A Systemic View of Future Mobility Scenario Impacts on and Their Implications for City Organizational LCA: The Case of Autonomous Driving in Vienna

Autonomous vehicles (AV) are expected to significantly reshape urban mobility. Whether advancements at vehicle level also translate into positive environmental outcomes at city level is still uncertain. We investigate under which conditions a city could enable low emission AV mobility and what challenges are to be expected along the way from an environmental point of view. We build upon our recent environmental performance study of Vienna and combine city organizational life cycle assessment (city-OLCA) with AV transport models from literature for three AV use cases: an own AV, a shared AV, and a shared AV ride service. Most cases lower Vienna’s passenger capacity (by up to 28%) and increase motorized road traffic by a maximum of 49% (own AVs). Traffic relief is observed for shared AVs (−40%) if accompanied by a conventional car ban. This case reduces transport related GHG emissions compared to both Vienna’s current baseline (−60%) and a future electrified transportation system (−4.2%). These transformations have also shifted emission responsibility to the public level. While Vienna’s total GHG emissions could be reduced by 12%, the city’s emission responsibility increases from 25% to 32%. Efficient mass transit, the electrification of the mobility sector and grid decarbonization are key to reducing transport emissions in Vienna. The direction of GHG emission development will be determined by the extent to which these conditions are promoted. AV mobility probably will not be a main contributor.

Understanding the costs, benefits, mitigation potentials and ethical aspects of New Zealand’s transport emissions reduction policies

<p>Greenhouse gas emissions from New Zealand’s road transport sector have been increasing rapidly since 1990. Between 1990 and 2017, New Zealand’s gross greenhouse gas emissions increased by 23.1% while emissions from the road transport sector increased by 82%; rising to 15.9 MtCO2e in 2017 from about 8.8 MtCO2e in 1990. To reduce transport emissions, the government has undertaken various initiatives including electric vehicle support, introduction of an emissions trading scheme (ETS), promotion of biofuel and other alternative fuels, and announcement of a feebate scheme. However, even though some of these policies require time to take effect, it is evident from the increase in emissions that there has so far been little progress in terms of transport emissions reduction. This raises questions over the acceptability and effectiveness of the policies taken by the government. Given the pressing need to reduce transport emissions globally and in New Zealand in particular, the present study initially investigates the major drivers of transport emissions from among a set of likely drivers, using a causality test. Because electric vehicles are widely seen as an obvious ‘solution’ within the sector, this study next examines the costs and mitigation potential of electric vehicles in the New Zealand context in order to understand the uncertainties, risks, barriers, costs, and policy gaps associated with their widespread adoption. Next, this study examines the scope for an increased carbon price signal to curb emissions growth. Finally, this study takes the view that technological and price instruments have to be seen within a wider range of possible transport policy measures, some of which may be complementary. The study therefore elicited the perspectives of a number of transport experts, and NGO and green energy activists. It ranked six mitigation policy pathways and 26 policy options on the basis of experts’, and NGO and green energy activists’ preferences. Findings of this study include that poor vehicle fuel economy is the major driver of transport emissions in New Zealand. Policies such as a high minimum vehicle fuel economy standard and/or feebate scheme could effectively help New Zealand reduce its transport emissions significantly. Electric vehicles (EVs) are also found to be potentially very effective in reducing emissions as around 80-85% of New Zealand’s electricity comes from renewable generation. Moreover, in terms of the ownership costs of using EVs, used EVs are now the most cost competitive among various vehicle types such as new EVs, used internal combustion engine vehicles (ICEVs) and new ICEVs. An increase in the carbon price to around NZD 235 per tonne of carbon dioxide (tCO2) is also likely to help the transport sector reduce its emissions by 11% from the 1990 level and achieve the Paris target. However, according to experts’, and NGO and green energy activists’ preferences, EV support and an increased carbon price are not the most preferred emissions reduction options. Some experts, and NGO and green energy activists viewed EV subsidization, EV free parking and EV access to high occupancy lanes as unethical because EVs are mostly used by high-income people whereas low-income people often use bus or low-cost used cars. Likewise, some experts, and NGO and green energy activists did not prefer an increased carbon price because the impact of such a policy would be uneven, and low-income people would be hurt severely compared to high-income people. Results demonstrate that active and public transport support and travel demand management are the most preferred options. Since New Zealand roads are not wide enough to support a high level of individual car use both in the short and the long run, most experts, and NGO and green energy activists preferred active and public transport under current and future circumstances. Policies related to bio-fuel support were least preferred because most experts, and NGO and green energy activists think an increased production and use of biofuels is likely to replace existing forestry and farm activity and decrease food production and forestry. It is hoped that the findings of this study will help to better illuminate the difficult policy options facing policy makers and work to assist them in identifying the most acceptable policies and projects for investment.</p>

Understanding the costs, benefits, mitigation potentials and ethical aspects of New Zealand’s transport emissions reduction policies

<p>Greenhouse gas emissions from New Zealand’s road transport sector have been increasing rapidly since 1990. Between 1990 and 2017, New Zealand’s gross greenhouse gas emissions increased by 23.1% while emissions from the road transport sector increased by 82%; rising to 15.9 MtCO2e in 2017 from about 8.8 MtCO2e in 1990. To reduce transport emissions, the government has undertaken various initiatives including electric vehicle support, introduction of an emissions trading scheme (ETS), promotion of biofuel and other alternative fuels, and announcement of a feebate scheme. However, even though some of these policies require time to take effect, it is evident from the increase in emissions that there has so far been little progress in terms of transport emissions reduction. This raises questions over the acceptability and effectiveness of the policies taken by the government. Given the pressing need to reduce transport emissions globally and in New Zealand in particular, the present study initially investigates the major drivers of transport emissions from among a set of likely drivers, using a causality test. Because electric vehicles are widely seen as an obvious ‘solution’ within the sector, this study next examines the costs and mitigation potential of electric vehicles in the New Zealand context in order to understand the uncertainties, risks, barriers, costs, and policy gaps associated with their widespread adoption. Next, this study examines the scope for an increased carbon price signal to curb emissions growth. Finally, this study takes the view that technological and price instruments have to be seen within a wider range of possible transport policy measures, some of which may be complementary. The study therefore elicited the perspectives of a number of transport experts, and NGO and green energy activists. It ranked six mitigation policy pathways and 26 policy options on the basis of experts’, and NGO and green energy activists’ preferences. Findings of this study include that poor vehicle fuel economy is the major driver of transport emissions in New Zealand. Policies such as a high minimum vehicle fuel economy standard and/or feebate scheme could effectively help New Zealand reduce its transport emissions significantly. Electric vehicles (EVs) are also found to be potentially very effective in reducing emissions as around 80-85% of New Zealand’s electricity comes from renewable generation. Moreover, in terms of the ownership costs of using EVs, used EVs are now the most cost competitive among various vehicle types such as new EVs, used internal combustion engine vehicles (ICEVs) and new ICEVs. An increase in the carbon price to around NZD 235 per tonne of carbon dioxide (tCO2) is also likely to help the transport sector reduce its emissions by 11% from the 1990 level and achieve the Paris target. However, according to experts’, and NGO and green energy activists’ preferences, EV support and an increased carbon price are not the most preferred emissions reduction options. Some experts, and NGO and green energy activists viewed EV subsidization, EV free parking and EV access to high occupancy lanes as unethical because EVs are mostly used by high-income people whereas low-income people often use bus or low-cost used cars. Likewise, some experts, and NGO and green energy activists did not prefer an increased carbon price because the impact of such a policy would be uneven, and low-income people would be hurt severely compared to high-income people. Results demonstrate that active and public transport support and travel demand management are the most preferred options. Since New Zealand roads are not wide enough to support a high level of individual car use both in the short and the long run, most experts, and NGO and green energy activists preferred active and public transport under current and future circumstances. Policies related to bio-fuel support were least preferred because most experts, and NGO and green energy activists think an increased production and use of biofuels is likely to replace existing forestry and farm activity and decrease food production and forestry. It is hoped that the findings of this study will help to better illuminate the difficult policy options facing policy makers and work to assist them in identifying the most acceptable policies and projects for investment.</p>

Tourism and Road Transport Emissions in Italy

Tourism is of great importance to European economies, but environmental degradation could reduce the attractiveness of many European destinations considerably. This is even more evident if the future of tourism is depicted in the UN’s Agenda 2030 for Sustainable Development. However, official statistics on the environmental impact of tourism provide only partial information, and almost always with an “accounting scheme” approach, such as occasional and experimental experiences on integrated economic and environmental accounts of tourism. It is necessary to enrich the activity of monitoring and measuring the impact of tourism on the environment and implement policies aimed at increasing the sustainability of the sector. This work intends to contribute to extending information about the theme, providing a new approach based on the integration of official data to study the relationship between tourism and the environment. In detail, the objective of the work is to estimate the level of emissions—in terms of the primary air pollutants—produced by tourists travelling in Italy by road transport in the period 2015–2019. Even if much has to be done to improve the knowledge on the tourism–environment nexus, this paper represents a first relevant step towards an approach that can be easily implemented in all EU countries.

Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.

The impact of road transport emissions on air quality in Brăila, Romania

Road transport, including accessibility and individual mobility is considered unanimously as a fundamental element of contemporary living. The study area is considering Braila County with a total population of around over 305,000. The area it is well served by 6 national roads, 27 county roads and 42 communal roads and contains some of the most heavily trafficked stretches of road in the Romania. The emissions analysed in this study CH4, CO, CO2, N2O, NH3, NOx, PM2.5 and PM10, were collected by the Agency for Environmental Protection Braila during 2015-2019 based on questionnaires according to EMEP/EEA air pollutant emission inventory guidebook. The highest level of pollutant emissions was recorded in 2017, more exactly 191714,5 Megatons. In this article we analysed five categories of pollution sources: Passenger car, Light commercial trucks, Heavy-duty vehicles, Motorcycles and Non - Road vehicles and other mobile equipment. With the exception of CO2, N2O and NH3, pollutant emissions decreased for the eight pollutants analysed.

Influence of anthropogenic impact of vehicles on roadside forest plantations

The article presents studies of the growth and development of forest stands along highways as a result of man-made impacts from road transport emissions. The obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonities of roadside stands during the period of light saturation is presented. In this regard, the obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonitets of roadside forest stands during the period of light saturation is presented. The use of the bonus in research to characterize the growth rate of forest roadside plantings depending on the distance to highways and the density of traffic flows on them allows us to characterize the amount of toxic pollutants entering forests. This allows us to assess the process of expanding the environmentally unfavorable zone along the highway. The article presents the possibility of calculating the concentration of pollutants, based on the model of turbulent diffusion, reduced, after some assumption, to the model of Gaussian distribution in atmospheric air. The dependence on the calculation of the intensity of emissions of pollutants, taking into account the composition of the traffic flow, is given.

Development and Evaluation of Simulation-Based Low Carbon Mobility Assessment Models

The transport sector is a significant contributor to global emissions. In Australia, it is the third largest source of greenhouse gases and is responsible for around 17% of emissions with passenger cars accounting for around half of all transport emissions. Governments at all levels have identified a need for a reduction in transport carbon emissions to meet their net zero emissions targets. This research aims to help decision makers estimate the carbon footprint of transport networks within their jurisdictions and evaluate the impacts of emission-reduction interventions, through development of a simulation-based low carbon mobility assessment model. The model was developed based on a framework that integrates multiple mobility components including individual travel preferences, traffic simulation, and an assessment interface to create a seamless tool for the end-user. The feasibility of the assessment model was demonstrated in a case study for a local city council in Melbourne. In one of many scenarios reported in this paper, the model showed that maintaining current levels of emissions would require a 20% reduction in vehicle trips by 2030, and a much larger reduction would be required to reduce the levels of greenhouse gas emissions and achieve desired emissions reduction targets. The paper concludes with recommendations and future directions to extend the model’s capabilities and applications.

Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation

Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO₂ emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO₂ emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO₂ emissions. Potential strategies and policies to reduce air travel demand and its CO₂ emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO₂ emissions from transport by 7.5%.