Search Results (65)

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

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

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

The aviation sector is a significant contributor to greenhouse gas emissions, and with the increasing demand for air travel these emissions are projected to continue rising in the coming years. Sustainable Aviation Fuel (SAF) could greatly help reduce these emissions and make the aviation industry more eco-friendly. SAF is a renewable, low-carbon alternative to conventional jet fuel produced from sustainable resources. A key step to bringing the fuel into regular use is studying how people view it. Understanding what the public think and feel about biofuels, including aviation fuel, is very important. This is because public opinion can shape consumer interest, demand for products, and the willingness of governments to back green energy policies and invest in clean technologies. The study systematically evaluates the public opinion, perception and awareness of SAF in the South Central United States and its utilization to decarbonize the aviation industry. This is performed through a series of multiple-choice survey questions and interviews. The study results show that while there is some recognition of the environmental impact of aviation and the potential role of biofuels in reducing this impact, there is still a need for greater public education and awareness regarding alternative fuels and their benefits for sustainable aviation. The findings of the study underscore a pivotal challenge in addressing aviation-related carbon emissions: the gap in public knowledge about potential solutions like biofuels and SAF. This gap not only reflects a lack of awareness but also hints at the possible skepticism or uncertainty among the public regarding the effectiveness and viability of these alternatives. Full article

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies. Full article

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019–2020. Results indicate that NOx and CO₂ emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R² value of 0.96 and 0.97, respectively, for predicting PM_2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM_2.5 (−0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO₂ emissions decreasing by 28.1%, NOx by 26.5%, and PM_2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM_2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution. Full article

An important aspect of a well-designed urban form is supporting active school travel by adolescents, as it has positive effects on physical activity, healthy lifestyles, and reducing vehicle-related carbon emissions. To achieve this, it is necessary to provide sufficient shading and fewer detours on home–school routes, especially in an era of frequent heatwaves. Analyzing the school travel environment at the city scale is essential for identifying practical solutions and informing comprehensive urban policy-making. This study proposes a framework for investigating, assessing, and intervening in home–school routes in Nanjing, China, emphasizing a dual assessment of commuting routes based on the pedestrian detour ratio and shading ratio. This work reveals that approximately 34% of middle school households in Nanjing face challenges in walking to and from school, with only 24.18% of walking routes offering fewer detours and sufficient shade. We advocate reengineering urban forms by reducing barriers to facilitate shortcuts, thereby providing school-age students with better access to cooler and healthier environments, aiming to promote walking and reduce car dependence. The findings may encourage more families to engage in active commuting and serve as a lever to drive school decarbonization and combat climate warming. Our work, with transferability to other cities, can assist urban designers in piloting urban (re)form incrementally and pragmatically to promote sustainable urban agendas. Full article

Background. Despite dentistry’s alarmingly high energy use, plastic waste, and travel emissions, research on Romanian dental students’ sustainability awareness is absent. This study aimed to assess their knowledge of the environmental impact of dental materials and practices, hypothesizing that early exposure to sustainability education would benefit preclinical students most. Materials and Methods. A cross-sectional survey using a form questionnaire with 15 items was conducted on 1800 dental students at Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, for one week in March 2022. The questionnaire, consisting of socio-demographics, students’ perspectives on sustainability in dentistry, and personal sustainability, was analyzed using SPSS 26. Data analysis included the Shapiro–Wilk test for normality, Fisher’s exact test for categorical variables, the Mann–Whitney U test for non-parametric quantitative comparisons, and Z-tests with Bonferroni correction for contingency tables. Results. A response rate of 26.06% was achieved, with 469 participants. The majority (51.1%), particularly males (66.1%), perceived sustainability as promoting durability. The most common definition of sustainability (33.8%) was related to environmental protection, with significantly higher agreement among female students (39.4%) (p = 0.001). While 49.3% of participants identified single-use plastics in patient care as having the greatest environmental impact in dental practices, 39.2% of female students, primarily from clinical study years (50%), ranked patient paperwork and records as the most significant factor (p = 0.031). The highest-carbon-footprint dental procedures were considered to be amalgam and composite fillings (50.7%), with clinical year students indicating this as the most relevant issue (62.8% vs. 47.7%) (p = 0.011). Students aged 25–30 were more actively engaged in sustainability initiatives compared to the younger group (p = 0.005), while all students over 30 identified scaling and polishing as the most impactful procedure (p < 0.001). A majority of students supported future university sustainability initiatives (62.7%) and an elective course on sustainability in dentistry (65%). Female students showed significantly greater interest than male students in both initiatives (66.3% vs. 52.7%, p = 0.003 and 70.8% vs. 49.6%, p < 0.001, respectively). Conclusions. Greater awareness of sustainability was found in preclinical-year dental students and among female students, with knowledge gaps in clinical-year students, particularly regarding the environmental impact of dental practices and materials. Introducing sustainability courses could better prepare future dentists for sustainable practices in dentistry. Research collaborations and curriculum reforms to further promote sustainability would also be beneficial. Full article

This study investigates the influence of urban tourists’ behaviour on the environmental performance of a destination, particularly in terms of carbon emissions. Tourist-related emissions are shaped by their choices and behaviours, impacting the overall carbon footprint of the locations they visit. To assess this impact, we introduce a methodology for quantifying greenhouse gas emissions linked to tourists’ energy consumption. This approach considers key tourism components—activities, accommodation, and transportation—analysing their roles in emissions across a trip’s temporal and spatial dimensions. By integrating tourists’ spatiotemporal behaviour with emissions data, our framework offers insights that can support local climate-responsive urban and tourism policies. We empirically apply the proposed model to the destination of Donostia/San Sebastián (Spain), where the primary travel sequences of visitors are analysed. We utilise cartographic techniques to map the environmental footprints of different tourist profiles, such as cultural and nature tourists. The findings indicate that visitors primarily motivated by nature and outdoor recreation constitute the segment with the highest greenhouse gas emissions (with a minimum footprint of 30.69 kg CO₂-equivalent per trip), followed by cultural tourists, and finally, other categories of visitors. The results highlight the practical applications of the proposed model for sustainable tourism management, providing valuable guidance for urban planners and policymakers in mitigating the environmental impacts of tourism. Full article

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices. Full article

Due to the limited availability of medical facilities and the urgency and irreplaceability of medical-seeking behaviors, the transportation processes used to access these resources inherently result in high carbon emissions. Unfortunately, pediatric medical facilities are among the least substitutable destinations, making it challenging to reduce travel-related CO₂ emissions by traditional means such as decreasing travel frequency or optimizing transportation means. This study proposes enhancing the spatial allocation of pediatric medical facilities to effectively reduce travel-related CO₂ emissions. This study selects 27 hospitals with pediatric departments in Tianjin as the research subject. It introduces a model for measuring travel-related CO₂ emissions for pediatric medical-seeking, STIRPAT, and ridge regression models as well as conducts simulations under various scenarios to test the hypotheses. Therefore, methods for enhancing the spatial allocation of pediatric medical facilities are proposed. The results show that (1) travel-related CO₂ emissions for pediatric medical-seeking are the highest in the city center, outpatient-related CO₂ emissions surpass inpatient ones, and children’s hospital-related CO₂ emissions are higher than those related to comprehensive hospitals, from which potential carbon reduction points can be explored; (2) children’s hospitals with multibranch and composite functional allocations can significantly reduce CO₂ emissions; (3) comprehensive hospitals can further alleviate CO₂ emissions from children’s hospitals by enhancing the medical level, transportation infrastructure, population distribution, and other spatial environmental factors; (4) from the perspective of low-carbon travel and equity, a spatial allocation strategy should be adopted for children’s hospitals that includes multiple branches and composite functions, while comprehensive hospitals should focus on service capacity, parity, supply–demand ratio, and the population density of children. Full article

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change is now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has the potential to simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes for motorised transport. The ELEVATE study aims to understand the impacts of e-micromobility, including identifying the people, places, and circumstances where they will be most beneficial in terms of improving people’s health while also reducing mobility-related energy demand and carbon emissions. A complex mixed methods design collected detailed quantitative and qualitative data from multiple UK cities. First, nationally representative (n = 2000), city-wide (n = 400 for each of the three cities; total = 1200), and targeted study area surveys (n = 996) collected data on travel behaviour, levels of physical activity, vehicle ownership, and use, as well as attitudes towards e-micromobility. Then, to provide insights on an understudied type of e-micromobility, 49 households were recruited to take part in e-cargo bike one-month trials. Self-reported data from the participants were validated with objective data-using methods such as GPS trackers and smartwatches’ recordings of routes and activities. CO₂ impacts of e-micromobility use were also calculated. Participant interviews provided detailed information on preferences, expectations, experiences, barriers, and enablers of e-micromobility. Full article

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting). Full article

The increasing number of vehicles operating in Poland, especially passenger vehicles, justifies the need to conduct air pollution emission tests in the context of the impact of vehicles on the natural environment. Firstly, this article reviews the publications related to air pollutant emissions and passenger vehicles traveling on Polish roads. However, it presents a special method using advanced research equipment to determine air pollutant emissions. The above research methods are justified in implementing clean transport zones. Real Driving Emissions represent an essential procedure in the implementation of clean transport zones in Poland, verifying the actual emissions of air pollutants and modeling this phenomenon using the results of real air pollutant emissions. The results of this research state that establishing a link between a vehicle’s air pollutant emissions and its age can support making transport or delivery planning more sustainable and choosing less carbon-intensive means of transport to reduce the negative impact of transport on the environment. The scientific novelty of the proposed solutions is the verification of the actual emissions of Euro 6 vehicles and the modeling of air pollutant emissions as a function of speed and acceleration. The research results are included in this article and will become input data for further analysis in examining the impact of vehicle operating age on air pollution emissions. Consequently, the novelty of the present research also lies in its focus on the verification of the impact of operating age, particularly in the context of vehicles exceeding 15 years of age, on air pollutant emissions. By establishing a correlation between a vehicle’s air pollutant emissions and its operating age, it becomes possible to make transport or delivery planning more sustainable. Furthermore, the selection of less carbon-intensive means of transport can contribute to reducing the negative impact of transport on the environment. Full article