Emission Energy Research Articles

Fracturing evolution of red sandstone: insights from three-point bending experiment and numerical simulation considering material inhomogeneity and internal discontinuities

To reveal the various propagation paths of micro-cracks under the continuous process of stress buildup, stress shadow, and stress transfer, three-point bending experiments and numerical simulations were carried out by considering material inhomogeneity and internal discontinuities. The characteristics of red sandstone fracturing evolution were analyzed from the aspects of acoustic emission (AE) energy index, infrared radiation (IR) changes, fracture surface roughness, stress fields and so on. The test results indicate that four stages are divided in the gradual process of energy release of red sandstone fracturing under three-point bending test, the rough fracture surfaces of crack were extremely small, tensile crack makes the largest proportion. IR and AE perform some significant precursor information of rock fracturing, e.g., a large amount of high-temperature debris scattered in infrared thermography, the maximum value of AE accumulative energy and the concentration effect of AE events location. Different tensile stress level has different features, macroscopic fracture morphology happens in a low level, and micro-cracks appears in the weakness of crystal surfaces in a high level. It needs to be emphasized that five different modes, pass through, crack-tip blunting, extended-back, crack-forking and passing round, were concluded in terms of the repeated process of stress buildup, stress shadow & stress transfer. These achievements contribute to the better understanding of the failure mechanisms of red sandstone.

Sustainability building materials and circularity score of residential buildings

Building materials used, approaches and technologies related to the operation of residential buildings influence the environment and climate change. For this purpose, an assessment of environmental impacts and circularity of 11 residential buildings (marked as A) was carried out using the life cycle assessment (LCA) methodology. LCA analysis consisted of four parts. First, the goal and scope were defined. Then, inventory analysis (LCI) entered the assessment using input data. They were needed to quantify inputs and outputs, resource and energy flows, emissions, and other leakages. Life cycle impact assessment (LCIA) quantified the pollutants into impact categories. The last part was the interpretation of the results. Investigated buildings were assessed within a moderate climate zone and "Cradle to Cradle" system boundaries over a lifetime of 60 years. The product and energy consumption phases generally contributed to the highest GHG emissions. In terms of materials, aerated concrete (up to 89.4%), concrete (20-51%) and clay brick (38-47%) caused the highest values for the GWP indicator. The end-of-life phase, including incineration, landfilling and reuse of materials, was quantified in the 0.1-74% range. Quantified global indicators are Global warming potential (GWP), Depletion potential of the stratospheric ozone layer (ODP), and Abiotic depletion potential for non-fossil resources (ADP-E). In this study, regional indicators are Acidification potential (AP), Eutrophication potential (EP) and Formation potential of tropospheric ozone (POCP). A building that achieves higher circularity is the building that makes the most use of renewable or reused resources, so it uses less raw materials. The circularity score representing materials recovery was 23% to 48%. The best circularity score of building E (48%) was due to downcycling (45.8%) and use as energy (32.1%) of applied materials. The lowest circularity score was found for building I, with a value of 23%. The most suitable building in terms of environmental impact and circularity was identified using multicriteria analysis.

Interpretation of gap states for vertical polycrystalline silicon thin film transistor

Abstract Polycrystalline silicon vertical thin film transistors (VTFTs) were fabricated employing a five-mask process. The electrical characterization was carried out to elucidate physical parameters, especially threshold voltage and effective mobility. The gap density of states (DOS) of VTFT was analyzed using the field effect method, whereas deep states and tail states were fitted by Gaussian and exponential distributions, respectively. Grain boundary barrier height was calculated using an analytic method. At the same time, the activation energy of thermionic emission through grain boundary was also introduced to show the accuracy of the calculation. In addition, lateral polycrystalline silicon thin film transistor (LTFT) was also analyzed and compared to show the grain boundary barrier heights and difference in gap states.

Virtual Power Plants: Bridging Utility and Customer Gaps to Facilitate Clean Energy Transitions

Abstract The U.S. electric grid has been challenged by extreme weather, load growth, peak demand increase, and intermittency of renewable energy resources as it continues its transition and modernization. As historic efforts such as peaker power plants reveal significant limitations in addressing the challenges, virtual power plants (VPPs) have emerged as a cost-effective alternative. VPPs are aggregations of distributed energy resources that can provide grid services like traditional power plants. This article revisits VPP analyses and finds that VPPs could 1) enhance grid reliability by managing peak demand; 2) facilitate decarbonization by promoting renewable energy resources or address integration challenges; 3) generate financial benefits for utilities and ratepayers; and 4) yield societal benefits by improving air quality and environmental inequity. This study surveys VPPs in the U.S. in terms of program design, customer segments, types of distributed energy resources, capacity, enrollment, compensation, and performance when resources are called for dispatch. Also, we test the responsiveness of a VPP in California that discharged its batteries into the grid during historic heat waves in 2022, demonstrating energy savings, emissions reductions, and financial savings for both the utility company and ratepayers. New business models may be required to take advantage of triple win situations for utilities and ratepayers.

Research on Multi-Objective Parameter Matching and Stepwise Energy Management Strategies for Hybrid Energy Storage Systems

Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an energy management strategy (EMS) based on stepwise rules optimized by Particle Swarm Optimization (PSO). The approach begins by applying a multi-objective optimization method, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fine-tune the parameters of lithium-ion batteries and ultracapacitors for an optimal balance in system performance. Additionally, an innovative stepwise-based EMS has been designed using adaptive PSO. This strategy builds a real-time control mechanism by dynamically adjusting the power distribution gradient threshold, taking into account the compensation for the state of charge (SOC). Comparative analysis across three typical operating conditions—urban, suburban, and highway—demonstrates that the stepwise-rule optimized strategy reduces the energy consumption of the HESS by 3.19%, 7.9%, and 5.37%.

Impact of Urban Block Morphology on Solar Availability in Severe Cold High-Density Cities: A Case Study of Residential Blocks in Harbin

Improving solar availability in urban blocks is vital to promoting energy conservation and emissions reduction. However, there are very few studies on the impact of block morphology on solar energy availability in high-density cities based on the particularities of climate and solar energy resources in severe cold regions at higher latitudes. This study took 434 block models generated through seven orientation conditions of 62 residential blocks in Harbin, China, as its research object. Through numerical simulations and statistical analysis, it revealed the quantitative relationship between block morphology and the availability of active photovoltaic and solar thermal collector technologies and passive thermal heating technologies. The results show that active solar technology has the highest availability in multi-story enclosed residential blocks, and passive thermal heating has the highest availability in the multi-high-level mixed-row type. The south façade of the building has the greatest active and passive solar availability. The overall active solar availability of the residential block is significantly negatively correlated with the mean building height, floor area ratio, and volume area ratio; it is significantly positively correlated with site coverage and the standard deviation of the building height. Controlling the block’s orientation between 15° south by west and 15° south by east can increase the active solar availability of the façade. This study provides a reference and evaluation basis for the sustainable planning and design of high-density cities in severely cold regions.

Assessing Biogas from Wastewater Treatment Plants for Sustainable Transportation Fuel: A Detailed Analysis of Energy Potential and Emission Reductions

This study assesses the potential for biogas production from wastewater treatment plants (WWTPs) in Adana, Türkiye, and evaluates the feasibility of transitioning a fleet of 83 municipal buses (ranging from 15 to 24 years old) to operate exclusively on biogas generated from these WWTPs. Biogas production data from three distinct WWTPs in Adana were analyzed, revealing a total annual biogas production of 5,394,346 Nm3. Replacing the diesel fleet with biogas-powered buses was found to yield a significant reduction in environmental impacts. CO2 emissions were reduced by 84%, particulate matter emissions decreased by 84.4%, and nitrogen oxides (NOX) dropped by 80%. These findings highlight the substantial potential of wastewater-derived biogas as a renewable energy source in public transportation, not only reducing reliance on non-renewable fuels but also contributing to improved air quality and energy efficiency. Transitioning to biogas-powered buses presents a promising model for sustainable public transportation, with broader implications for reducing the environmental footprint of urban transit systems.

ShipNetSim: An Open-Source Simulator for Real-Time Energy Consumption and Emission Analysis in Large-Scale Maritime Networks

The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO2 emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO2 emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO).

Innovative carbon emission monitoring in urban mobility: A case study of Beijing

China’s national goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060 necessitate a comprehensive transition of Chinese cities from high-carbon to low- and zero-carbon development. The transportation sector, a crucial area for energy conservation and emission reduction, faces significant challenges in achieving carbon neutrality due to rapid urbanization and motorization, which are projected to increase total emissions. Accurate monitoring of carbon emissions is vital for managing these emissions and achieving the “dual carbon” goals. This study provides a detailed analysis of Beijing’s integrated carbon emission monitoring system for urban mobility. It examines the innovative technical framework, which combines top-down and bottom-up approaches, enabling real-time and precise emissions tracking. The findings reveal that the system significantly improves data accuracy, supports effective governance strategies, and offers practical recommendations for scalability and adoption by other cities. In addition, the study evaluates the policy effectiveness of Beijing’s initiatives, providing evidence of substantial progress toward reducing carbon emissions. The research highlights the potential for replicability of Beijing’s system worldwide in transforming urban carbon governance. By addressing challenges such as data integration and resource limitations, the study contributes a comprehensive framework that advances the field of urban mobility emissions monitoring. This approach highlights the necessity of digital transformation in achieving sustainable urban development while setting a benchmark for other cities aiming to align with global climate goals.

The Role of Poverty and Gender in Shaping Households’ Energy Consumption Patterns in Selected European Countries

In the context of Sustainable Development Goals, declining poverty (Goal 1), achieving gender equality (Goal 5), and ensuring access to clean and affordable energy (SDG7) are still behind track, and the gaps are not yet ready to be rapidly fulfilled. As part of Goal 7, energy consumption-related targets still lack the potential to be targeted. Considering these elements, this study aims to determine the impact of poverty and gender equality on the energy consumption of households in several European countries. Using data from 2010 to 2022 and the moments’ quantile regression method combined with a pooled OLS based on Driskoll-Kraay estimators, we found statistically significant results regarding the impact of poverty and gender on household energy consumption. These findings’ significance will direct policy design towards those meaningful tools that will increase energy efficiency, address energy poverty, and ensure energy just transition, leaving no one behind. Based on the main findings, the policymakers can understand that a mix of policies is significantly more efficient. In such circumstances, social and economic inequalities will not successfully address development issues without including key environmental priorities, such as emissions mitigation and energy consumption patterns.

Prefabricated building construction in materialization phase as catalysts for hotel low-carbon transitions via hybrid computational visualization algorithms

This study examines the carbon emissions of star-rated hotels in Hangzhou, comparing the environmental impact of prefabricated construction (PC) and conventional construction (CC) methodologies. The research reveals that PC generally results in lower carbon emissions during the materialization phase, with notable variations across different hotel star levels and administrative regions. Higher-star hotels exhibit higher total emissions, primarily due to larger scale and reliance on conventional construction methods. In contrast, lower-tier hotels benefit more consistently from the adoption of prefabricated construction, leading to significant reductions in carbon emissions. Regional analysis shows that the impact of the COVID-19 pandemic on hotel turnover and carbon decoupling trends varies, with core urban areas experiencing a more pronounced decoupling effect, while suburban regions exhibited slower recovery. The findings underscore the potential for prefabricated construction to reduce carbon footprints, particularly in mid-tier and lower-tier hotels. This study contributes to the understanding of sustainable construction practices in the hotel industry and provides a foundation for future research focused on refining carbon emission assessments, incorporating real-world data, and exploring the integration of renewable energy and lifecycle emissions.

Friction-based recycling: an evaluation of friction extrusion for fabricating Ti-6Al-4 V wire fabricated from machining chip feedstock

Abstract Titanium and its alloys are used in aviation and automobile industries due to their remarkable strength to weight ratio, but machining loss commonly is high with ~ 80 wt% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4 V machining chips into solid wires which could be used as feedstock in additive manufacturing. The friction consolidation technique was first used to convert chips with varying degrees of oxygen content into solid billets for its use as feedstock material in subsequent friction extrusion. The extrudates were fabricated above the beta transition temperature, which was achieved by selecting the rotation rate and feed rate, to process the billets near 1000 °C using a tungsten-lanthana extrusion die. This work presents the first occurrence of friction extruded titanium alloy wires. The effect of friction extrusion on microstructural features, tensile properties, and texture are reported. Overall, the friction extrusion method is capable of directly recycling Ti-6Al-4 V scrap into extruded wire.

Performance Evaluation of an Innovative Photovoltaic–Thermal Flash-Tank Vapor Injection Heat Pump for Simultaneous Heating and Power Generation

Amid escalating global energy demand and heightened environmental concern, this study presents an innovative photovoltaic–thermal flash-tank vapor injection heat pump (PFVHP). This system integrates a photovoltaic–thermal (PVT) module into a conventional flash-tank vapor injection heat pump (FVHP) to realize simultaneous heating and power generation. Two distinct operation modes are designed for the PFVHP: TS-mode (two-source mode) for most solar radiation conditions and AS-mode (air-source mode) for low- or no-solar-radiation conditions. The energy, exergy, economic, and operational emission performance of the PFVHP are theoretically analyzed and compared with those of the FVHP. The findings reveal that the PFVHP can achieve a maximum cycle and system coefficient of performance (COP) at the respective optimal intermediate pressures. Exergy analysis indicates that enhancing solar radiation helps the PFVHP produce more heat exergy and electricity, but reduces the system exergy efficiency. As the evaporating temperature ranges from −20 °C to 5 °C, the cycle COP and system COP of the PFVHP are, respectively, 8.5% to 6.3% and 50.0% to 35.2% higher than the COP of the FVHP. The exergy flow comparison demonstrates that the PFVHP significantly enhances the system performance by reducing the overall exergy loss in devices excluding a PVT module, benefiting from the absorption of solar exergy by the PVT module. Economic and operational emission analyses indicate that the PFVHP offers a payback period of 9.38 years and substantially reduces the air pollution emissions compared to the FVHP.

Research on damage characterization of SCC and rock composite specimens based on CPU and GPU heterogeneous code acceleration

The interface between self-compacting concrete (SCC) and rock has significant effects on the strength, damage, and crack growth of rock-filled concrete (RFC). In this paper, the strength, failure characteristics, and damage mechanism of SCC-rock composite specimens with different interface inclination angles under uniaxial compression are studied by physical tests and numerical simulations. The 3D finite element solver is developed by using CPU-GPU heterogeneous computing, which greatly improves computing efficiency and can be applied to large-scale mesh model calculation with tens of millions of degrees of freedom. The GPU-accelerated solver and the Realistic Failure Process Analysis (RFPA) theory are utilized to simulate the uniaxial compression of 3D mesoscopic stochastic mechanical models of three-phase SCC-rock composite specimens at different interface inclination angles. The results show that the compressive strength, peak strain, and accumulated acoustic emission (AE) energy of the composites decrease first and then increase with the increase of interface inclination angle, and the composites with different interface inclination angles show different failure characteristics. The progressive failure process of the composite specimen and its mechanical behavior are reproduced by numerical simulation. The research results provide an important reference for RFC engineering design and research.

Big Data-Driven Carbon Trading and Industrial Firm Value Based on DEA and DID

Carbon trading has emerged as a critical environmental and economic mechanism for promoting energy conservation and emission reduction among firms in China. Leveraging big data from listed industrial firms participating in carbon trading, this study employs the super-efficiency SBM model and the common frontier model to evaluate firm-level carbon performance. Using carbon performance as a mediating variable, the study investigates the impact of carbon trading on firm value, considering the moderating effects of internal and external governance mechanisms. The findings reveal the following: (1) Carbon trading enhances firm value by improving carbon performance. (2) Internal governance mechanisms strengthen the positive effect of carbon trading on firm value, while government intervention weakens this effect. (3) The value-enhancing effect of carbon trading is more pronounced for firms in China’s central and western regions. (4) Among industrial firms, carbon trading has the strongest impact on the value of manufacturing firms. These results provide valuable insights for policymakers and firms aiming to align environmental and economic objectives through carbon-trading mechanisms.

Optimising energy use and environmental impacts in asphalt production: a life cycle approach

ABSTRACT This study provides a comprehensive evaluation of energy dynamics and environmental impacts in asphalt mixture production, addressing key factors that influence sustainability in the industry. The research focuses on assessing the effects of production temperature, aggregate moisture content, and using alternative materials, aiming to optimise operations and reduce environmental footprints. An expanded thermodynamic energy balance equation was used to predict thermal energy consumption during production, representing an innovative approach to improving asphalt manufacturing efficiency. Through life cycle assessment (LCA), the study evaluates the environmental performance of warm mix asphalt (WMA) and hot mix asphalt (HMA) across key impact categories. The findings confirm that WMA is a more sustainable alternative to HMA, showing reductions in GWP by 15% to 30% and lower fossil resource depletion. Sensitivity analysis demonstrated that temperature and moisture content variations significantly affect energy consumption and emissions in the production phase. Dry aggregates and WMA technology were shown to reduce energy consumption by up to 90% compared to wet aggregates in HMA mixtures. This research contributes to developing more sustainable asphalt production practices, highlighting the importance of optimising variables. The expanded energy balance equation offers a predictive tool for improving operational efficiency and promoting sustainability in asphalt manufacturing.

Driving Sustainability: Analyzing Eco-Driving Efficiency Across Urban and Interurban Roads with Electric and Combustion Vehicles

Eco-driving is a key strategy for reducing energy consumption and emissions in electric vehicles (EVs) and internal combustion engine (ICE) vehicles. However, research gaps remain regarding its effectiveness across different driving environments, vehicle types, transmission systems, and contexts. This research evaluates eco-driving efficiency in urban and interurban settings, comparing small (Caceres) and large (Madrid) cities and assessing EVs ICE with direct, manual, and automatic transmissions. The authors conducted a large-scale driving experiment in Spain, with over 500 test runs across different road types. Results in the large city show that eco-driving reduces energy consumption by 30.4% in EVs on urban roads, benefiting from regenerative braking, compared to 10.75% in manual ICE vehicles. Automatic ICE vehicles also performed well, with 29.55% savings in local streets. In interurban settings, manual ICE vehicles achieved the highest savings (20.31%), while EVs showed more minor improvements (11.79%) due to already optimized efficiency at steady speeds. The small city showed higher savings due to smoother traffic flow, while single-speed transmissions in EVs enhanced efficiency across conditions. These findings provide valuable insights for optimizing eco-driving strategies and vehicle design. Future research should explore AI-driven eco-driving applications and real-time optimization to improve sustainable mobility.

Machine learning model for predicting long-term energy consumption in buildings

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation.Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance.

Hofmeister Effect-Enhanced, Nanoparticle-Shielded, Thermally Stable Hydrogels for Anti-UV, Fast-Response, and All-Day-Modulated Smart Windows.

Thermochromic smart windows offer energy-saving potential through temperature-responsive optical transmittance adjustments, yet face challenges in achieving anti-UV radiation, fast response, and high-temperature stability characteristics for long-term use. Herein, the rational design of Hofmeister effect-enhanced, nanoparticle-shielded composite hydrogels, composed of hydroxypropylmethylcellulose (HPMC), poly(N,N-dimethylacrylamide) (PDMAA), sodium sulfate, and polydopamine nanoparticles, for anti-UV, fast-response, and all-day-modulated smart windows is reported. Specifically, a three-dimensional network of PDMAA is created as the supporting skeleton, markedly enhancing the thermal stability of pristine HPMC hydrogels. Sodium sulfate induces a Hofmeister effect, lowering the lower critical solution temperature to 32 °C while accelerating phase transition rates fivefold (30 s vs. 150 s). Intriguingly, small-sized polydopamine nanoparticles simultaneously enable high luminous transmittance of 66.9% and outstanding anti-UV capability. Additionally, the smart window showcases a high solar modulation (51.2%) and maintains a 10.2 °C temperature reduction versus a glass window during all-day modulation applications. The design strategy is effective, opening up new avenues for manufacturing fast-response and durable thermochromic smart windows for energy savings and emission reduction.

Asymmetric tail risk spillovers between carbon emission allowance and energy markets: evidence from China

ABSTRACT As a critical emerging market for carbon neutrality, the carbon emission allowance market’s risk linkage to energy markets is crucial. By employing the multivariate-quantile conditional autoregressive model, the Granger causality risk test and QVAR-DY, we find significant bilateral downside risk transmission between coal and carbon markets in the short term, while the carbon market exhibits persistent risk spillover effects on clean energy and natural gas markets across both short and long horizons. Analysis of upside risk dynamics demonstrates that climate policy implementations and associated market events generate spillover effects propagate through the carbon market to energy markets. China’s National ETS has strengthened the correlation between the carbon and energy markets. These findings highlight the carbon market’s crucial role in curbing fossil fuel consumption and controlling climate risk. The results of this study are valuable for enterprises, investors, and regulators in enhancing carbon risk management, promoting green investment, and supporting sustainable development.