Application of a Adaptive Neuro-Fuzzy Technique for Projection of the Greenhouse Gas Emissions from Road Transportation

The economic and health impacts resulting from the greenhouse effect is a major concern in many countries. The transportation sector is one of the major contributors to greenhouse gas (GHG) emissions worldwide. Almost 15 percent of the global GHG and over 20 percent of energy-related CO2 emissions are produced by the transportation sector. Quantifying GHG emissions from the road transport sector assists in assessing the existing vehicles’ energy consumptions and in proposing technological interventions for enhancing vehicle efficiency and reducing energy-supply greenhouse gas intensity. This paper aims to develop a model for the projection of GHG emissions from the road transport sector. We consider the Vehicle-Kilometre by Mode (VKM) to Number of Transportation Vehicles (NTV) ratio for the six different modes of transportation. These modes include motorcycles, passenger cars, tractors, single-unit trucks, buses and light trucks data from the North American Transportation Statistics (NATS) online database over a period of 22 years. We use multivariate regression and double exponential approaches to model the projection of GHG emissions. The results indicate that the VKM to NTV ratio for the different transportation modes has a significant effect on GHG emissions, with the coefficient of determination adjusted R2 and R2 values of 89.46% and 91.8%, respectively. This shows that VKM and NTV are the main factors influencing GHG emission growth. The developed model is used to examine various scenarios for introducing plug-in hybrid electric vehicles and battery electric vehicles in the future. If there will be a switch to battery electric vehicles, a 62.2 % reduction in CO2 emissions would occur. The results of this paper will be useful in developing appropriate planning, policies, and strategies to reduce GHG emissions from the road transport sector.

The paper gives special attention on long distance passenger transport and specific emissions related to different transport modes, particularly road and air transport sector. The goal of this research is creation and selection of appropriate methodology for modelling the cost estimation of GHG emissions in road and air transport sector for Republic of Serbia as well as the application of the methodology regarding to detailed calculation by transport mode and sub modes. Input data for road transport sector refer to the 2013 and include road and traffic conditions on the road network. Input data for air transport sector are related to the 2014 and international airport ‘Nikola Tesla’ Belgrade as the main hub point with the highest recorded number of aircraft operations in the Western Balkan countries. The obtained results reveal that, due to realized transport volume, diesel cars have the largest share of the costs of GreenHouse Gas (GHG) emissions within the passenger long distance road transport. Cost estimates of CO2 emissions in the air transport sector shows that A319 aircraft type have the major share in total costs. The reasons are twofold: first, a high level of Landing and Take-Off (LTO) emission factor for CO2 and second, largest number of LTO cycles.

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO2eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO2eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation.

Development and application of China provincial road transport energy demand and GHG emissions analysis model

How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?

Transitioning to net-zero greenhouse gas (GHG) emissions by 2050 is becoming increasingly urgent, requiring accelerated efforts to decarbonise all economic sectors, including transport, a growing emissions source. A transition to battery electric vehicles (BEV) would accelerate the decarbonisation of road transport and provide other benefits. But in Australia, BEV uptake has been negligible, and the scale and pace required to reach net-zero emissions by 2050 has not been addressed to date.This study applies a national-scale integrated macroeconomic model (iSDG-Australia) to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions by 2050. It models five scenarios incorporating different levels of economic and population growth, vehicle longevity, ambitions for BEV uptake, fleet renewal, forced phase-out of fossil-fuelled vehicles and shifts to renewable electricity. Scenario projections are benchmarked on their zero-emission vehicle mix, fuel and electricity consumption, GHG emissions, and broader social and economic impacts. We conclude the scale and pace of change must be transformational rather than transitional, requiring urgent policy action. An ambitious and rapid transition to 100% BEVs in new vehicle sales, accelerated fleet renewal, and a shift to renewable electricity generation could achieve a net-zero outcome for Australia’s road transport sector by 2050.

Driving a sustainable road transportation transformation

This study examined the relationship between urban compactness and greenhouse gas (GHG) emissions in the road transport sector in South Korea, focusing on 84 cities, particularly 27 metropolitan areas with populations of approximately 500,000. We developed an urban compactness index (UCI) using Moran’s I, entropy, and the Gini coefficient, integrating city size into the analysis. Cities were categorized into five groups based on their size to analyze GHG emissions and regional variations in compactness comparatively. Our results revealed a significant inverse relationship between UCI and per capita road transport GHG emissions, which was more pronounced in larger cities. Specifically, cities with a population over 1 million displayed reduced per capita road transport GHG emissions in compact urban structures. In conclusion, these findings suggest that larger cities can effectively reduce per capita road transport GHG emissions through urban planning for compact development. Additionally, planners need to consider city size when analyzing the UCI and formulating urban planning strategies aimed at achieving carbon neutrality.

One of the priorities of the “Europe 2020” strategy is to combat climate change and to reduce greenhouse gases (GHG) emissions. The key elements for the climate policy framework for the European Commission for 2020 are as follows: (1) reducing GHG emissions by 40% in comparison to the level in 1990; (2) increasing the share of renewable energy in the use of final energy to 27%; (3) increasing the energetic efficiency by 27%. Those are ambitious goals which will require the Member States to increase their efforts in all the sectors of the economy. In 2015 the GHG emissions in the EU fell by 23.7% in comparison to the level in 1990. All the sectors, apart from the transport sector contributed to the emission reduction in the years 1990–2015. The transport emission increased by 13.3% in that period in comparison to the year 1990, which is particularly worrisome. This is important because the fuels use in the transport sector contributed to approximately 20% of all the GHG emissions in the EU in 2015. The article presents the factors and the tools which significantly affect the achievement of the goals set in the Green Paper: a 2030 framework for energy and climate policies, which concern the transport sector and the indicated guidelines and instruments supporting them. The road transport will be extensively analysed as it is the transport mode which shows an extraordinary growth tendency and it is a vital barrier in the achievement of the goals set in the area of “Climate change and GHG emission reduction”. The article presents the results of the research, which show the impact of various identified tools on the achievement of the threepriorities of the climate policy. The multivariate analysis of variance (MANOVA) was used, in which the dependent variables were: the GHG emission levels, the use of renewable energy and the energy intensity of transport. The results were calculated based on the data from 28 Member States and the model was verified.

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.

Comment on “Consumer purchase intentions for electric vehicles: Is green more important than price and range?” K. Degirmenci, MH Breitner Transportation Research Part D 51 (2017) 250–260

This article proposes an integrated assessment methodology aimed at supporting decision-makers in design energy production scenarios to power a low emissions traffic fleet. The Multidimensional Air Quality (MAQ) system is used to define and solve a decision problem that selects a set of energy production scenarios minimizing costs, impacts on air quality, and greenhouse gases (GHGs) emissions. This study focuses on the road transport sector, that is responsible for 25% of European GHGs emissions and 39% of NOx emission, a precursor of both NO2 and PM10 concentrations. The electrification of the light vehicle fleet and the use of biomethane to power heavy vehicles are analyzed, estimating the electricity demand increase, exploring different energy production mixes, and assessing the impacts on air quality, costs, and GHGs according to the fuels/sources used to satisfy the energy demand. A case study over Lombardy region, in Northern Italy, is proposed.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Scenario analysis on alternative fuel/vehicle for China’s future road transport: Life-cycle energy demand and GHG emissions

Using panel data from 21 Organization for Economic Cooperation and Development (OECD) countries collected between 2000 and 2016, this study analyzes the effect of age structure on greenhouse gas (GHG) emissions from road transportation. Previous studies have failed to reflect the driver’s behavior patterns, especially by age group. We apply the Fully-Modified Ordinary Least Squares (FMOLS) method, including the age structure effect by reorganizing 17 age groups into a polynomial structure. The age structure exhibits an asymmetric inverted U-shaped effect on GHG emissions. Initially, people emit more GHGs as they age, and reach peak emissions in their late 20s, after which emissions fall until around the age of 70, when GHG emissions remain constant because of minimum mobility demand. Factors, such as higher income, increased vehicle ownership, and raised transport volumes increase emission rates. On the other hand, fuel transition and increased fuel price, population density, urbanization rate, and fuel economy reduce GHG emissions. Furthermore, we perform a projection of GHG emissions until 2050, and conclude that the effect of age structure is limited because of the minimum mobility demand of the elderly. We conclude that various policy measures, such as increased fuel economy and urbanization, must be considered in order to achieve sustainable transport