Analysis of greenhouse gas emissions from electric vehicle considering electric energy structure, climate and power economy of ev: A China case

Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity generation mix: A global perspective

Well-to-wheel greenhouse gas emissions of electric versus combustion vehicles from 2018 to 2030 in the US

This study analyzed the greenhouse gas (GHG) emissions from the transportation sector in Korea from 1990 to 2013 using Logarithmic Mean Divisia Index (LMDI) factor decomposition methods. We decomposed these emissions into six factors: The population effect, the economic growth effect due to changes in the gross domestic product per capita, the energy intensity effect due to changes in energy consumption per gross domestic product, the transportation mode effect, the energy mix effect, and the emission factor effect. The results show that some factors can cause an increase in GHG emissions predominantly influenced by the economic growth effect, followed by the population growth effect. By contrast, others can cause a decrease in GHG emissions, predominantly via the energy intensity effect. Even though the transportation mode effect has contributed to a reduction of GHG emissions, it remains relatively small compared to other factors. The energy mix and emission factor effects contributed to the reduction of GHG emissions in the early 2000s, however the effects have led to an increase of GHG emissions since the mid-2000s. Altogether, based on these results, this study suggests some GHG mitigation policies aimed at achieving the national target for this sector.

Driving a sustainable road transportation transformation

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

Simulation of GHG Emissions for Production Phase of Battery Electric and Hydrogen Fuel Cell Electric HDVs under Different Electricity Grid Mixes

At present, urban greenhouse gas (GHG) emissions from different wastewater treatment stages are attracting increasing attention. Based on the Guidelines of the China Greenhouse Gas List Compilation (Trial) and the IPCC National Greenhouse Gas List Guidelines in 2006, this paper evaluated urban GHG emissions from wastewater treatment in China from 2011 to 2020. The contribution rates of GHG emissions to the total GHG emissions were calculated for the different wastewater treatment stages. The variations in annual GHG emissions and differences in GHG emissions among different regions and provinces were also analyzed. The total amount of equivalent CO2 emissions reaches 1478.51 million tons, and the annual average amount of equivalent CO2 emissions from 2011 to 2020 is 147.9 million tons, which shows a trend of decreasing first and then increasing. The distribution of GHG emissions from wastewater treatment is uneven among provinces and regions; Guangdong Province has the highest emission, while the Xizang autonomous Region has the lowest. The correlation and contribution rate analysis revealed that paper production and chemical and side food production could discharge a large amount of wastewater with a high COD content, which may have an important impact on GHG emissions during the wastewater treatment stages. According to the study results, CH4 accounts for the largest proportion (63.08%) of the total GHG emissions. The most important source of CH4 comes from the industrial wastewater treatment stage. The annual average CO2 emissions account for 22.24% of the total GHG emissions, which are mainly from the power and chemical consumption stage. The annual average N2O emissions account for 14.68% of the total GHG emissions and are mainly from the wastewater collection and discharge stage. Therefore, in the future, GHG emission reduction strategies should focus on CH4 emissions in the industrial wastewater treatment stage and develop CH4 recycling and utilization technologies.

Statistical analysis of greenhouse gas emissions of South Korean residential buildings

Feeding strategies and manure management for cost-effective mitigation of greenhouse gas emissions from dairy farms in Wisconsin

As the transportation sector is one of the main emitters of large quantities of pollutions to the atmosphere, industries have been trying to cope with this issue and launch many campaigns or projects to reduce air pollutions. In any industries around the world, an electric vehicle is a part of alternative transportation mode which has recently experienced considerable growth. The paper aims to evaluate energy consumption and greenhouse gas (GHG) emissions of the food sector for the entire life cycle and particularly focusing on green road transportation. The focus of the analysis covers the mango powder drink mix transportation, distribution, and disposal aspects, especially for road transportation. The observed results showed that the electric vehicles have emissions reduction potential and consequently showed low impacts in Global Warming Potential (GWP) impact category. The environmental impact assessment identified that the primary source of energy use and GHG emissions was the transportation process from Hong Keaw plantation to King Mongkut’s Institute of Technology Ladkrabang (KMITL) (0.025 kgCO2eq).

A whole farm systems analysis of greenhouse gas emissions of 60 Tasmanian dairy farms

In recent years, the issue of climate change has gained significant attention and become a focal point of discussion in various sectors of civil society. Governments, individuals, and scientists worldwide are increasingly concerned about the observed changes in climate patterns, often attributed to the rising levels of greenhouse gases. In this context, the main objective of this study is to assess the greenhouse gas emissions associated with the railway system in the state of Pernambuco, Brazil, and compare them with other national case studies, aiming to obtain greenhouse gas emission parameters specific to the railway system and propose mitigation models to address this environmental impact in the air. To achieve this goal, a comprehensive life cycle assessment (LCA) methodology was employed to examine the life cycle of the Pernambuco Metro. This involved conducting an inventory of resource inputs and emissions using actual observed data. Additionally, a comparative analysis of greenhouse gas emissions across different urban rail transport systems is presented to provide valuable contextual insights. The study findings reveal that the total greenhouse gas emissions from the Pernambuco rail system amount to 6170.54 t CO2e. Considering a projected total service life of 50 years, the estimated greenhouse gas emissions for the entire life cycle of the system’s operation and maintenance reach 308,550 t CO2e. The interdisciplinary nature of this research highlights the significance of studying the atmospheric effects of the Pernambuco railway system as a crucial parameter for designing strategies and technologies aimed at reducing air pollution within the region. Through quantifying and analyzing the greenhouse gas emissions of the Pernambuco rail system, this study provides valuable insights that contribute to addressing concerns related to climate change and promoting sustainable practices. It underscores the importance of developing effective strategies to mitigate air pollution and facilitates informed decision-making for the future of urban transportation systems.

LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector

Life cycle analysis of greenhouse gas emissions from organic and conventional food production systems, with and without bio-energy options

Greenhouse Gas Emissions of Electric Vehicles Associated with Wind and Photovoltaic Electricity