Analysis of energy use and greenhouse gas emissions (GHG) of transplanting and broadcast seeding wetland rice cultivation

The LanzaTech process can convert carbon monoxide-containing gases produced by industries, such as steel manufacturing, into valuable fuel products. The life-cycle analysis (LCA) of energy use and greenhouse gas emissions from the LanzaTech process has been developed for a Chinese setting using the original Tsinghua China Automotive LCA model along with a customized module developed principally for the process. The LCA results demonstrate that LanzaTech gas-to-liquid (GTL) processing in China’s steel manufacturing is favorable in terms of life-cycle fossil energy and can reduce greenhouse gas emissions by approximately 50% compared with the conventional petroleum gasoline. The LanzaTech process, therefore, shows advantages in both energy-savings and a reduction in greenhouse gas emissions when compared with most bio-ethanol production pathways in China.

This paper presents a detailed well-to-wheel analysis of energy use, greenhouse gas and criteria emissions for a prototype Parallel-Series Plug-in Hybrid Electric Vehicle (PHEV) developed as part of the EcoCAR 2 competition.

Electric vehicles (EVs) emit substantially fewer air pollutants than conventional internal combustion engine vehicles. However, the continuous increase in electricity demand from the power grid for EV charging, resulting from the growing adoption and total vehicle miles traveled, leads to higher greenhouse gas emissions from the power sector. This study presents a predictive analysis of energy sector greenhouse gas emissions from EV charging at the regional level across the United States under various projection scenarios of technology costs, fuel prices, demand growth, and electricity sector policies. The predictive modeling of greenhouse gas emissions from EV charging is performed using a machine learning model developed on the Meta Prophet platform designed to capture temporal patterns and seasonality with a high level of precision. Trained on simulation data from the Cambium model, developed by the National Renewable Energy Laboratory (NREL), our model provides accurate and continuous predictions of CO2, N2O, and CH4 emission rates from EV charging through 2050 under eight power generation planning scenarios, each outlining different projections for costs, prices, demand, and policy outcomes. Our analysis suggests that, by 2030, total grid emissions of CO2, N2O, and CH4 from EV battery charging in the United States are projected to decline by 52.67%, 65.71%, and 53.65%, respectively, compared to 2025 under the mid-case scenario, despite an overall increase of 152.57% in EV electricity demand. By 2050, emissions are expected to decline further by 80.13%, 94.15%, and 75.62%, respectively, with total EV electricity demand rising by 802.02%. Our scenario analysis results underscore the pivotal role of regional energy mixes and the pace of renewable energy deployment in decarbonizing transportation through electrification, highlighting the urgent need for proactive policy measures to accelerate the adoption of clean and renewable energy technologies in order to meet long-term climate targets.

Life-cycle analyses of energy use and greenhouse gas (GHG) emissions were carried out for three scenarios of the Coskata biomass-to-ethanol (EtOH) process under Chinese conditions using the original Tsinghua China Automotive Energy LCA Model in conjunction with a module developed particularly for the Coskata process. The results show that 1) the Coskata pathway has good performance in terms of life-cycle fossil energy use and GHG emissions; 2) the electricity used in the biomass-to-EtOH process has the most significant effect on life-cycle fossil-energy use, natural gas as boiler fuel has the second-greatest effect, while fuel used in feedstock transportation has the third; and 3) different energy resources for boilers in the biomass-to-EtOH factory provide different life-cycle results: coal is the least favorable while biomass is the most favorable. We conclude that 1) the Coskata pathway has the dual merits of fossil energy-savings and lower GHG emissions compared with all other bio-EtOH pathways and conventional gasoline in China; and 2) shifting from coal as the fuel for factory boilers to biomass will strengthen the advantages.

Plug-in hybrid electric vehicles (PHEVs) are being developed for mass production by the automotive industry. PHEVs have been touted for their potential to reduce the US transportation sector's dependence on petroleum and cut greenhouse gas (GHG) emissions by (1) using off-peak excess electric generation capacity and (2) increasing vehicles energy efficiency. A well-to-wheels (WTW) analysis - which examines energy use and emissions from primary energy source through vehicle operation - can help researchers better understand the impact of the upstream mix of electricity generation technologies for PHEV recharging, as well as the powertrain technology and fuel sources for PHEVs. For the WTW analysis, Argonne National Laboratory researchers used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne to compare the WTW energy use and GHG emissions associated with various transportation technologies to those associated with PHEVs. Argonne researchers estimated the fuel economy and electricity use of PHEVs and alternative fuel/vehicle systems by using the Powertrain System Analysis Toolkit (PSAT) model. They examined two PHEV designs: the power-split configuration and the series configuration. The first is a parallel hybrid configuration in which the engine and the electric motor are connected to a single mechanical transmission that incorporates a power-split device that allows for parallel power paths - mechanical and electrical - from the engine to the wheels, allowing the engine and the electric motor to share the power during acceleration. In the second configuration, the engine powers a generator, which charges a battery that is used by the electric motor to propel the vehicle; thus, the engine never directly powers the vehicle's transmission. The power-split configuration was adopted for PHEVs with a 10- and 20-mile electric range because they require frequent use of the engine for acceleration and to provide energy when the battery is depleted, while the series configuration was adopted for PHEVs with a 30- and 40-mile electric range because they rely mostly on electrical power for propulsion. Argonne researchers calculated the equivalent on-road (real-world) fuel economy on the basis of U.S. Environmental Protection Agency miles per gallon (mpg)-based formulas. The reduction in fuel economy attributable to the on-road adjustment formula was capped at 30% for advanced vehicle systems (e.g., PHEVs, fuel cell vehicles [FCVs], hybrid electric vehicles [HEVs], and battery-powered electric vehicles [BEVs]). Simulations for calendar year 2020 with model year 2015 mid-size vehicles were chosen for this analysis to address the implications of PHEVs within a reasonable timeframe after their likely introduction over the next few years. For the WTW analysis, Argonne assumed a PHEV market penetration of 10% by 2020 in order to examine the impact of significant PHEV loading on the utility power sector. Technological improvement with medium uncertainty for each vehicle was also assumed for the analysis. Argonne employed detailed dispatch models to simulate the electric power systems in four major regions of the US: the New England Independent System Operator, the New York Independent System Operator, the State of Illinois, and the Western Electric Coordinating Council. Argonne also evaluated the US average generation mix and renewable generation of electricity for PHEV and BEV recharging scenarios to show the effects of these generation mixes on PHEV WTW results. Argonne's GREET model was designed to examine the WTW energy use and GHG emissions for PHEVs and BEVs, as well as FCVs, regular HEVs, and conventional gasoline internal combustion engine vehicles (ICEVs). WTW results are reported for charge-depleting (CD) operation of PHEVs under different recharging scenarios. The combined WTW results of CD and charge-sustaining (CS) PHEV operations (using the utility factor method) were also examined and reported. According to the utility factor method, the share of vehicle miles traveled during CD operation is 25% for PHEV10 and 51% for PHEV40. Argonne's WTW analysis of PHEVs revealed that the following factors significantly impact the energy use and GHG emissions results for PHEVs and BEVs compared with baseline gasoline vehicle technologies: (1) the regional electricity generation mix for battery recharging and (2) the adjustment of fuel economy and electricity consumption to reflect real-world driving conditions. Although the analysis predicted the marginal electricity generation mixes for major regions in the United States, these mixes should be evaluated as possible scenarios for recharging PHEVs because significant uncertainties are associated with the assumed market penetration for these vehicles. Thus, the reported WTW results for PHEVs should be directly correlated with the underlying generation mix, rather than with the region linked to that mix.

Researchers at Argonne National Laboratory expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model and incorporated the fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW results were separately calculated for the blended charge-depleting (CD) and charge-sustaining (CS) modes of PHEV operation and then combined by using a weighting factor that represented the CD vehicle-miles-traveled (VMT) share. As indicated by PSAT simulations of the CD operation, grid electricity accounted for a share of the vehicle's total energy use, ranging from 6% for a PHEV 10 to 24% for a PHEV 40, based on CD VMT shares of 23% and 63%, respectively. In addition to the PHEV's fuel economy and type of on-board fuel, the marginal electricity generation mix used to charge the vehicle impacted the WTW results, especially GHG emissions. Three North American Electric Reliability Corporation regions (4, 6, and 13) were selected for this analysis, because they encompassed large metropolitan areas (Illinois, New York, and California, respectively) and provided a significant variation of marginal generation mixes. The WTW results were also reported for the U.S. generation mix and renewable electricity to examine cases of average and clean mixes, respectively. For an all-electric range (AER) between 10 mi and 40 mi, PHEVs that employed petroleum fuels (gasoline and diesel), a blend of 85% ethanol and 15% gasoline (E85), and hydrogen were shown to offer a 40-60%, 70-90%, and more than 90% reduction in petroleum energy use and a 30-60%, 40-80%, and 10-100% reduction in GHG emissions, respectively, relative to an internal combustion engine vehicle that used gasoline. The spread of WTW GHG emissions among the different fuel production technologies and grid generation mixes was wider than the spread of petroleum energy use, mainly due to the diverse fuel production technologies and feedstock sources for the fuels considered in this analysis. The PHEVs offered reductions in petroleum energy use as compared with regular hybrid electric vehicles (HEVs). More petroleum energy savings were realized as the AER increased, except when the marginal grid mix was dominated by oil-fired power generation. Similarly, more GHG emissions reductions were realized at higher AERs, except when the marginal grid generation mix was dominated by oil or coal. Electricity from renewable sources realized the largest reductions in petroleum energy use and GHG emissions for all PHEVs as the AER increased. The PHEVs that employ biomass-based fuels (e.g., biomass-E85 and -hydrogen) may not realize GHG emissions benefits over regular HEVs if the marginal generation mix is dominated by fossil sources. Uncertainties are associated with the adopted PHEV fuel consumption and marginal generation mix simulation results, which impact the WTW results and require further research. More disaggregate marginal generation data within control areas (where the actual dispatching occurs) and an improved dispatch modeling are needed to accurately assess the impact of PHEV electrification. The market penetration of the PHEVs, their total electric load, and their role as complements rather than replacements of regular HEVs are also uncertain. The effects of the number of daily charges, the time of charging, and the charging capacity have not been evaluated in this study. A more robust analysis of the VMT share of the CD operation is also needed.

Researchers at Argonne National Laboratory expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model and incorporated the fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW results were separately calculated for the blended charge-depleting (CD) and charge-sustaining (CS) modes of PHEV operation and then combined by using a weighting factor that represented the CD vehicle-miles-traveled (VMT) share. As indicated by PSAT simulations of the CD operation, grid electricity accounted for a share of the vehicle's total energy use, ranging from 6% for a PHEV 10 to 24% for a PHEV 40, based on CD VMT shares of 23% and 63%, respectively. In addition to the PHEV's fuel economy and type of on-board fuel, the marginal electricity generation mix used to charge the vehicle impacted the WTW results, especially GHG emissions. Three North American Electric Reliability Corporation regions (4, 6, and 13) were selected for this analysis, because they encompassed large metropolitan areas (Illinois, New York, and California, respectively) and provided a significant variation of marginal generation mixes. The WTW results were also reported for the U.S. generation mix and renewable electricity to examine cases of average and clean mixes, respectively. For an all-electric range (AER) between 10 mi and 40 mi, PHEVs that employed petroleum fuels (gasoline and diesel), a blend of 85% ethanol and 15% gasoline (E85), and hydrogen were shown to offer a 40-60%, 70-90%, and more than 90% reduction in petroleum energy use and a 30-60%, 40-80%, and 10-100% reduction in GHG emissions, respectively, relative to an internal combustion engine vehicle that used gasoline. The spread of WTW GHG emissions among the different fuel production technologies and grid generation mixes was wider than the spread of petroleum energy use, mainly due to the diverse fuel production technologies and feedstock sources for the fuels considered in this analysis. The PHEVs offered reductions in petroleum energy use as compared with regular hybrid electric vehicles (HEVs). More petroleum energy savings were realized as the AER increased, except when the marginal grid mix was dominated by oil-fired power generation. Similarly, more GHG emissions reductions were realized at higher AERs, except when the marginal grid generation mix was dominated by oil or coal. Electricity from renewable sources realized the largest reductions in petroleum energy use and GHG emissions for all PHEVs as the AER increased. The PHEVs that employ biomass-based fuels (e.g., biomass-E85 and -hydrogen) may not realize GHG emissions benefits over regular HEVs if the marginal generation mix is dominated by fossil sources. Uncertainties are associated with the adopted PHEV fuel consumption and marginal generation mix simulation results, which impact the WTW results and require further research. More disaggregate marginal generation data within control areas (where the actual dispatching occurs) and an improved dispatch modeling are needed to accurately assess the impact of PHEV electrification. The market penetration of the PHEVs, their total electric load, and their role as complements rather than replacements of regular HEVs are also uncertain. The effects of the number of daily charges, the time of charging, and the charging capacity have not been evaluated in this study. A more robust analysis of the VMT share of the CD operation is also needed.

Trade-linked Canada–United States household environmental impact analysis of energy use and greenhouse gas emissions

This study provides an empirical assessment of energy use and greenhouse gas (GHG) emissions associated with high and low residential development. Three major elements of urban development are considered: construction materials for infrastructure (including residential dwellings, utilities, and roads), building operations, and transportation (private automobiles and public transit). Two case studies from the City of Toronto are analyzed. An economic input–output life-cycle assessment (EIO-LCA) model is applied to estimate the energy use and GHG emissions associated with the manufacture of construction materials for infrastructure. Operational requirements for dwellings and transportation are estimated using nationally and/or regionally averaged data. The results indicate that the most targeted measures to reduce GHG emissions in an urban development context should be aimed at transportation emissions, while the most targeted measures to reduce energy usage should focus on building operations. The results also show that low-density suburban development is more energy and GHG intensive (by a factor of 2.0–2.5) than high-density urban core development on a per capita basis. When the functional unit is changed to a per unit of living space basis the factor decreases to 1.0–1.5, illustrating that the choice of functional unit is highly relevant to a full understanding of urban density effects.

A fuel cycle model—called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model—has been developed to evaluate well-to-wheels (WTW) energy and emission impacts of motor vehicle technologies fueled with various transportation fuels. The GREET model contains various hydrogen (H2) production pathways for fuel cell vehicle (FCV) applications. In this study, the GREET model was expanded to include four nuclear H2 production pathways: (a) H2 production at refueling stations via electrolysis using light water reactor–generated electricity, (b) H2 production in central plants via thermochemical water cracking using heat from a high-temperature gas-cooled reactor (HTGR), (c) H2 production in central plants via high-temperature electrolysis using HTGR-generated electricity and steam, and (d) H2 production at refueling stations via electrolysis using HTGR-generated electricity. The WTW analyses of these four options include these stages: uranium ore mining and milling, uranium yellowcake transportation, uranium conversion, uranium enrichment, uranium fuel fabrication, uranium fuel transportation, electricity or H2 production in nuclear power plants, H2 transportation, H2 compression, and H2 FCV operation. Our well-to-pump results show that significant reductions in fossil energy use and greenhouse gas (GHG) emissions are achieved by nuclear-based H2 compared to natural gas–based H2 production via steam methane reforming for a unit of H2 delivered at refueling stations. When H2 is applied to FCVs, the WTW results also show large benefits in reducing fossil energy use and GHG emissions.

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

Analysis of greenhouse gas emission reductions by collaboratively updating equipment in sewage treatment and municipal solid waste incineration plants

Life cycle assessment in support of sustainable transportation

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.

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.