters and uncertainties caused by excessive carbon emissions. The research framework of this study is divided into three parts: connotative characteristics, influencing factors, and optimization strategy. Following this framework, the concept and critical features of campus carbon resilience “carbon minus resilience”, “carbon saving resilience”, “carbon reduction resilience”, and “carbon sequestration resilience” are analyzed and outlined. Next, an integrated impact factor system for campus carbon resilience is proposed. This system incorporates aspects such as land utilization, building operation, landscape creation, and energy regeneration from the perspective of the built environment. Finally, with the core objective of effectively reducing the dynamic range of carbon emissions when dealing with critical disturbances and improving the adaptability and resilience of campuses to cope with excessive carbon emissions, this study proposes an optimization strategy of “setting development goals–establishing an evaluation system–proposing improvement strategies–dynamic feedback and adjustment” to provide ideas and theoretical guidance for responding to university campus carbon risk and planning carbon resilience. Full article

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the low-carbon green innovation R&D activities of enterprises into two distinct stages, as follows: the green R&D stage and the results transformation stage. It then assesses the efficiency of each stage and provides an overall efficiency rating. The model has been applied to a sample of listed Chinese iron and steel enterprises (CISES). The results of the study show that the overall efficiency of low-carbon innovation and development of CISES is on the low side, with the highest efficiency achieved in the green R&D stage, which is less than the lowest efficiency attained in the transformation stage, and most of the enterprises are in the stage of high green R&D and low transformation of the results. The ability of marketization of the R&D results still needs to be strengthened. Full article