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Michael Wang - One of the best experts on this subject based on the ideXlab platform.

  • greet 1 5 transportation fuel cycle model vol 1 methodology development use and results
    Other Information: PBD: 6 Oct 1999, 1999
    Co-Authors: Michael Wang
    Abstract:

    This report documents the development and use of the most recent version (Version 1.5) of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel-cycle emissions and energy associated with various transportation fuels and advanced vehicle technologies for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, particulate matter with diameters of 10 micrometers or less, and sulfur oxides) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates total energy Consumption, fossil fuel Consumption, and Petroleum Consumption when various transportation fuels are used. The GREET model includes the following cycles: Petroleum to conventional gasoline, reformulated gasoline, conventional diesel, reformulated diesel, liquefied Petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied natural gas, liquefied Petroleum gas, methanol, Fischer-Tropsch diesel, dimethyl ether, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydropower, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; soybeans to biodiesel; flared gas to methanol, dimethyl ether, and Fischer-Tropsch diesel; and landfill gases to methanol. This report also presents themore » results of the analysis of fuel-cycle energy use and emissions associated with alternative transportation fuels and advanced vehicle technologies to be applied to passenger cars and light-duty trucks.« less

  • greet 1 5 transportation fuel cycle model vol 1 methodology development use and results
    Other Information: PBD: 6 Oct 1999, 1999
    Co-Authors: Michael Wang
    Abstract:

    This report documents the development and use of the most recent version (Version 1.5) of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel-cycle emissions and energy associated with various transportation fuels and advanced vehicle technologies for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, particulate matter with diameters of 10 micrometers or less, and sulfur oxides) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates total energy Consumption, fossil fuel Consumption, and Petroleum Consumption when various transportation fuels are used. The GREET model includes the following cycles: Petroleum to conventional gasoline, reformulated gasoline, conventional diesel, reformulated diesel, liquefied Petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied natural gas, liquefied Petroleum gas, methanol, Fischer-Tropsch diesel, dimethyl ether, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydropower, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; soybeans to biodiesel; flared gas to methanol, dimethyl ether, and Fischer-Tropsch diesel; and landfill gases to methanol. This report also presents themore » results of the analysis of fuel-cycle energy use and emissions associated with alternative transportation fuels and advanced vehicle technologies to be applied to passenger cars and light-duty trucks.« less

  • development and use of the greet model to estimate fuel cycle energy use and emissions of various transportation technologies and fuels
    Other Information: PBD: Mar 1996, 1996
    Co-Authors: Michael Wang
    Abstract:

    This report documents the development and use of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel- cycle emissions and energy use associated with various transportation fuels for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, sulfur oxides, and particulate matter measuring 10 microns or less) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates the total fuel-cycle energy Consumption, fossil fuel Consumption, and Petroleum Consumption using various transportation fuels. The GREET model includes 17 fuel cycles: Petroleum to conventional gasoline, reformulated gasoline, clean diesel, liquefied Petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied Petroleum gas, methanol, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydrogen, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; and landfill gases to methanol. This report presents fuel-cycle energy use and emissions for a 2000 model-year car powered by each of the fuels that are produced from the primary energy sources considered in the study.

Danny Yeung - One of the best experts on this subject based on the ideXlab platform.

  • determinants of the crude oil futures curve inventory Consumption and volatility
    Journal of Banking and Finance, 2017
    Co-Authors: Christina Sklibosios Nikitopoulos, Matthew Squires, Susan Thorp, Danny Yeung
    Abstract:

    Abstract Since 2008, the WTI oil futures curve has been positively sloped for extended periods. We test whether changes in inventory alone can explain this atypically long contango. To do this, we estimate monthly VARs of the CME WTI oil futures spread and OECD and U.S. inventory in line with standard theory, and add Petroleum Consumption and implied volatility to the vector of endogenous variables. When we model the futures spread as one continuous series, results confirm two-way causation between inventory and the futures curve, as predicted by the theory of storage. However when we separate negative and positive futures spreads we find that: two-way causation between the futures spread and U.S. inventory breaks down; shocks to OECD Petroleum Consumption cause more negative spreads and shocks to U.S. Consumption cause more positive spreads in addition to inventory-driven changes; and increases in volatility directly raise positive spreads. These new causal channels have become significant since 2008 and can be related to higher inventory, inelastic supply of oil and uncertainty about global economic conditions.

  • determinants of the crude oil futures curve inventory Consumption and volatility
    Social Science Research Network, 2016
    Co-Authors: Christina Sklibosios Nikitopoulos, Matthew Squires, Susan Thorp, Danny Yeung
    Abstract:

    Since 2008, the usually negative crude oil futures spread has been positive for extended periods, raising doubts about conventional explanations. We re-examine the dynamics of the futures spread using monthly VARs on the CME WTI oil futures spread, OECD and U.S. oil and Petroleum inventories and Consumption, and historical and implied volatility. When we model the spread as one continuous series, results confirm bi-directional causation between inventory and the futures spread, as predicted by the theory of storage. However results show that excess inventory is not adequately modelled as deviations from a secular trend: Consumption has a separate causal relation with de-trended inventory. When negative and positive spread regimes are modelled separately, we find that shocks to OECD Petroleum Consumption directly widen negative spreads. Further, increases in volatility make positive spreads more steeply positive but are not related to negative spreads, consistent with inelastic supply of crude oil.

Richard De Neufville - One of the best experts on this subject based on the ideXlab platform.

  • life cycle model of alternative fuel vehicles emissions energy and cost trade offs
    Transportation Research Part A-policy and Practice, 2001
    Co-Authors: Jeremy Hackney, Richard De Neufville
    Abstract:

    Abstract This paper describes a life cycle model for performing level-playing field comparisons of the emissions, costs, and energy efficiency trade-offs of alternative fuel vehicles (AFV) through the fuel production chain and over a vehicle lifetime. The model is an improvement over previous models because it includes the full life cycle of the fuels and vehicles, free of the distorting effects of taxes or differential incentives. This spreadsheet model permits rapid analyses of scenarios in plots of trade-off curves or efficiency frontiers, for a wide range of alternatives with current and future prices and levels of technology. The model is available on request. The analyses indicate that reformulated gasoline (RFG) currently has the best overall performance for its low cost, and should be the priority alternative fuel for polluted regions. Liquid fuels based on natural gas, M100 or M85, may be the next option by providing good overall performance at low cost and easy compatibility with mainstream fuel distribution systems. Longer term, electric drive vehicles using liquid hydrocarbons in fuel cells may offer large emissions and energy savings at a competitive cost. Natural gas and battery electric vehicles may prove economically feasible at reducing emissions and Petroleum Consumption in niches determined by the unique characteristics of those systems.

Gilbert E Metcalf - One of the best experts on this subject based on the ideXlab platform.

  • using tax expenditures to achieve energy policy goals
    The American Economic Review, 2008
    Co-Authors: Gilbert E Metcalf
    Abstract:

    Tax expenditures are a major source of support for energy related activities in the federal budget exceeding direct budget support for energy by a factor of nearly six. Focusing on the policy goals of reducing greenhouse gas emissions and Petroleum Consumption, I find these tax expenditures highly cost ineffective at best and counterproductive at worse. The tax credit for ethanol is an example of a cost ineffective subsidy. The cost of reducing CO2 emissions through this subsidy exceeded $1,000 per ton of CO2 avoided in 2006. A change in the way the subsidy is administered provides an opportunity to measure its incidence. I find that the entire subsidy is passed backward to ethanol producers and possibly farmers. Consideration of market structure suggests that farmers receive little of the subsidy.

  • using tax expenditures to achieve energy policy goals
    The American Economic Review, 2008
    Co-Authors: Gilbert E Metcalf
    Abstract:

    Tax expenditures are a major source of support for energy related activities in the federal budget exceeding direct budget support for energy by a factor of nearly six. Focusing on the policy goals of reducing greenhouse gas emissions and Petroleum Consumption, I find these tax expenditures highly cost ineffective at best and counterproductive at worse. The tax credit for ethanol is an example of a cost ineffective subsidy. The cost of reducing CO2 emissions through this subsidy exceeded $1,700 per ton of CO2 avoided in 2006 and the cost of reducing oil Consumption over $85 per barrel.

Christina Sklibosios Nikitopoulos - One of the best experts on this subject based on the ideXlab platform.

  • determinants of the crude oil futures curve inventory Consumption and volatility
    Journal of Banking and Finance, 2017
    Co-Authors: Christina Sklibosios Nikitopoulos, Matthew Squires, Susan Thorp, Danny Yeung
    Abstract:

    Abstract Since 2008, the WTI oil futures curve has been positively sloped for extended periods. We test whether changes in inventory alone can explain this atypically long contango. To do this, we estimate monthly VARs of the CME WTI oil futures spread and OECD and U.S. inventory in line with standard theory, and add Petroleum Consumption and implied volatility to the vector of endogenous variables. When we model the futures spread as one continuous series, results confirm two-way causation between inventory and the futures curve, as predicted by the theory of storage. However when we separate negative and positive futures spreads we find that: two-way causation between the futures spread and U.S. inventory breaks down; shocks to OECD Petroleum Consumption cause more negative spreads and shocks to U.S. Consumption cause more positive spreads in addition to inventory-driven changes; and increases in volatility directly raise positive spreads. These new causal channels have become significant since 2008 and can be related to higher inventory, inelastic supply of oil and uncertainty about global economic conditions.

  • determinants of the crude oil futures curve inventory Consumption and volatility
    Social Science Research Network, 2016
    Co-Authors: Christina Sklibosios Nikitopoulos, Matthew Squires, Susan Thorp, Danny Yeung
    Abstract:

    Since 2008, the usually negative crude oil futures spread has been positive for extended periods, raising doubts about conventional explanations. We re-examine the dynamics of the futures spread using monthly VARs on the CME WTI oil futures spread, OECD and U.S. oil and Petroleum inventories and Consumption, and historical and implied volatility. When we model the spread as one continuous series, results confirm bi-directional causation between inventory and the futures spread, as predicted by the theory of storage. However results show that excess inventory is not adequately modelled as deviations from a secular trend: Consumption has a separate causal relation with de-trended inventory. When negative and positive spread regimes are modelled separately, we find that shocks to OECD Petroleum Consumption directly widen negative spreads. Further, increases in volatility make positive spreads more steeply positive but are not related to negative spreads, consistent with inelastic supply of crude oil.