Search Results (423)

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Heavy-duty trucking is central to the U.S. economy, and improving its long-term sustainability requires cost-effective, energy-efficient, and reliable operations. Emerging technologies—advanced powertrains, batteries, and alternative fuels—offer potential solutions, but their economic and operational viability remains uncertain. This study evaluates the performance of Class 8 battery electric (BEV), plug-in hybrid (PHEV), fuel cell electric (FCEV), and diesel trucks in terms of energy use and the levelized cost of driving (LCOD) to determine when these technologies become competitive without compromising operational reliability. The analysis explores how evolving fuel prices and vehicle technology improvements in 2023, 2035, and 2050 influence the cost competitiveness of each powertrain. By comparing the results at both the technology level and the fleet level, the study demonstrates that powertrains that appear cost-effective on individual routes may not always scale to fleet-wide viability, and vice versa. The analysis is based on real-world data from over 15,700 Class 8 truck trips recorded in California in 2022, capturing diverse driving scenarios, payload conditions, and operational constraints. The results show that BEV250 can deliver cost-effective performance in short-haul operations (0–250 miles) under depot electricity prices below USD 0.34/kWh and maintain this advantage through 2050 as battery costs decline. In the 250–500-mile segment, the technology-level analysis indicates that BEV500 often achieves the lowest LCOD on individual tours, particularly under low electricity prices, while the fleet-level results show that FCEVs provide a more consistent cost performance across all tours, especially when the route variability is high. For long-haul operations (>500 miles), where BEVs are assumed to operate without en-route charging, FCEVs emerge as the most cost-effective non-diesel option by 2050, provided hydrogen prices fall below USD 6/kg. PHEVs show a limited long-term competitiveness and are mainly viable under transitional fuel price conditions. Overall, the findings underscore that there is no one-size-fits-all solution. Powertrain adoption must be range-aware, infrastructure-sensitive, and fleet-structured. By integrating technology-level and fleet-level perspectives, this study provides actionable insights for fleet operators, policymakers, and industry stakeholders seeking to balance cost, reliability, and sustainability in heavy-duty freight. Full article

The dynamic monitoring of fishing activities is fundamental to fishery management. Leveraging multi-year (2020–2023) AIS data from squid jigging vessels, this study employed a multi-level data mining and spatial statistical approach to decode the spatiotemporal patterns of fishing effort in the Southeast Pacific Ocean. Our analysis reveals a highly concentrated and cyclical operation model: temporally, 20% of days contributed 46% of the total effort; spatially, 30% of the fishing grounds accounted for 80% of the effort, forming four persistent hotspots. Vessels exhibited a distinct bimodal speed distribution, enabling clear behavioral differentiation. While no fishing was detected inside the seasonal no-take zone, activities were observed near its boundaries and Exclusive Economic Zones, highlighting compliance and potential risks. The significant spatial aggregation, strongest in June, underscores the tight linkage between fleet operations and resource distribution. These findings provide a scientific basis for spatially explicit management strategies to ensure the sustainable harvesting of squid resources. Full article

Sustainable development is currently a key priority in improving production systems, requiring an integrated approach that combines economic efficiency, environmental responsibility, and rational energy management. In response to these challenges, this article presents a novel application of computer simulation as a tool for comprehensively assessing the impact of technological improvements in the machining process. The study introduces and compares two models: a baseline model representing the actual state of the machinery fleet with conventional machine tools, and an innovative alternative model incorporating modern numerically controlled (CNC) machines. The results demonstrate, for the first time in this context, that the implementation of CNC technology not only significantly reduces process time and energy demand but also improves resource efficiency, thereby lowering CO₂ emissions and operating costs. This research highlights the innovative use of computer simulation to support decision-making in sustainable manufacturing, offering a practical framework for evaluating technological modernization options and promoting the sustainable development of production enterprises. Full article

The aim of the paper is to recognize the role of external institutions in supporting the Transport, Shipping, and Logistics (TSL) sector in the transformation towards sustainable and low-emission operations in Poland. In the context of the EU’s decarbonization agenda and accelerating climate challenges, the study explores how regulatory, financial, and normative mechanisms affect the electrification of transport fleets. A mixed-methods approach was applied, combining qualitative content analysis of European and national policy frameworks with a quantitative CATI survey among logistics enterprises. The results reveal that legal and normative instruments remain the dominant institutional drivers of fleet electrification, while fiscal incentives—subsidies and tax reliefs—play a supportive but still secondary role. Sectoral and financial pressures from banks and market stakeholders are emerging as new, complementary forces of change. Firm size, ownership structure, and market scope significantly moderate these perceptions. The paper contributes to institutional and innovation-diffusion theory and offers policy insights for designing coherent and multi-level frameworks. Full article

Autonomous vehicles (AVs) promise improved safety and sustainability, yet their sophisticated sensing, computing, and communication systems impose auxiliary power loads of 1.5–3.2 kW, risking an increase of up to 45% in global transport energy demand by 2040 if left unaddressed. Existing energy management strategies fail to jointly optimize propulsion and autonomy subsystems under real-world dynamic traffic, treat ADAS loads as static, and lack statistically rigorous validation. This paper proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS)-PID framework that integrates (i) 5 s V2X traffic preview, (ii) online PID gain scheduling, and (iii) energy-aware rule pruning for real-time energy allocation. Validated on a real-world trajectory dataset, the approach consistently reduces fuel consumption by up to 4.4% over pure fuzzy logic, 0.05% over FL-RWOA, 1.16% over FL-GWO, and 2.39% over FL-PSO across 25–100 km segments (paired t-test, p ≤ 0.001 on 50 random segments). Additional benefits include 18% faster transient response and 18% lower inference computational load compared to metaheuristic baselines. Scaled to fleet level, the 0.51 L/100 km average saving equates to over CAD 100 million annual savings in Canada. The hybrid neuro-fuzzy architecture offers a deployable, adaptive solution for sustainable autonomous transportation. Full article

The increasing frequency and intensity of extreme disasters, exacerbated by climate change, pose significant challenges to sustainable development by disrupting critical infrastructure and hampering relief efforts. Enhancing disaster resilience—a core objective of sustainable development—requires integrated approaches that simultaneously address infrastructure restoration and efficient resource allocation. This study proposes a sustainable optimization framework for post-disaster response, integrating road rehabilitation decisions with emergency logistics planning within a three-tier supply chain network. We develop a mathematical model that synergistically optimizes repair crew scheduling, depot location, and vehicle routing, with the objective of maximizing a comprehensive satisfaction index that balances timely delivery (time satisfaction) and fulfillment of material needs (demand satisfaction). This integrated approach directly contributes to sustainable disaster management by ensuring more reliable and equitable access to vital resources in affected communities. A tailored variable neighborhood search algorithm is designed to solve the model efficiently, as demonstrated through large-scale numerical experiments. Our findings highlight several policy-relevant insights for sustainable emergency planning: adequate budgeting is crucial for uninterrupted relief operations; strategic investments in rapid road repair capabilities or vehicle fleets significantly enhance system efficiency; and prioritizing time satisfaction (rapid response) yields greater overall benefits than merely increasing delivered quantities. Furthermore, restoring critical road infrastructure is shown to mitigate transportation uncertainties, thereby strengthening the resilience of the entire relief system. This work provides a quantifiable methodology and practical decision support tools for building more sustainable and resilient communities in the face of disasters. Full article

The present integration of electric vehicles into everyday life has the potential to redefine current standards of urban mobility. However, the territorial impact of this deployment demands a multiscale effort to ensure both efficient and sustainable performance; this is even more necessary in a disconnected system like an island. This article addresses the possibility of transforming the existing fossil-fuel-based infrastructure within Europe’s outermost regions into an electric vehicle charging network, with particular emphasis on the Canary Islands’ strategic plans. Using official datasets from Red Eléctrica de España (REE), IDAE, and the Canary Islands’ Energy Transition Plan (PTECan), we develop three scenarios (2025 baseline, 2030, and 2040) to quantify the additional electricity demand, peak load requirements, charging infrastructure needs, and associated greenhouse gas emissions. The methodology combines EV fleet projections, the driving patterns of residents and tourists, and vehicle efficiency data to estimate yearly electricity demand and hourly charging loads. The carbon intensity profiles of each island’s grid are used to calculate well-to-wheel emissions of EVs, benchmarked against internal combustion engine vehicles. The results indicate that achieving 250,000 EVs by 2030 would increase electricity demand by 1.1–1.4 TWh/year (+8–12% of current consumption), requiring approximately 25,000–30,000 public charging points. EV emissions range from 90 to 150 gCO₂/km depending on charging time, compared to 160–190 gCO₂/km for ICE vehicles. Smart charging and vehicle-to-grid integration could mitigate 15–25% of peak load increases, reducing the curtailment of renewables and deferring grid investments. A comparative analysis with Zealand highlights policy synergies and differences in insular versus continental grids. The findings confirm that large-scale EV adoption in the Canary Islands is technically feasible, but quite difficult, as it requires the deep, coordinated planning of renewable expansion, storage, and a charging infrastructure. BEV WTW advantages become unequivocal once the average grid carbon intensity falls below ≈0.8–0.9 tCO₂/MWh, underscoring the primacy of accelerated renewable build-out and demand-side flexibility. Despite uncertainties in adoption and technology trajectories, the approach is transparent and reproducible with official datasets, providing a transferable planning tool for other islanded systems and mainland Europe. The proposed method demonstrates its usefulness in direct linking electrification scenarios with the real capacity of the electricity system, allowing the identification of very critical integration thresholds and guiding evidence-based planning decisions. Full article

Drastically cutting carbon footprints to reduce global warming is now a universal norm, in keeping with the United Nations’ Convention on Climate Change 2015. The global proliferation of electric vehicles (EVs) is, hence, appropriate. India (Niti Aayog) has given a determined call for ‘only EV’ on road by 2030, a transition which will be led by electric two-wheelers (E2Ws) with 80% of the market. The Indian E2W (IE2W) industry needs to adopt green manufacturing and sustainable supply chain management (SSCM), addressing environmental, economic, and social issues. The battery supply chain (an environmental gray area) must also follow circularity and sustainability principles. With artificial intelligence (AI) having come into play in industry and manufacturing, it will undoubtedly influence the circular economy (CE) and sustainability concerns in the IE2W space. This review aims to critically study the available literature on AI’s contribution to CE and sustainability in the IE2W sector. The study has revealed a lack of sufficient research, specifically in the IE2W sector, including AI’s effect on waste management, government policies, etc. For the government, the study recommends a higher outlay for R&D, bridging skill gaps, and strengthening regulatory frameworks and ethics; and, for the IE2W industry, this study recommends increased focus on CE, public awareness, compliance with ethical norms for AI deployment, and prioritizing a fleet-first model. The study is expected to enhance value for the IE2W sector, the government, the public, and the environment. Full article

Reducing greenhouse gas (GHG) emissions and fuel consumption remains a critical objective in courier fleet management. Dynamic routing, which continuously updates delivery routes in response to real-time conditions, offers a promising solution. However, its implementation is hindered by challenges in real-time data analytics and intelligent decision-making. This study addresses two underexplored, yet impactful, variables in dynamic fleet routing: (1) the changing weight of delivery trucks due to unloading at each stop and (2) traffic conditions on local roads, where most deliveries occur. We propose a machine learning-driven smart rerouting system that integrates real-time data analytics into a dynamic routing optimization framework focused on minimizing fuel consumption. Our approach consists of two key components. First, trucks are equipped to collect continuous real-time data on vehicle weight, which are analyzed using frequency domain techniques, and traffic conditions, which are interpreted via neural networks. Second, these data inform an optimization model that explicitly captures the relationship between fuel consumption, emissions, vehicle weight, and traffic dynamics. This model surpasses conventional capacitated vehicle routing approaches by embedding real-time reasoning into route planning. Extensive simulation studies demonstrate that the proposed system significantly reduces both GHG emissions and fuel consumption compared to traditional routing models, highlighting its potential for sustainable and cost-effective fleet operations. Full article

We present a site-agnostic workflow to identify Fireline Tactical Support Points (FTSPs) and corridors following wildfire where spectral-change proxies (dNBR, RdNBR, and dNDVI) are paired pre/post-fire and co-registered on a 20 m grid together with a 72 h rainfall accumulation layer, which is treated as an operational feasibility and safety overlay, complementing access and terrain. Applied to the Vesuvius National Park (Italy) wildfire episode of August 2025, the pipeline yields suitability/susceptibility surfaces, ranked factors, and corridor candidates, with estimated successes including coherent prioritization within high-severity mosaics, improved continuity toward existing access routes, and reduced overlap with mapped sensitive areas at like-for-like suitability. Low-carbon staging is retained as a design safeguard, while detailed greenhouse-gas accounting is intentionally deferred to future, fleet-resolved multi-criteria analyses. The approach enables rapid, repeatable decision support and is relevant to SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

The penetration rate of battery electric buses (BEBs) continues to rise, and the design of BEB networks has become the foundation for establishing efficient and sustainable public transportation systems. Improving the equity of bus network and reducing the total cost of the bus system are taken as the targets, a multi-objective programming model for TNDP is proposed in this study. Among them, the Gini coefficient of bus travel times during peak hours and the direct travel proportion of the elderly during non-peak hours are used to describe the equity of the bus network. When calculating the comprehensive cost, factors such as the fleet size of battery electric buses, charging facilities requirements, and charging costs are taken into account. To enhance the reliability of the obtained results, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to generate the Pareto-optimal solution set. The Mandl’s benchmark network is used for comparative validation, and a case study based on the road network of Zhengzhou is undertaken. Calculation results indicate that the proposed model not only minimizes the total travel costs but also significantly reduces the Gini coefficient of the transportation mode distribution. Under the constraint of overall expenses, it effectively improves the equity and the direct travel proportion of the elderly served by the bus system. The results can provide quantitative support to formulate livelihood transportation policies for local government and optimize the allocation of public transportation resources. Full article