Search Results (361)

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

The sustainable development of eco-tourism is significantly influenced by multiple conditions within spatiotemporally continuous geographic scenarios. However, existing evaluations of the development value of eco-tourism resources (Eco-TRDVs) are non-spatial and do not sensitively represent their complex relationships. This study proposed a GIS approach for evaluating regional Eco-TRDVs by mapping the complex interconnections with spatial distances. Inherent and external conditions for evaluating Eco-TRDVs were classified under three indicators and digitized using GIS and remote sensing technologies. Then, the analytic hierarchy process and GIS cost distance analysis were introduced to define the initial values and cumulate Eco-TRDVs with distances. Taking the Taihang Honggu National Forest Park, China, as the case area, the Eco-TRDVs over the entire area in 2017 and 2020 were mapped. The results present a continuous spatial variability of Eco-TRDVs and comprehensively reflect the complex interconnections of constraint elements with spatial distances. The evaluation is sensitive to the intrinsic value of poles, as evidenced by the high development values and high-density distribution of their contours. Source additions improve the evaluation considerably, with transportation networks having a greater impact than economic development zones and urban elements. Furthermore, aggravated fragmentation of the price flow field increases spatial heterogeneity. The development value shows a negative linear correlation with distance. The proposed approach handles the spatially oriented relationships of the multi-conditions, and supports future planning and monitoring of spatial-temporal changes in eco-tourism development. Full article

Intermodal transport (IT), which implies the combination of several different types of transport to achieve a more efficient and economical movement of goods, is of increasing importance in modern supply chains. In the conditions of globalization, growth of trade flows and increasingly pronounced requirements for sustainability, effective planning and management of intermodal routes have become crucial, which is why their evaluation and ranking are essential for making strategic and operational decisions. Accordingly, this paper aims to identify the most favorable alternative for developing intermodal transport. Deciding on the choice of the most important intermodal route requires consideration of a large number of criteria, often of a mutually conflicting nature, which places this problem in the domain of multi-criteria decision-making (MCDM). Accordingly, this paper develops a hybrid decision-making model in a fuzzy environment, which combines fuzzy DELPHI (FDELPHI), fuzzy factor relationship (FFARE), and fuzzy axial-distance-based aggregated measurement (FADAM) methods. The model enables the identification and evaluation of relevant criteria, as well as the ranking of defined variants under the requirements and attitudes of various stakeholders. The practical application and effectiveness of the developed model were demonstrated and confirmed by a case study for Bosnia and Herzegovina (B&H). The sensitivity analysis showed that even with changes in the weights of the criteria or the elimination of the most important criteria, the solution remains consistent and reliable. This indicates the robustness of the model and suggests that changes in the parameters do not lead to significant changes in the final results. This confirms the validity of the proposed model and increases confidence in its applicability in practice. Full article

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article

Selecting the most suitable machines to use for wood recovery is essential for computing the operating costs of the whole forestry machinery chain (FMC). Nevertheless, a generalized approach for selecting the most suitable FMC and quantifying the corresponding economic performances for wood recovery (i.e., harvesting and long-distance transport) is still missing. The primary aim of this study is to describe a decision support model, called FOREstry MAchinery chain selection (“FOREMA v1”), which is able to (i) select the most feasible FMC and (ii) calculate the costs (such as EUR∙h⁻¹; EUR∙t⁻¹ of dry matter, DM) of each operation (OP) comprising the FMC. The model is made up of three different modules (Ms): machinery chain selection (M1), machinery chain organization (M2), and cost calculation (M3). In M1, feasible FMCs are defined according to seven technical parameters that characterize the forest area. For each FMC, FOREMA v1 defines the sequence of OPs and the types of machines that can potentially be used. Once the characteristics of the area in which wood recovery occurs are processed, the user selects the types of machines to use according to the model’s suggestions. In M2 and M3, the user is supported in organizing the FMC (e.g., calculation of the required time, working productivity, and so on) and computing the operating costs. The secondary aim of this study is to discuss a case study focused on chips production for energy generation, providing empirical evidence on how FOREMA v1 works. The proposed model provides a systematic approach for the selection and optimization of the most suitable FMC to adopt for biomass recovery, thus supporting decision-making processes. The results showed that felling had the lowest cost per unit of time (63.7 EUR·h⁻¹) but the highest cost per unit of mass (35.4 EUR·t DM⁻¹) due to its longer working time and lower productivity. Loading and long-distance transport incurred the highest costs both per unit of time (223.5 EUR·h⁻¹) and per unit of mass (29.4 EUR·t DM⁻¹), attributed to the use of medium–small-sized trailers coupled with tractors operating at low speeds, leading to a high number of cycles. For the entire FMC the costs were equal to 147.3 EUR·h⁻¹ and 101.1 EUR·t DM⁻¹. Full article

Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Full article

Electric vehicles (EVs) are gradually gaining high penetration in transportation due to their low carbon emissions and high power conversion efficiency. However, the large-scale charging demand poses significant challenges to grid stability, particularly the risk of transformer overload caused by random charging. It is necessary that a coordinated charging strategy be carried out to alleviate this challenge. We propose a hierarchical charging scheduling framework to optimize EV charging consisting of demand prediction and hierarchical scheduling. Fuzzy reasoning is introduced to predict EV charging demand, better modeling the relationship between travel distance and charging demand. A hierarchical model was developed based on NSGA-II, where the upper layer generates Pareto-optimal power allocations and then the lower layer dispatches individual vehicles under these allocations. A simulation under this strategy was conducted in a residential scenario. The results revealed that the coordinated strategy reduced the user costs by 21% and the grid load variance by 64% compared with uncoordinated charging. Additionally, the Pareto front could serve as a decision-making tool for balancing user economic interest and grid stability objectives. Full article

Carbon transfer embodies the spatial redistribution of carbon emissions resulting from interregional economic activities and trade. In recent years, accelerated regional integration and deepening specialization within industrial chains have rendered traditional bilateral analytical frameworks inadequate for capturing the complexity of interregional carbon transfer networks. This evolving context necessitates the incorporation of spatial interaction effects to elucidate the multi-nodal and multi-pathway characteristics inherent in contemporary carbon transfer patterns. Based on the spatial interaction theoretical framework and a multiregional input–output (MRIO) model, we analyze the spatial dependence characteristics of interregional carbon transfer in China. The results reveal that interregional carbon transfer in China exhibited an upward trend from 2012 to 2017, demonstrating statistically significant positive origin dependence, destination dependence, and network dependence. The distance between regions exerts a significantly negative influence on interregional carbon transfer. Interregional carbon transfer is not merely a bilateral phenomenon; its fundamental nature is characterized as a network phenomenon. Our study demonstrates that precise regulation of the allocation of industrial land and transportation infrastructure land, strengthening the decisive role of market mechanisms in resource allocation for regional low-carbon development, and establishing interregional collaboration mechanisms for low-carbon exchange can effectively reduce the occurrence of interregional carbon transfer. These findings provide policymakers with more precise information to achieve equitable carbon emissions distribution across regions. Full article

This study investigated the impact of different reverse logistics strategies on the economic and environmental performance of a system within the rubber flooring sector. A simulation tool was developed to replicate the behavior of a real production system, focusing on the transition from linear to circular processes. By considering multiple factors influencing system performance, this research offers an overview of the sustainability of various RL strategies and provides realistic estimates for different scenarios. Three key factors were used to evaluate each strategy’s response: transportation distance, flooring thickness, and returned flooring quality. The findings suggest that an environmental advantage generally favors on-site inspections at the customer’s location to assess the returned product’s condition, regardless of distance. However, centralizing inspections at the manufacturer’s facility is more economically advantageous when distances are short, particularly when the company prioritizes recycling over other circular economy practices. Based on these results, practical implications and guidelines are proposed to help companies balance cost-effectiveness with sustainability, optimizing their operations within a circular economy framework. Full article

With the increase in urban–rural disparities in China, rural public transportation systems in low-density areas face unique challenges, especially in the contexts of sparse population, complex topography, and uneven resource allocation; research on public transportation in low-density areas has had less attention compared to high-density urban areas. Therefore, how to solve the dilemma of public transportation service provision in low-density rural areas due to sparse population and long travel distances has become an urgent problem. In this paper, a dynamic optimization model based on a two-layer planning framework was constructed. The upper layer optimized the topology of multimodal transportation nodes through the Floyd shortest path algorithm to generate a composite network of trunk roads and feeder routes; the lower layer adopted an improved Logit discrete choice model, integrating the heterogeneous utility parameters, such as time cost, economic cost, and comfort, to simulate and realize the equilibrium allocation of stochastic users. It was found that the dynamic game mechanism based on the “path optimization–fairness measurement” can optimize the travel time, mode, route, and bus stop selection of rural residents. At the same time, the mechanism can realize the fair distribution of rural transportation network subjects (people–vehicles–roads). This provides a dynamic, multi-scenario macro policy reference basis for the optimization of a rural transportation network layout. Full article

The COVID-19 pandemic has caused a profound interruption in the way people travel and has had a very negative impact on tourism and economics throughout the world, especially on the coastal fishing communities in South Korea. These previously problematic areas, having suffered a decrease in the local population as well as stood in the midst of the economic downturn, experienced a great cut in the number of tourists coming from far away, which additionally caused their collapse of resilience and sustainability. This research investigates the recovery trends of 45 seashore-fishing districts in South Korea and how the change in travel distance and the number of visitors before and after the pandemic have affected these trends. Through the utilization of big data from the Korea Tourism Data Lab (2019–2023) and Geographic Information System (GIS) analysis, we observe the changes in visitor flows, use the indices of resilience as an indicator to measure them, and investigate how proximity affects travel recovery. The survey results indicate that the regions neighboring metropolitan zones were not only the ones that suffered the most from travel distance during the pandemic but also experienced quick recovery after the pandemic. The new promotional campaigns, in tandem with an improved network of transportation, contributed to the swift recovery of these areas. The remote areas, on the other hand, persist in fighting the problems of regionalized tourism and have only limited accessibility. The proposition of “distance-dependent resilience” theory as well as the Blue Urbanism framework is offered in order to bring up the ideas of sustainable tourism and population stabilization. The study is expected to serve as a cornerstone for the practice of adaptive governance and strategic planning in the matter of the coastal areas after the pandemic. Full article

The vast majority of ocean plastics originate from land and are transported over long distances to their final sink. Yet, our current understanding of transfer mechanisms through rivers and estuaries remains poor due to a lack of consistent methods for assessing and monitoring plastic waste. In this study, we quantify and characterize the abundance of plastics in the Tijuana River estuary, located along the U.S.–Mexico border. We found a total of 2804 plastic debris items, of which 79.3% were sampled during heavy rainfalls and 20.7% during the dry period. Overall, most plastics were attributed to five economic sectors: packaging, food, construction, fishing, and tourism, highlighting losses during the use and waste management phases of the plastic’s value chain. Based on the results of the analysis, consistent monitoring of plastic pollution is recommended for managing variable plastic loads. Full article

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

With growing global concerns over sustainable wastewater treatment, there is a pressing need for low-cost, eco-friendly filtration solutions. This study conducted a life cycle assessment (LCA) to evaluate the potential of improving slow sand filtration efficiency by integrating alternative materials like clay and oyster shell powder (OSP), while minimizing the environmental footprint. Additionally, the adaptability of three-dimensional (3D) printing was explored to incorporate these materials into innovative filter designs, assessing scalability for broader wastewater applications. Ten filter configurations, including a slow sand filter (SSF) enhanced with OSP (90:10) and 3D-printed clay–OSP bricks (ratios of 90:10, 85:15, 80:20), were assessed across three sourcing distances: local (in situ), regional (161 km), and distant (1609 km). The results showed that SSFs with OSP consistently delivered lower environmental impacts, reducing freshwater ecotoxicity, eutrophication, and human toxicity by up to 4% compared to conventional SSFs, particularly when transport was minimized. Among brick-based systems, single-brick columns offered the best balance of performance and impact, while three-brick columns had the highest environmental burden, largely due to the increased electricity use. Economic analysis reinforced the environmental findings: SSFs with OSP were the most cost-effective option, followed closely by SSFs, while brick-based systems were slightly more expensive, with costs rising sharply when sourcing distances exceeded 161 km. Overall, integrating OSP into SSFs offers an optimal balance of sustainability and affordability, while single-brick columns (90:10) present a promising alternative. Future research should further optimize material blends and design configurations to align with long-term environmental and economic goals. Full article