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Found 6273 publications
Evaluation of sustainability reporting of the food industry in Hungary from an EU taxonomy perspective
Publication Name: Quality and Quantity
Publication Date: 2024-10-01
Volume: 58
Issue: 5
Page Range: 4479-4504
Description:
Compliance with green requirements is becoming increasingly important in assessing the performance of companies. The new CSRD legislation requires a wider range of companies to produce sustainability reports and their content is influenced by the EU's taxonomy regulation setting out the framework for sustainable finance. The disclosure of information affects the perception of companies' sustainability performance, which will affect their access to financial resources and development opportunities. The main question is, both in theory and in practice, how companies can comply with the legislation in the future. It is essential for the competitiveness of Hungary's food industry to keep pace with future environmental sustainability requirements, so we examined the sustainability reporting practices of the sector's key companies in terms of their contribution to the environmental objectives set out in the taxonomy regulation. The research fits well with the EU's overall green transition regulatory procedure and our study is gap-filling at macro-regional and sectoral levels. The sustainability reports were assessed by content analysis using a scoring method. The results show that the sustainability reporting practices of food processing companies in Hungary differ significantly. Furthermore, greater emphasis must be placed on reporting and the credibility of the reports to meet future expectations. Foreign-owned companies and companies with more than 500 employees attribute greater importance to reporting. In the food processing sector, the disclosure of information and data under the taxonomy objectives of mitigation of climate change, sustainable use of water and marine resources, and transition to a circular economy was most common.
Open Access: Yes
Effects of Various Herbicide Types and Doses, Tillage Systems, and Nitrogen Rates on CO2 Emissions from Agricultural Land: A Literature Review
Publication Name: Agriculture Switzerland
Publication Date: 2024-10-01
Volume: 14
Issue: 10
Page Range: Unknown
Description:
Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO2 emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO2 emissions under different environmental conditions are poorly understood. This review explores how various agricultural practices and inputs affect CO2 emissions and addresses the impact of pest-management strategies, tillage systems, and nitrogen fertiliser usage on CO2 emissions using multiple databases. Key findings indicate that both increased and decreased tendencies in greenhouse gas (GHG) emissions were observed, depending on the herbicide type, dose, soil properties, and application methods. Several studies reported a positive correlation between CO2 emissions and increased agricultural production. Combining herbicides with other methods effectively controls emissions with minimal chemical inputs. Conservation practices like no-tillage were more effective than conventional tillage in mitigating carbon emissions. Integrated pest management, conservation tillage, and nitrogen fertiliser rate optimisation were shown to reduce herbicide use and soil greenhouse gas emissions. Fertilisers are similarly important; depending on the dosage, they may support yield or harm the soil. Fertiliser benefits are contingent on appropriate management practices for specific soil and field conditions. This review highlights the significance of adaptable management strategies that consider local environmental conditions and can guide future studies and inform policies to promote sustainable agriculture practices worldwide.
Open Access: Yes
Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches
Publication Name: Batteries
Publication Date: 2024-10-01
Volume: 10
Issue: 10
Page Range: Unknown
Description:
In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.
Open Access: Yes
Would really long-only climate-transition strategies in commodities bring lower market risk for sustainable markets in the long run? The Islamic sustainable market versus the global sustainability leaders
Publication Name: Economic Analysis and Policy
Publication Date: 2024-06-01
Volume: 82
Issue: Unknown
Page Range: 1271-1295
Description:
By allocating investments towards commodities that align with climate-transition goals, environmentally conscious commodity investment strategies serve to promote and support sustainable markets, channeling capital towards sectors that prioritize environmental sustainability. Through the application of a quantile causality test, which examines the relationship between commodity-based strategies with a climate-transition focus and eco-friendly markets, over the period spanning from May 1, 2013, to May 25, 2023, our findings reveal a bi-directional causality relationship between different themes of sustainable markets and long-only climate-transition strategies in the commodity market across various market conditions. Furthermore, employing a quantile time-frequency connectedness approach allows us to discern that long-only climate-transition strategies in the commodity market exhibit lower long-run total connectedness with responsible and conscious markets compared to the short term. Consequently, these results suggest that transition-oriented strategies for commodities in a climate-conscious world not only mitigate market risk for regenerative markets in the long run but also indicate that different types of global sustainability leaders demonstrate a stronger connectedness with climate-transition strategies in commodities when compared to the Islamic sustainable market across a majority of quantiles and time horizons. In light of these findings, policymakers are urged to prioritize the long-term dimensions of climate-transition strategies in commodity markets by implementing new emission standards and environmental benchmarks. Additionally, the design and implementation of similar long-only climate-transition strategies in other markets would further enhance the long-term effectiveness of climate-conscious markets and foster stronger connections with responsible markets. our study underscores the significance of integrating climate-transition strategies into commodity markets and highlights their role in promoting sustainable and environmentally conscious investment practices. By directing investments towards climate-aligned commodities, policymakers and market participants can contribute to the long-term sustainability of global markets while fostering stronger connections between sustainable markets and climate-transition strategies in commodities.
Open Access: Yes
Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil
Publication Name: Applied Sciences Switzerland
Publication Date: 2024-06-01
Volume: 14
Issue: 11
Page Range: Unknown
Description:
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.
Open Access: Yes
DOI: 10.3390/app14114819
Laboratory and Numerical Investigation of Pre-Tensioned Reinforced Concrete Railway Sleepers Combined with Plastic Fiber Reinforcement
Miklós Kuczmann
Szabolcs Fischer
I. Fekete
Zoltán Major
Richárd Nagy
Szabolcs Szalai
Mykola Sysyn
Attila Németh
Dmytro Kurhan
Majid Movahedi Rad
Sarah Khaleel Ibrahim
Dániel Harrach
Szabolcs Kocsis Szurke
Vivien Jóvér
Hanna Csótár
Gusztáv Baranyai
Bálint Molnár
Klaudia Madarász
András Pollák
Bence Hermán
László Gáspár
Publication Name: Polymers
Publication Date: 2024-06-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
This research investigates the application of plastic fiber reinforcement in pre-tensioned reinforced concrete railway sleepers, conducting an in-depth examination in both experimental and computational aspects. Utilizing 3-point bending tests and the GOM ARAMIS system for Digital Image Correlation, this study meticulously evaluates the structural responses and crack development in conventional and plastic fiber-reinforced sleepers under varying bending moments. Complementing these tests, the investigation employs ABAQUS’ advanced finite element modeling to enhance the analysis, ensuring precise calibration and validation of the numerical models. This dual approach comprehensively explains the mechanical behavior differences and stresses within the examined structures. The incorporation of plastic fibers not only demonstrates a significant improvement in mechanical strength and crack resistance but paves the way for advancements in railway sleeper technology. By shedding light on the enhanced durability and performance of reinforced concrete structures, this study makes a significant contribution to civil engineering materials science, highlighting the potential for innovative material applications in the construction industry.
Open Access: Yes
Challenges in Mapping Soil Variability Using Apparent Soil Electrical Conductivity under Heterogeneous Topographic Conditions
Publication Name: Agronomy
Publication Date: 2024-06-01
Volume: 14
Issue: 6
Page Range: Unknown
Description:
Site-specific management requires the identification of treatment areas based on homogeneous characteristics. This study aimed to determine whether soil mapping based on apparent soil electrical conductivity (ECa ) is suitable for mapping soil properties of fields with topographic heterogeneity. Research was conducted on two neighbouring fields in Fejér county, Hungary, with contrasting topographic heterogeneity. To characterise the spatial variability of soil attributes, ECa was measured and supplemented by obtaining soil samples and performing soil profile analysis. The relationship between ECa and soil physical and chemical properties was analysed using correlation, principal component, and regression analyses. The research revealed that the quality and strength of the relationship between ECa and soil remarkably differed in the two studied fields. In homogeneous topographic conditions, ECa was weakly correlated with elevation as determined by soil physical texture and nutrient content in a strong (R2 = 0.72) linear model. On the other hand, ECa was significantly determined by elevation in heterogeneous topographic conditions in a moderate (R2 = 0.47) linear model. Consequently, ECa -based soil mapping can only be used to characterise the soil, thus delineating management zones under homogeneous topographic conditions.
Open Access: Yes
Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp
S. Janssen
J. L. Pérez-Camanyo
László Környei
J. L. Santiago
V. Rodrigues
G. Sousa Santos
P. Thunis
E. Rivas
C. Cuvelier
I. Sakellaris
M. G. Villani
Akos Kovacs
Bence Liszkai
F. Martín
N. Reiminger
X. Jurado
D. Barbero
J. L. Pérez-Camanyo
R. Jackson
Ákos Kovács
J. Sousa
J. Bartzis
R. San José
G. Tinarelli
J. Stocker
Zoltán Horváth
F. Russo
Publication Name: Science of the Total Environment
Publication Date: 2024-05-15
Volume: 925
Issue: Unknown
Page Range: Unknown
Description:
In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.
Open Access: Yes
Frequency- and Temperature-Dependent Uncertainties in Hysteresis Measurements of a 3D-Printed FeSi wt6.5% Material
Publication Name: Sensors
Publication Date: 2024-05-01
Volume: 24
Issue: 9
Page Range: Unknown
Description:
Additive manufacturing of soft magnetic materials is a promising technology for creating topologically optimized electrical machines. High-performance electrical machines can be made from high-silicon-content FeSi alloys. Fe-6.5wt%Si material has exceptional magnetic properties; however, manufacturing this steel with the classical cold rolling methodology is not possible due to the brittleness of this material. Laser powder bed fusion technology (L-PBF) offers a solution to this problem. Finding the optimal printing parameters is a challenging task. Nevertheless, it is crucial to resolve the brittleness of the created materials so they can be used in commercial applications. The temperature dependence of magnetic hysteresis properties of Fe-6.5wt%Si materials is presented in this paper. The magnetic hysteresis properties were examined from 20 °C to 120 °C. The hysteresis measurements were made by a precision current generator–based hysteresis measurement tool, which uses fast Fourier transformation–based filtering techniques to increase the accuracy of the measurements. The details of the applied scalar hysteresis sensor and the measurement uncertainties were discussed first in the paper; then, three characteristic points of the static hysteresis curve of the ten L-PBF-manufactured identical toroidal cores were investigated and compared at different temperatures. These measurements show that, despite the volumetric ratio of the porosities being below 0.5%, the mean crack length in the samples is not significant for the examined samples. These small defects can cause a significant 5% decrement in some characteristic values of the examined hysteresis curve.
Open Access: Yes
DOI: 10.3390/s24092738
