Search Results (5,814)

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

Retrofitting existing buildings has become a critical strategy for reducing energy consumption, improving thermal comfort, and achieving carbon reduction targets in the built environment. Among retrofit measures, wall insulation plays a pivotal role in minimizing heat loss and enhancing building energy efficiency. Conventional insulation materials, although effective, are often associated with high embodied energy, limited recyclability, and environmental concerns. Consequently, bio-based composite materials derived from natural fibers, agricultural residues, and renewable binders have emerged as promising sustainable alternatives. However, the moisture sensitivity of lignocellulosic materials remains a major challenge that can compromise thermal performance, durability, and long-term service life. This review provides a comprehensive and critical assessment of bio-based hydrophobic composite panels for wall insulation in retrofit applications. Unlike previous reviews that have primarily examined bio-based insulation materials, natural-fiber composites, or hydrophobic modifications separately, this study integrates these interconnected research domains within a unified framework. The review systematically examines raw material selection, composite panel manufacturing processes, hydrophobic surface-engineering strategies, thermal and moisture-related performance, durability characteristics, retrofit implementation approaches, and sustainability considerations. The analysis demonstrates that hydrophobic modification significantly reduces moisture uptake, enhances dimensional stability, and preserves thermal-insulation performance under varying environmental conditions. Natural-fiber-based composites, including hemp, flax, jute, bamboo, coconut fiber, and agricultural residues, exhibit competitive thermal conductivity (λ) values while offering reduced environmental impacts compared with conventional insulation materials. Furthermore, the integration of advanced hydrophobic treatments improves resistance to water penetration, biological degradation, and freeze–thaw damage, thereby increasing the long-term reliability of retrofit insulation systems. Full article

In processes related to the treatment of mineral materials, the crushing stage determines the ability to obtain the required particle-size fraction. At the same time, it is an exceptionally energy-intensive step (accounting for about 5% of global electricity consumption) and one that generates significant environmental impacts, particularly in the form of high noise levels and considerable dust emissions. This study focuses on acoustic issues associated with the operation of crushers equipped with materials of varying hardness. Noise level measurements were carried out and then compared with the machines’ operational parameters, such as reduction ratio, throughput, energy consumption, and grain-size distribution. The results indicate that the properties of the processed material have a significant influence on noise emission during the crushing process. The study included various types of materials, such as pebble, basalt, and granite (feed size 16–22 mm), as well as lower-strength materials, including aerated concrete, recycled concrete, and ceramic materials (average particle size of approximately 50 mm), enabling a comparative analysis under controlled operating conditions. The measured noise levels ranged from front position 105.3 dB and side position 105.2 dB, depending on the material type, with the highest values observed for [hard material, e.g., recycled concrete and basalt] and the lowest for [weak material, e.g., aerated concrete]. The differences between extreme cases reached up to the top position 107.6 dB, indicating a strong relationship between material properties and acoustic emission. These findings highlight the importance of material selection in crushing processes and provide a useful reference for reducing noise impact and improving the environmental performance of industrial aggregate production. Full article

The study presents the results of research aimed at developing digital models for determining the operating parameters of railway power supply systems equipped with distributed generation plants based on renewable energy sources (RESs). RESs can be used in railway transport to increase the reliability of power supply to facilities located in areas with insufficiently developed power grids. This primarily applies to consumers, for whom a power failure can lead to significant damage, accidents, and a threat to human life. RES can serve as independent power sources for special-group consumers and can increase energy conversion efficiency. Furthermore, large-scale implementation of renewable energy sources can significantly reduce energy supply costs and improve power quality. The study employs phase-coordinate modeling, which is characterized by the following features: a systems approach, which implies determining operating conditions while considering the properties and characteristics of complex traction and supply networks; versatility, which enables modeling of power supply systems of various structures and designs; and comprehensiveness, which involves calculating normal, emergency, and special operating parameters—crucial for scenarios such as ice melting on catenary wires. The modeling results obtained using the Fazonord AC-DC software (ver. 5.3.5.2) show that RES-based distributed generation plants provide a variety of beneficial effects: reduction in electricity consumption from power system networks; decrease in voltage unbalance and harmonic distortion on the busbars of regional windings of traction substations; and stabilization of voltage levels on current collectors of electric locomotives. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

This study investigates an integrated ecological strategy to reduce electricity consumption in semi-collective housing located in the hot–arid climate of Biskra, Algeria, a region with high solar potential. The research combines photovoltaic (PV) electricity generation with passive thermal insulation using a locally sourced bio-based material derived from date palm fibers. The case study includes 104 dwellings within a residential complex of 350 units. Results show that monocrystalline PV panels (350 W) can produce approximately 479 kWh/panel/year. To meet the total annual electricity demand (504,712 kWh), around 1052 panels are required, corresponding to 1714 m² (13.8%) of the available building envelope. This installation area demonstrates the significant photovoltaic potential of the residential complex under hot–arid climatic conditions. Thermal analysis indicates that integrating a 5 cm palm fiber insulation layer increases thermal resistance from 2.06 to 2.62 m²·°C/W and reduces heat flux from 2.18 to 1.72 W/m². This improvement decreases conductive heat transfer through the envelope by approximately 21%, while numerical simulations indicate indoor temperature reductions of 4–8°C during summer conditions. These findings demonstrate that combining PV systems with bio-based insulation significantly enhances energy efficiency and thermal comfort in residential buildings under desert climatic conditions. Full article

The transition toward circular economy practices and CO₂ reduction goals is driving the development of new sound absorption technologies. Traditional absorbers made from mineral wool or foams provide broadband absorption; however, their production is associated with intensive energy consumption and non-renewable resources. This is why the focus has been shifting from the mere substitution of materials to integrated solutions that combine sustainability with structure. This paper reviews recent innovations in sustainable absorbers based on bio-based and recycled materials. The acoustic performance of porous materials depends on such factors such as pore structure, airflow resistivity and geometric parameters such as thickness, multi-layer structure and resonances. At the same time, additive manufacturing (AM) allows creating geometry-controlled absorbers providing advanced acoustic properties. Despite many sustainable absorbers demonstrating sufficient sound absorption properties at medium and high frequencies, their use at low frequencies remains challenging. Additionally, concerns regarding durability, flame retardance, and environmental consistency continue to limit their broader application. Yet, hybrid, multi-material strategies, particularly those combining geopolymer matrices with bio-based or recycled fillers, are identified as a promising route to address these limitations. This review outlines current trends and highlights key challenges and future directions in the design of sustainable sound-absorbing systems. Full article

Hybrid tractors are a promising solution for reducing fuel consumption and emissions in agricultural machinery. However, their low-speed, high-torque operation with frequent load fluctuations demands an energy management strategy (EMS) that is both real-time capable and highly adaptive. This study focuses on a coaxial parallel hybrid electric tractor, developing a forward simulation model that integrates longitudinal vehicle dynamics, engine, motor, battery, and transmission systems. An improved equivalent fuel consumption minimization strategy (ECMS) with state-of-charge feedback correction, termed F-ECMS, is proposed. It dynamically adjusts the equivalence factor based on real-time battery SOC to approach optimal fuel economy while sustaining charge. Dynamic programming (DP) is used to establish a global benchmark. Simulations under a typical plowing cycle show that over 14,400 s, the F-ECMS maintains SOC (0.5964) close to the DP reference (0.6000), while achieving a 1.51% reduction in equivalent fuel consumption compared to a rule-based strategy. The results demonstrate that the proposed F-ECMS offers an effective balance between real-time performance and fuel economy, showing strong potential for practical implementation in hybrid agricultural vehicles. Full article

Energy-saving renovations of existing central air-conditioning systems have increasingly received recognition. Theoretical energy consumption model (TECM) is commonly used to estimate the baseline energy consumption of water pumps for comprehensive benefit assessments. Nevertheless, the calculated results often exhibit significant discrepancies compared to the actual values. This study proposes a novel enhanced theoretical energy consumption model (EN-TECM) for improving the calculation accuracy of pump energy consumption, focusing on two dimensions: the number and frequency of operating pumps. Then, 576 sets of pump operation data from 29 actual central air-conditioning systems are used as samples to determine the model key parameter. Further, three actual engineering cases are employed to evaluate the application performance of the EN-TECM. The model regression results show a significant reduction in calculation errors across all operating conditions, with the mean relative errors within ±1%. The analysis results of all engineering cases demonstrate that the relative errors of the cumulative total energy consumption decrease by more than 60% compared to the TECM. This research offers a practical and effective method for accurately calculating the energy consumption of chilled water pumps in field-operated central air-conditioning water systems, which will facilitate the advancement of energy-saving retrofitting projects for existing buildings. Full article

Biomimetic design has been used to reduce the high operating resistance of agricultural soil-engaging components, thereby lowering energy consumption. However, most existing contour-based structural biomimetic designs rely on a single or a few biological samples, making the resulting designs susceptible to individual variation and randomness in sample selection. To address this issue, this study used the abdomen of antlion larvae as a biological prototype. Abdominal contours of 85 antlion larvae were extracted from the front, top, and side views, and elliptic Fourier descriptors (EFDs) were used for contour normalization, averaging, and reconstruction to obtain population mean contours. Seven biomimetic wedge specimens were designed based on the population mean contours, and vertical penetration and horizontal cutting tests were conducted in two different media. The results showed that in the vertical penetration tests, the B-FT specimen, which integrated contour features from the front and top views, exhibited the best drag-reduction performance. Its average penetration resistance decreased by 44.26% and 32.81% in quartz sand and loam soil, respectively. In the horizontal cutting tests, the B-FTS specimen, which integrated contour features from all three views, showed the lowest average cutting resistance, with reductions of 17.62% and 36.47%, respectively. The FTS contour features were further applied to the biomimetic design of a subsoiler tine and validated by discrete element method (DEM) simulation and soil bin tests. Compared with the standard subsoiler tine, the biomimetic subsoiler tine reduced draft force by 11.57% in the simulation and by 12.61% in the soil bin test. These results demonstrate the drag-reduction effectiveness of population mean contours and provide a statistically grounded geometric reference for the biomimetic low-resistance design of agricultural soil-engaging components. Full article

This study proposes and validates a structured methodology based on Design for Six Sigma (DFSS) for sustainable product design, addressing the lack of standardization in the integration of design tools and the need to simultaneously consider qualitative, quantitative, and sustainability-related variables. The methodology integrates Voice of the Customer (VOC), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), computer-aided design and engineering (CAD/CAE), and Design of Experiments (DOE) within a ten-stage framework combining the stages from DMADV (Define, Measure, Analyze, Design, Verify) and IDOV (Identify, Design, Optimize, Validate) approaches. The proposed method was applied to the design of a structural concrete block, considering performance variables such as weight, factor of safety, displacement, energy consumption, and carbon emissions. The results show that the integration of QFD enabled prioritization of customer requirements, while DOE and regression models identified significant factors and interactions. Multi-response optimization using desirability functions achieved a balanced solution, improving structural performance and sustainability indicators. In particular, a significant reduction in carbon emissions was achieved. Validation through simulation confirmed the consistency between predicted and observed results. The findings demonstrate that the proposed methodology provides a systematic and replicable approach for product design, improving decision-making and supporting the development of more sustainable products. Full article

In the new development stage of China’s green and low-carbon transition, agricultural industry agglomeration serves as a key catalyst for sustainable agricultural practices. Its effects on agricultural carbon reduction and energy conservation urgently need investigation. This research uses panel data from 31 Chinese provinces spanning 2005 to 2021 to investigate the nonlinear effects of agricultural industry agglomeration on agricultural carbon emissions and energy consumption, employing econometric models such as the two-way fixed effects model, mediation model, and moderation model. The findings indicate that (1) there’s a clear inverted U-shaped pattern linking agricultural industry agglomeration to both carbon emissions and energy consumption in agriculture; (2) agricultural scale effects and socialized services are key mechanisms; (3) marketization and environmental regulation positively moderate this relationship; and (4) the carbon reduction and energy-saving effects are more pronounced in regions with higher agricultural modernization levels, higher urbanization rates, and plain areas. This finding contributes to optimizing the path of agricultural industry agglomeration and facilitates the synergy of carbon reduction and energy conservation in such agglomeration. Full article

Achieving sustainability in the manufacturing sector calls for systemic reductions in energy consumption and carbon emissions without compromising productivity. In the global energy consumption landscape, the manufacturing sector accounts for a significant proportion and is a major source of carbon emissions, with manufacturing systems and HVAC (Heating, Ventilation, and Air Conditioning) systems being the principal energy consumers. Existing research typically optimizes these two systems independently, neglecting their dynamic coupling; production scheduling determines equipment power and heat dissipation, which alters building thermal loads and consequently affects HVAC energy consumption. To address this problem and advance sustainable manufacturing practices, this study proposes an energy-aware scheduling framework integrating manufacturing and HVAC control. A WOA-XGBoost energy consumption prediction model is constructed, employing the Whale Optimization Algorithm to tune XGBoost hyperparameters, achieving a prediction accuracy of R² = 0.937 on the Shanghai typical meteorological year dataset. The HVAC decision variables are defined as five operational control variables—supply air flow rate, fan total pressure, ERV sensible/latent heat recovery effectiveness, and ventilation air flow rate—ensuring the physical realizability of scheduling solutions. An integrated scheduling-and-control model incorporating production capacity constraints and electricity demand response is then formulated and solved using a hybrid Particle Swarm Optimization algorithm. Validation on a five-machine, four-buffer flow shop demonstrates that the proposed framework reduces total electricity cost by 8.85% and total energy consumption by 14.88% in summer compared with a physics-based coupling baseline, with all metrics exhibiting coefficients of variation below 4% across ten independent runs. These results demonstrate that the proposed data-driven framework provides a practical and scalable pathway toward sustainable manufacturing by jointly reducing energy use and associated carbon emissions while maintaining full production throughput. Full article

To effectively reduce fuel consumption and emissions in personal transport, it is essential to understand how energy transfer inefficiencies arise under real-world driving conditions. This study investigates the behavioral and spatial determinants of energy transfer inefficiency in personal diesel vehicles using high-resolution vehicle telematics data. The research proposes a composite Energy Inefficiency Index (EII) derived from real-world indicators of driving behavior, including acceleration, braking, idling, speed variability, and trip structure. These indicators are normalized and weighted using principal component analysis to quantify inefficiency at trip and spatial levels. Geospatial analysis, including Global Moran’s I and heatmap visualization, is employed to identify spatial clustering of energy inefficiency across urban and extra-urban environments. The results reveal a moderate average level of energy inefficiency across the analyzed vehicle fleet, with braking frequency, acceleration frequency, trip duration, and idling time emerging as the primary behavioral drivers of inefficient energy transfer. A statistically significant positive spatial autocorrelation indicates pronounced clustering of inefficiency in dense urban areas characterized by congestion and stop–start traffic dynamics. Furthermore, this study evaluates potential fuel, cost, and CO₂ emission reductions achievable through improved driving behavior and compares these gains with those associated with vehicle electrification. The findings demonstrate that targeted behavioral interventions—such as eco-driving and idling reduction—can yield substantial efficiency improvements and emission reductions, complementing the benefits of electrification. Overall, this research provides a data-driven framework for managing energy transfer inefficiency in personal diesel vehicles by integrating behavioral analysis, spatial assessment, and telematics-based monitoring, offering practical insights for policymakers, transport planners, and vehicle technology developers. Full article