Search Results (313)

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

The integration of autonomous vehicles (AVs) into urban transportation systems has significant potential to enhance traffic efficiency and reduce environmental impacts. This study evaluates the impact of different AV penetration scenarios (0%, 10%, 30%, 50%) on traffic performance and carbon emissions along Prince Mohammed bin Salman bin Abdulaziz Road in Riyadh, Saudi Arabia. Using microscopic simulation (SUMO) based on real-world datasets, we assess key performance indicators such as travel time, stop frequency, speed, and CO₂ emissions. Results indicate notable improvements with increasing AV deployment, including up to 25.5% reduced travel time and 14.6% lower emissions at 50% AV penetration. Coordinated AV behavior was approximated using adjusted simulation parameters and Python-based APIs, effectively modeling vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N) communications. These findings highlight the benefits of harmonized AV–human vehicle interactions, providing a scalable and data-driven framework applicable to smart urban mobility planning. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. Full article

While airlines can directly quantify carbon emissions based on flight-specific fuel burn data, such data, along with data on other gaseous emissions that do not scale linearly with fuel consumption, are often unavailable to external stakeholders, necessitating the reliance on estimation models. Emissions are thus approximated from known quantities, with most usually from the fuel burned and distance travelled. Emission approximators developed for the aviation industry thus involve some degree of approximation and assumptions, as well as different exogenous and endogenous factors. As a result, such solutions differ primarily due to the significant methodological variations they incorporate. This paper assesses carbon emission approximators developed to valorize emissions generated by flight operations. It reveals the significance and sources of the misestimation of emissions by focusing on the ICAO Carbon Emission Calculator (ICEC), ICAO CORSIA CO₂ Estimation and Reporting Tool (CERT) and EUROCONTROL’ Advanced Emission Model (AEM) and Small Emitters Tool (SET). Thereby, the main research findings indicate considerable estimation uncertainty among the reviewed solutions, ranging from 1.77% to 27.95% on average compared to the baseline, which translates to statistical confidence levels ranging from 15% to 77.50% on average with respect to a 95% confidence threshold. Full article

Electric vehicles (EVs) play a critical role in the transition to transportation electrification and are important for achieving carbon neutrality in this sector. China currently leads the world in EV ownership; however, the energy regulation potential of in-use batteries remains largely untapped in the context of an increasingly saturated EV stock. This study systematically evaluates the long-term benefits of vehicle-to-everything (V2X) applications based on EV sales projections and advancements in battery technology. The results indicate that, without compromising daily travel requirements, V2X applications could enable 109.50–422.37 TWh of annual electricity dispatch by 2030, achieving an estimated economic benefit of 198.92–767.25 billion CNY, and reducing carbon dioxide (CO₂) emissions by 45.01–173.60 Mt. By 2060, these figures are projected to increase significantly, with annual dispatchable electricity reaching 4217.39–21,689.43 TWh, generating an economic value of 10.82–55.66 trillion CNY, and reducing CO₂ emissions by 118.09–607.30 Mt. Furthermore, V2X applications could substantially contribute to achieving the emission reduction targets outlined in China’s Nationally Determined Contributions (NDCs). These findings highlight that V2X applications, as a transformative solution that promotes deep integration between the transportation and power sectors, enhance cross-sectoral emission reduction synergies and support the realization of carbon neutrality goals. Full article

This study focuses on the importance of sustainability in the tourism and accommodation sector in terms of energy use. Energy, which is one of the biggest cost components in accommodation facilities, not only brings a financial burden but also leads to environmental degradation through significant carbon emissions. On the other hand, as environmental awareness increases globally, the number of environmentally sensitive travellers increases and accommodations that stand out with sustainable practices and use renewable energy sources are preferred. There is a lack of comprehensive research on this subject in Northern Cyprus. This study is a preliminary study for a more comprehensive study. Due to the key role of managers in the transition to sustainable energy use in accommodation facilities, their opinions are very important in determining the situation. Therefore, the study aims to learn the evaluations of hotel managers in order to determine the status of sustainable energy practices in accommodation facilities. Our findings indicate that although hotel managers in Northern Cyprus are aware of holistic energy management, legal and infrastructural barriers significantly hinder the practical implementation of environmentally friendly energy practices. Full article

One of the challenges of our age is climate change and the ways in which it affects the Earth’s global ecosystem. To face the problems linked to such an issue, the international community has defined actions aimed at the reduction in greenhouse gas emissions in several sectors, including the aviation industry, which has been requested to mitigate its environmental impact. Conventional aircraft propulsion systems depend on fossil fuels, significantly contributing to global carbon emissions. For this reason, innovative propulsion technologies are needed to reduce aviation’s impact on the environment. Electric propulsion has emerged as a promising solution among the several innovative technologies introduced to face climate change challenges. It offers, in fact, a pathway to more sustainable air travel by eliminating direct greenhouse gas emissions, enhancing energy efficiency. Unfortunately, integrating electric motors into aircraft is currently a big challenge, primarily due to thermal management-related issues. Efficient heat dissipation is crucial to maintain optimal performance, reliability, and safety of the electric motor, but aeronautic applications are highly demanding in terms of power, so ad hoc Thermal Management Systems (TMSs) must be developed. The present paper explores the design and optimization of a TMS tailored for a megawatt electric motor in aviation, suitable for regional aircraft (~80 pax). The proposed system relies on coolant oil injected through a hollow shaft and radial tubes to directly reach hot spots and ensure effective heat distribution inside the permanent magnet cavity. The goal of this paper is to demonstrate how advanced TMS strategies can enhance operational efficiency and extend the lifespan of electric motors for aeronautic applications. The effectiveness of the radial tube configuration is assessed by means of advanced Computational Fluid Dynamics (CFD) analysis with the aim of verifying that the proposed design is able to maintain system thermal stability and prevent its overheating. Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. 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

Tourism’s impact on the physical environment has been discussed for almost 50 years. Tourism components, such as transport, accommodation, and consumption of activities, consume energy. However, little is known about whether particular visitor segments consume less energy, as the general focus tends to be on tourism in its entirety. Yet, some forms of tourism generate more carbon emissions than other types. Visiting Friends and Relatives (VFR) travel presents characteristics that could make it a suitable segment for destinations to consider targeting. This conceptual research article considers VFR travel through a sustainable tourism lens. This research aimed to examine the profiles and characteristics of VFR travel to understand whether and to what extent VFR travel may be a segment that has a comparatively lower impact on the environment. As a conceptual research article, it offers a theoretical foundation for empirical studies through introducing new ideas and creating a conceptual framework. Full article