Search Results (960)

The Mapleton Falls National Park transmission line corridor in Queensland, Australia, has received a number of vegetation management treatments over the last decade to maintain and protect the infrastructure and to ensure continuous electricity supply. Recent treatments have included ‘mega-mulching’ (mechanical mastication of vegetation to a mulch layer) in 2020 and targeted herbicide treatment of woody vegetation, with the aim of reducing vegetation height by encouraging a native herbaceous groundcover beneath the transmission lines. We measured vegetation structure (cover and height) and composition (species presence in 15 × 2 m plots), at 12 transects, 90 m in length on the transmission line corridor, to determine if management goals were being achieved and to determine how the vegetation and fire hazard (based on the overall fuel hazard assessment method) varied among the treated corridor, the forest edge environment, and the natural forest. The results showed that vegetation structure and composition in the treated zones had been modified to a state where herbaceous plant species were dominant; there was a significantly (p < 0.05) higher native grass cover and cover of herbs, sedges, and ferns in the treated zones, and a lower cover of trees and tall woody plants (>1 m in height) in these areas. For example, mean native grass cover and the cover of herbs and sedges in the treated areas was 10.2 and 2.8 times higher, respectively, than in the natural forest. The changes in the vegetation structure (particularly removal of tall woody vegetation) resulted in a lower overall fuel hazard in the treated zones, relative to the edge zones and natural forest. The overall fuel hazard was classified as ‘high’ in 83% of the transects in the treated areas, but it was classified as ‘extreme’ in 75% of the transects in the adjacent forest zone. Importantly, there were few introduced species recorded. The results suggest that fuel management has been successful in reducing wildfire risk in the transmission corridor. Temporal monitoring is recommended to determine the frequency of ongoing fuel management. Full article

Tropical forests continue to experience high levels of habitat loss and degradation, with wildfires becoming a frequent component of human-modified landscapes. Here we investigate the response of palm species to the conversion of old-growth forests to successional mosaics, including forest patches burned during wildfires. Palms (≥50 cm height) were recorded once in 2023–2024, across four habitat classes: terra firme old-growth stands, regenerating forest stands associated with slash-and-burn agriculture, old-growth stands burned once and twice, and active cassava fields, in the Tapajós-Arapiuns Extractive Reserve, in the eastern Brazilian Amazon. The flammability of palm leaf litter and forest litter were also examined to assess the potential connections between palm proliferation and wildfires. A total of 10 palm species were recorded in this social forest (including slash-and-burn agriculture and resulting successional mosaics), with positive, negative, and neutral responses to land use. Species richness did not differ among forest habitats, but absolute palm abundance was greatest in disturbed habitats. Only Attalea spectabilis Mart. (curuá) exhibited increased relative abundance across disturbed habitats, including active cassava field. Attalea spectabilis accounted for almost 43% of all stems in the old-growth forest, 89% in regenerating forests, 90% in burned forests, and 79% in crop fields. Disturbed habitats supported a five-to-ten-fold increment in curuá leaves as a measure of habitat flammability. Although curuá litter exhibited lower flame temperature and height, its lower carbon and higher volatile content is expected to be more sensitive to fire ignition and promote the spread of wildfires. The conversion of old-growth forests into social forests promotes the establishment of palm-dominated forests, increasing the potential for a forest transition further fueled by wildfires, with effects on forest resilience and social reproduction still to be understood. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

In this study, we comprehensively analyzed material consumption (fuel, hydraulic oil, lubricants, and AdBlue fluid) and estimated carbon dioxide emissions during logging operations. This study was carried out in the northeastern part of Poland. Four harvesters and four forwarders representing two manufacturers (John Deere-Deere & Co., Moline, USA, and Komatsu Forest AB, Umeå, Sweden) were analyzed to compare their operational efficiency and constructional influences on overall operating costs. Due to differences in engine emission standards, approximate greenhouse gas emissions were estimated. The results indicate that harvesters equipped with Stage V engines have lower fuel consumption, while large forwarders use more consumables than small ones per hour and cubic meter of harvested and extracted timber. A strong positive correlation was observed between total machine time and fuel consumption (r = 0.81), as well as between machine time and total volume of timber harvested (r = 0.72). Older and larger machines showed about 40% higher combustion per unit of wood processed. Newer machines meeting higher emission standards (Stage V) generally achieved lower CO₂ and other GHG emissions compared to older models. Machines with Stage V engines emitted about 2.07 kg CO₂ per processing of 1 m³ of wood, while machines with older engine types emitted as much as 4.35 kg CO₂ per 1 m³—roughly half as much. These differences are even more pronounced in the context of nitrogen oxide (NO_x) emissions: the estimated NO_x emissions for the older engine types were as high as ~85 g per m³, while those for Stage V engines were only about 5 g per m³ of harvested wood. Continuing the study would need to expand the number of machines analyzed, as well as acquire more detailed performance data on individual operators. A tool that could make this possible would be fleet monitoring services offered by the manufacturers of the surveyed harvesters and forwards, such as Smart Forestry or Timber Manager. Full article

Rural areas around the world are increasingly dealing with energy and environmental challenges. These challenges are particularly acute in developing countries, where persistent reliance on traditional energy sources—such as wood fuel—intersects with concerns about forest conservation and energy sustainability. While wood fuel use is often portrayed as unsustainable, it is important to acknowledge that much of it remains ecologically viable and socially embedded. This study explores the role of rural entrepreneurs in shaping low-carbon transitions at the intersection of household energy practices and environmental stewardship. Fieldwork was carried out in four rural Zambian communities in 2016 and complemented by 2024 follow-up reports. It examines the connections between household energy choices, greenhouse gas emissions, and forest resource dynamics. Findings reveal that over 60% of rural households rely on charcoal for cooking, with associated emissions estimated between 80 and 150 kg CO₂ per household per month. Although this is significantly lower than the average per capita carbon footprint in industrialized countries, such emissions are primarily biogenic in nature. While rural communities contribute minimally to global climate change, their practices have significant local environmental consequences. This study draws attention to the structural constraints as well as emerging opportunities within Zambia’s rural energy economy. It positions rural entrepreneurs not merely as policy recipients but as active agents of innovation, environmental monitoring, and participatory resource governance. A model is proposed to support sustainable rural energy transitions by aligning forest management with context-sensitive emissions strategies. Full article

U.S. gasoline and diesel prices are often volatile, driven by geopolitical risks and disruptions in the fossil fuel market. Forest biomass fuels, particularly renewable diesel derived from logging residues, offer a low-carbon alternative with the potential to stabilize fuel prices. This study evaluates whether biomass can moderate fuel price volatility using ANOVA, Tukey post hoc tests, and quadratic regression based on monthly data for biomass production, inventories, and retail fuel prices. Findings reveal the existence of a significant nonlinear relationship between forest biomass inventory levels and fossil fuel prices. Average gasoline prices peaked in the medium-inventory group (M = 0.837) and dropped in the high-inventory group (M = 0.684). Diesel prices followed a similar pattern, with the highest values in the medium-inventory group (M = 0.963) and the lowest in the high-inventory group (M = 0.759). One-way ANOVA results were statistically significant for both gasoline (F(2, 99) = 7.39, p = 0.001) and diesel (F(2, 99) = 7.22, p = 0.0012). Tukey tests confirmed that diesel prices fell significantly from both medium to high and low to high-inventory levels. This result remains robust when using the biomass index level and the biomass production level. These results indicate a threshold effect: only at higher biomass inventories do fossil fuel prices decline, suggesting a potential for substitution. However, current policies inadequately support biomass integration, highlighting the need for targeted reforms. Full article

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. The study examines the integration of computational methods, including computational fluid dynamics, neural networks, and genetic algorithms, with various fuel types such as biodiesel, biomass, coal, gasoline, hydrogen, and natural gas. A systematic search in the Scopus database identified relevant articles, which were categorized based on fuel types and computational methodologies. The analysis covers key areas such as combustion modelling and simulation, engine applications, alternative fuels, pollutant control, and industrial combustion systems. This review highlights the growing role of machine learning and genetic algorithms in enhancing combustion efficiency, reducing emissions, and optimizing energy production, providing insights into the current state of the art and future trends in this critical field. The study further examines the geographical distribution of research, noting significant contributions from Canada, China, France, Germany, India, Iran, Japan, Malaysia, Pakistan, Saudi Arabia, the United Kingdom, and the United States, alongside other international contributions. A total of 165 peer-reviewed articles were analyzed, covering a range of combustion scenarios and fuel types. The most frequently applied methods include artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) for predictive modeling, as well as genetic algorithms (GAs) for system optimization. ANN-based models achieved high accuracy in predicting NO_x emissions and flame speed, with some studies reporting mean absolute errors below 5%. GA methods demonstrated effectiveness in fuel blend optimization and geometry design, achieving emission reductions of up to 30% in experimental setups. This review also highlights persistent challenges such as data availability, model generalization, and reproducibility, and proposes future directions toward more interpretable and standardized applications of ML/GA in combustion science. Full article

The development of combustion experiments in a controlled environment is essential for comparing different fuels and quantifying the influence of different key parameters. It is fundamental to understand the transport phenomena at the particle level to obtain reliable results and information for further proper biomass combustion modeling of large-scale equipment. Hence, this paper presents a comprehensive analysis of the thermal decomposition and kinetic of eight samples of forest biomass fuels in terms of combustion behavior by using the thermogravimetric analysis (TGA) technique. The tests were carried out in an oxidizing atmosphere at a heating rate between 5 and 100 °C/min up to 900 °C. It was observed that, for all samples, fuel conversion follows a sequence of drying, devolatilization, and char combustion. Furthermore, differences in chemical and physical composition, as well as in structures and their thermal stability, justify the differences observed between the mass-loss curves of the different fuels. For this, the complexity of kinetic study is addressed in this paper by using different approaches: isoconversional and model-fitting methods. However, the use of isoconversional methods proved ineffective for determining reliable kinetic parameters, due to their sensitivity to particle conversion. A significant variation in activation energy was observed during the devolatilization stage, ranging from 47.92 to 101.30 kJ/mol. For the char oxidation stage, it ranged from 14.97 to 35.48 kJ/mol. These results highlight Eucalyptus as the most reactive species among those studied. Full article

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. Full article

Texas has abundant forest resources, and the forest sector contributes tremendously to the state economy. However, Texas has the lowest log truck weight limits among the neighboring states, which puts the state at a competitive disadvantage in the forest industry. This study examined the economic and environmental impacts of increasing log truck weight limits from 84,000 to 92,000 pounds across these supply chain sectors: forestry, logging, sawmills, and truck transportation. Economic estimation was conducted using IMPLAN with 2023 data, while the environmental impacts were assessed through a survey. Two scenarios, representing 12 and 13 percent efficiency improvements from the increased log truck weight limits, were analyzed using standard truck tare weights. The 12 percent efficiency improvement generated a total of 864 jobs, USD 56.31 million in labor income, USD 90.90 million in value added, and USD 189.91 million in industry output. While the 13 percent efficiency improvement generated a total of 936 jobs, USD 61.01 million in labor income, USD 98.52 million in value added, and USD 205.73 million in industry output. Additionally, the 12 percent and 13 percent efficiency improvements reduced annual fuel consumption by 4.69 million and 5.53 million liters and lowered carbon dioxide emissions by 12.61 thousand and 14.89 thousand tonnes, respectively. These results offer valuable insights for policymakers aiming to improve efficiency and profitability in the timber industry. Full article