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This paper aims at understanding and evaluating the environmental and economic impacts of a crowdshipping platform in urban areas. The investigation refers to the city of Rome and considers an environmental-friendly crowdshipping based on the use of the mass transit network of the city, where customers/crowdshippers pick-up/drop-off goods in automated parcel lockers located either inside the transit stations or in their surroundings. Crowdshippers are passengers that would use the transit network anyhow for other activities (e.g., home-to-work), thus avoiding additional trips. The study requires firstly, estimating the willingness to buy a crowdshipping service like the one proposed here, in order to quantify the potential demand. The estimation is realized adopting an extensive stated preference survey and discrete choice modeling. Then, several scenarios with different features of the service are proposed and evaluated up to 2025 in terms of both externalities (local and global pollutant emissions, noise emissions and accidents reductions) and revenues. The results are useful to understand and quantify the potential of this strategy for last mile B2C deliveries. Moreover, it provides local policy-makers and freight companies with a good knowledge base for the future development of a platform for public transport-based crowdshipping and for estimating the likely impact the system could have both from an economic and environmental point of view.

This article aimed to assess the accuracy of the COPERT model in predicting CO2 emissions and energy consumption in real operating conditions, represented by the WLTP homologation tests. Experimental data obtained for a Euro 6 vehicle were compared with the values estimated by the COPERT model, assuming identical speed conditions. MLP and SANN artificial neural networks were also used to create a model describing the complex relationships between emissions, speed, and energy consumption. The results indicate an apparent overestimation of CO2 and energy consumption values by the COPERT model, especially in the low-speed range typical of urban traffic. The minimum energy consumption values were observed at speeds of 50–70 km/h, indicating the existence of an optimal drive system operation zone. The neural models showed high efficiency in predicting the tested parameters—the best results were obtained for the MLP 6-10-1 architecture, whose correlation coefficient exceeded 0.98 in the validation set. The paper highlights the need to calibrate the COPERT model using local experimental data and integrate artificial intelligence methods in modern emission inventories.

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

The increasing number of heavy-duty vehicles (HDVs) on roads has become a major contributor to harmful emissions, posing critical environmental challenges and exacerbating global warming. This study aims to establish correlations between road types and the emissions they generate, offering actionable insights for logistics planning and strategies to mitigate diesel vehicle emissions. The analysis is based on input data from a selected transport company, covering parameters such as vehicle type, average mileage, speed, and driving style, as well as environmental conditions like ambient temperature and humidity. Emissions and energy consumption levels are estimated using the COPERT model. A key research challenge involves accurately predicting and managing air pollution caused by HDVs under varying vehicular, technological, and fuel conditions, as well as fluctuating atmospheric and operational factors. The findings indicate that highway driving produces the highest emissions of pollutants such as Se and Zn, while urban peak hours record the highest levels of NOx, NO, and NO2. These results emphasise the critical role of strategic route selection in reducing total emissions and managing levels of individual harmful substances. This research highlights the importance of integrating sustainable practices into transport planning to reduce environmental impacts, align with global climate objectives, and advance sustainable development in the transport sector.

Transformation of European transport belongs among the key challenges to achieve a reduction of 55% by 2030 and climate neutrality by 2050. This study focuses on GHG emissions in road transport in Slovakia, as it currently accounts for 19% of total GHG emissions (road transport emissions account for 99% of transport emissions). The main driver for this study was the preparation of Slovakia’s Climate Act and investigation of where are the limits of greenhouse gas emission reduction by 2050. With the aim of achieving maximum reduction in emissions by 2050 compared to 2005 levels, various scenarios were developed using the COPERT model to explore emission reduction strategies. The scenarios considered different subsectors of road transport, including passenger cars, light-commercial vehicles, heavy-duty vehicles (buses and trucks), and L-category vehicles and examined encompassed reduction of transport demand, improving energy efficiency, and utilizing advanced technologies with alternative fuels (hybrids, PHEV, CNG, LNG or LPG). However, the economic aspects of specific mitigation options were not considered in this analysis. The results show that there is a possibility of 77% GHG emission reduction by 2050 in comparison with the 2005 level. This reduction is accompanied by a shift in vehicle technologies to alternative fuels like electricity, hydrogen, and to a smaller extent biofuels and biomethane. This study shows that it will be possible to achieve 86.7% zero-emission cars and an additional 12.9% low emission and alternative fueled cars by 2050. By identifying and assessing these scenarios, policymakers and stakeholders can gain insights into the possibilities, challenges, and potential solutions for meeting the climate targets set by the European Union’s Fit for 55 climate package.

The environmental impacts of road transport, in particular air pollution and noise, are receiving increasing attention in urban and regional planning, as they can not only predict vehicle movements but also provide detailed information on traffic volumes and speed distributions, which are indispensable for effective regulation, targeted interventions and health-conscious urban planning. This study presents an emission calculation module that can be integrated into traffic models and provides detailed estimates of pollutants emitted by road vehicles. The developed module builds on the COPERT methodology, which accounts not only for exhaust emissions such as CO2, NOx and PM, but also for non-exhaust emissions from brake wear, tyre wear, road abrasion and evaporation. The presented system has an open architecture, enabling further customisation, particularly when local measured data are available. This contributes to building a stronger, data-driven link between transport planning and environmental protection.

Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies.

Heavy‐duty diesel vehicles (HDDVs), though few in number, are major polluters in East Africa, producing significant amounts of CO 2 , CO NO x , PM, and PM 10 . Their rising numbers along the bustling northern corridor, fueled by economic activity, raise concerns. This paper provides an in‐depth analysis of the emission trends and projections of HDDVs along the northern corridor, employing the COPERT model for emission estimations from 2015 to 2020 and the ARIMA model for emission predictions from 2021 to 2030. Through detailed analysis, the research illustrates the dynamics of emissions across different vehicle categories: rigid trucks (14–32 t) and articulated trucks (14–60 t), noting the effectiveness of emission control technologies and the impact of evolving emission standards from EURO I to EURO II. A significant finding is the potential advancements in reducing particulate matter emissions per vehicle by up to 20%, contrasting with the rising trends in CO 2 emissions of 344% (80.62 to 357.81 kt) that pose challenges for climate change mitigation efforts. Forecasting using the ARIMA model reveals an anticipated significant decline in total emissions from 2021 to 2030 by up to 30%, suggesting a positive outlook toward achieving lower pollution levels through adherence to stricter emission standards and the adoption of cleaner technologies. The paper also addresses the inherent uncertainties in emission estimates, advocating the development of adaptable and resilient environmental policies to effectively respond to a wide range of emission scenarios. This study contributes valuable information on the environmental and public health implications of HDDV emissions, offering a comprehensive understanding that can inform policymaking and technological advancements in the search for sustainable transportation solutions.

Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.

The multimodal North Sea–Baltic corridor, consisting of 6934 km of road, is an integral part of the EU’s trans-European transport network. However, an unsatisfied level of development of alternative fuels infrastructure for road transport is considered one of the obstacles to connecting northern Member States and North-East countries. A “what-if” scenario was employed to obtain useful insights into how a given situation might be handled, and a comparison of several paths forward to make better decisions was analysed. Environmental insights for transportation sector scenarios in 2030–2035 were explored and analysed using the COPERT v5.5.1 software program. In this study, the installation of natural gas infrastructures of various station sizes and with varying capacities and types of natural gas (LNG, CNG, bio-methane) dispensed was evaluated in detail. Replacement of the existing HDV fleet (heavy-duty vehicles) with LNG-powered trucks would result in the following investment to upgrade the existing network and build new stations to meet rising LNG demand: from €21.47 to €32.3 million (the scenario of 10% market share for HDVs running on LNG), €42.94 to €64.6 (20%), and €64.4 to €96.9 (30%). The dual-fuel 10–diesel fuel 90% scenario seems to be the safest option for a large-scale investment until 2035 which may lead to moderate emission savings of 84.6 kton CO2 eq. compared to 2022 levels.