Search Results (13,951)

This paper evaluates the efficiency of modernization of the regional railway line Prievidza–Jelšovce in Slovakia using cost–benefit analysis (CBA), reflecting increased investment costs and the potential electrification of the line. The assessment is based on a detailed analysis of transport demand and infrastructure conditions, where daily railway passenger volumes range between 2300 and 3700 passengers, while individual car transport exceeds 10,000 passengers per day in most sections. Two alternative modernization variants were evaluated. The results show that the project generates socio-economic benefits, particularly through travel time savings amounting to approximately €42.3 million and reductions in operating costs and externalities. Significant environmental benefits were identified, especially in the case of the more advanced variant, with reductions in air pollution reaching €56.3 million and greenhouse gas emissions reaching €42.2 million. Despite these benefits, the economic evaluation indicates negative net economic outcomes for both variants. The total economic investment costs (excluding VAT and adjusted for economic appraisal) reach €543.4 million for the EIA variant and €511.9 million for the proposed variant, resulting in net economic values of −€186.2 million and −€70.8 million, respectively. The results therefore suggest that neither variant achieves full economic efficiency under the given assumptions, although the proposed variant performs significantly better. The findings highlight the strong sensitivity of project efficiency to investment costs and the scope of modernization. The study confirms the necessity of regularly updating CBA analyses in transport projects, as changes in input parameters can substantially influence investment decision-making. Full article

As demand for energy-efficient buildings grows, the use of expanded polystyrene (EPS) insulation is expected to increase, intensifying the need for material-efficient strategies such as recycling and reuse. This study investigates the technical feasibility, chemical safety, and climate implications of reusing EPS insulation recovered from building and infrastructure applications. EPS boards with service lives exceeding 20 years were collected from demolition sites and characterised for density, compressive strength, thermal conductivity, and hazardous substance content. Measured material properties were compared with historical test reports from 1976 to 2009 to assess long-term performance. The thermal conductivity and compressive strength of the used EPS samples fell within or close to the 95% prediction intervals for the corresponding products at the time of production, indicating limited long-term degradation. No brominated flame retardants or other substances of concern were detected above the detection limits. Life cycle assessment (LCA) results showed that reuse provides greater greenhouse gas (GHG) emission reduction potential than improved recycling alone, primarily through avoided virgin EPS production and reduced processing needs. An important insight from this study is that key material properties of used EPS can be reliably estimated from simple measurements of density, dimensions, and weight, and that direct reuse is feasible for less demanding applications. Additionally, further work is needed to test additional samples from diverse demolition sites across various applications and climates to establish a consistent basis for reuse. Full article

The electrification of floating production, storage, and offloading (FPSO) units has emerged as a strategic solution to meet the growing demand for increased oil production while reducing carbon emissions associated with onboard gas turbine generation. Power-from-shore (PFS) systems represent a promising approach to achieving this goal, with transmission technologies based on high-voltage direct current (HVDC) and high-voltage alternating current (HVAC) solutions. Although HVDC is more suitable for long-distance and high-power applications, HVAC systems offer advantages in terms of robustness, simplicity, and operational maturity. Nevertheless, the reactive power compensation requirements arising from the high capacitance of submarine cables remain a major technical challenge. This study investigates and compares several reactive power compensation topologies applied to three distinct PFS systems. The proposed methodology enables a comprehensive evaluation of both onshore and subsea reactor placement strategies under technically and technologically feasible conditions. The results demonstrate that long-distance transmission of 75 MW over 250 km was achieved exclusively through subsea compensation configurations, which maintained efficiencies above 90% and voltage and current profiles within operational limits. Conversely, onshore-only compensation proved to be the most efficient solution for shorter transmission distances. The results demonstrate that the full electrification of an FPSO is technically feasible, with voltage and current profiles remaining within acceptable operational limits. The findings also indicate that mid-cable reactor placement (at 50%) is not the most effective configuration, with superior results observed for placements at 20–80% and 40–70% of the cable length. Overall, the outcomes confirm that subsea reactor placement enables higher power transfer over longer distances, significantly extending the technical boundaries traditionally separating HVDC and HVAC solutions. These results emphasize the need for continued technological development to make subsea shunt reactor installation a viable and reliable option for future FPSO electrification projects. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

This study comparatively assesses the Municipal Emission Reduction Plans (MERPs) of Spetses, Platanias, and Souli, examining their role as analytical and strategic tools for local climate planning, with particular emphasis on water-related infrastructure. A descriptive comparative analysis was conducted using secondary data extracted from officially approved MERPs, covering sectoral and total greenhouse gas emissions for 2019 and 2023, as well as reported mitigation actions and 2030 targets. The results reveal significant inter-municipal variations in emission patterns, driven by geomorphological characteristics, infrastructure configuration, and energy consumption, but also by governance structures and system boundaries. Water supply and irrigation systems are identified as highly energy-intensive sectors, particularly in municipalities with extensive, pumping-dependent networks. At the same time, the analysis shows that the inclusion or exclusion of outsourced services—such as water supply and wastewater management—substantially affects the representation of emissions and the prioritization of mitigation actions. The study concludes that MERPs can support climate planning at the municipal level, but their effectiveness is conditioned by data completeness, system boundaries, and governance models. These findings highlight the need to move beyond purely accounting-based approaches toward integrated planning frameworks that capture the full operational scope of municipal systems, enabling more accurate emission assessment and more effective, context-specific mitigation strategies within the water–energy–nexus. Full article

The energy consumption of digital communication infrastructures is increasingly recognized as a component of operational building energy use. In multi-story public buildings, Wireless Local Area Networks (WLANs) are typically deployed under static, always-on configurations, leading to avoidable energy overhead caused by spatial interference and inefficient access point placement. This study proposes an energy-aware WLAN deployment framework that integrates user-weighted spatial placement with deterministic three-dimensional vertical interference coordination. The framework is evaluated using 50 independent Monte Carlo simulations on a representative three-story public building model. Results indicate a reduction in annual operational energy consumption from 1892.71 kWh to 1333.71 kWh (-29.5%), with a proportional decrease in carbon emissions, while maintaining a 97% coverage requirement. Furthermore, worst-case signal quality improves, with Signal-to-Interference-plus-Noise Ratio (SINR) P10 increasing from 17.66 dB to 25.53 dB and median network capacity rising by 30.6%. These findings suggest that interference-aware spatial coordination can function as an effective energy optimization layer within building-integrated digital infrastructures. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO₂ emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

The transition toward low-impact facility management requires robust evidence that environmental optimization does not compromise hygienic reliability. In the professional cleaning sector, sustainability claims are often based on product substitution rather than on integrated performance validation. This study provides a dual-perspective evaluation comparing a conventional cleaning protocol with the Pfe Green system, an eco-designed approach compliant with the Italian Minimum Environmental Criteria (CAM, D.M. 29 January 2021), implemented in a civil building of Renault Italia S.p.A. in Rome. A combined Life Cycle Assessment (LCA) and microbiological evaluation was conducted under real operational conditions to quantify both climate-related and hygienic outcomes. The LCA, performed in accordance with ISO 14040–44 and ISO 14067 standards, demonstrated that the Green protocol achieved an approximately 30% reduction in Global Warming Potential over a 100-year horizon (GWP₁₀₀), mainly attributable to structural process optimization, including reduced detergent mass, lower laundering temperatures, improved dilution control, and energy-efficient machinery. Microbiological monitoring, conducted according to ISO 14698 and UNI EN ISO 18593, showed that both systems ensured high levels of microbial abatement, with the Green protocol exhibiting slightly higher average reductions in total viable counts (96.1% vs. 94.2%). These findings confirm that lower chemical input and eco-formulated products do not compromise hygienic performance when supported by standardized procedures and microfiber-based mechanical removal. By integrating life-cycle metrics with microbiological validation, this research proposes a replicable assessment model for sustainable cleaning services. The results demonstrate that CAM-aligned green protocols can simultaneously reduce greenhouse gas emissions and maintain hygienic effectiveness, thereby supporting evidence-based sustainable procurement and corporate environmental strategies. The originality of this study lies in the integration of life-cycle environmental assessment with real-world microbiological validation under operational conditions, providing a comprehensive framework for evaluating sustainable cleaning systems beyond product-level substitution. Full article

With increasingly stringent greenhouse gas emission regulations in the shipping industry, there is an urgent need for an efficient energy management strategy for new energy ship power systems. However, existing dispatch models often overlook the dynamic energy-saving potential of active drag reduction technologies and lack effective optimization algorithms capable of handling high-dimensional, multi-constrained problems. To address these problems, this paper proposes a novel integrated dispatch framework for hybrid energy ship power systems that incorporates air lubrication systems. First, a unified multi-energy dispatch model is established, coupling the dynamic operation of air lubrication systems with electrical, thermal, and propulsion energy flows. Second, an Improved Traffic Jam Optimizer algorithm is proposed, which enhances global exploration and local exploitation through a nonlinear parameter adaptation mechanism, differential mutation strategy, and dynamic hybrid search architecture. Convergence analysis based on Markov chain theory is provided to guarantee algorithmic reliability. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, solution accuracy, and stability. Furthermore, integrating air lubrication systems into the ship power system reduces total operating costs and greenhouse gas emissions by up to 20.569% and 6.310%, respectively. Full article

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

Stabilization/solidification (S/S) is a crucial technology for the engineering reuse of oil-contaminated soil. A key challenge, however, is preventing the migration of residual oil under varying hydraulic conditions. This study investigates the efficacy of a lime and fly ash binder in treating oil-contaminated soil. We systematically compared the performance of untreated (UOCS) and treated (TOCS) soils under different aqueous environments (humidity injection, water injection, and permeation). We evaluated oil migration, water-holding capacity, and permeability characteristics. The results demonstrate that the lime–fly ash treatment effectively adsorbed and immobilized oil contaminants, restricting their mobility to a remarkably low range of 0.54% to 4.90%. Furthermore, the S/S treatment significantly improved the soil’s hydraulic properties: it enhanced the water-holding capacity, reduced the soil-water characteristic curve hysteresis, and counteracted the oil-induced hydrophobicity. Consequently, the effective permeation channels were restored, leading to a higher permeability coefficient in TOCS compared to UOCS. Crucially, the hydro-mechanical performance of the treated soil met the criteria of the Solidification/Stabilization Resource Guide, confirming its suitability for engineering applications. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article