Transportation Energy Data Book: Edition 29

Davis, Stacy Cagle; Diegel, Susan W; Boundy, Robert Gary

doi:10.2172/983839

Cited by 55 publications

(43 citation statements)

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“…A vehicle with a fuel economy of 14 mpg in the city is expected to have an EPA sticker (combined) fuel economy of 16.3 mpg, based on the way EPA accounts for the city/highway drive cycle differences. USVI fuel economy of light trucks and SUVs is lower than the U.S. fuel economy of those vehicles (18.1mpg), because the USVI vehicles are older (17.5 years versus 13.5 years, on average [Davis, et al 2010]), because of the hilly terrain of St. Thomas, and because the U.S. Corporate Average Fuel Economy (CAFE) standards do not apply to the USVI until model year 2012.…”

Section: Usvi Road Transportation Vital Statisticsmentioning

confidence: 99%

U.S. Virgin Islands Transportation Petroleum Reduction Plan

Johnson¹

2011

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Section: Usvi Road Transportation Vital Statisticsmentioning

confidence: 99%

U.S. Virgin Islands Transportation Petroleum Reduction Plan

Johnson¹

2011

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“…For example, the American scholars, Davis et al [42], found that the fuel economy of cars and light trucks in the USA improved by 0.47% to 0.53% every year from 1990-2009 without any policy requirements being in place and, according to the vehicle inventory surveys conducted by the US Census Bureau, it was found that, despite no new fuel-economy requirements, the fuel economy of the US HDTs improved by about 1% every year from 1977 to 2002 [43]. Therefore, in this paper, it is assumed that from 2015 to 2035 the fuel-consumption rate of heavy-and medium-duty vehicles for gasoline, diesel, and natural gas in Chongqing will improve by 1% per year and the fuel-consumption rate of light-duty vehicles will improve by 0.5% per year.…”

Section: Scenario Design For Fuel Economymentioning

confidence: 99%

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

Tan

Zeng

et al. 2018

Sustainability

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This study, using Chongqing City of China as an example, predicts the future motor vehicle population using the Gompertz Model and the motorcycle population using the piecewise regression model, and predicts and analyzes fuel consumption and greenhouse gas (GHG) emissions of motor vehicles from 2016 to 2035 based on the bottom-up method under different scenarios of improving the fuel economy of conventional vehicles, promoting alternative fuel vehicles, and the mixed policy of the above two policy options. The results indicate that the total population of motor vehicles in Chongqing will increase from 4.61 million in 2015 to 10.15 million in 2035. In the business-as-usual scenario, the road-transportation energy demand in Chongqing will keep increasing from 2015 and will peak in 2030, before it begins to decline by 2035. The trends for the tank to wheel (TTW) and well to wheel (WTW) GHG emissions are similar to that of energy demand. The WTW GHG emissions will increase from 24.9 Mt CO 2 e in 2016 to 50.5 Mt CO 2 e in 2030 and will then gradually decline to 48.9 Mt CO 2 e in 2035. Under the policy scenarios of improving fuel economy of conventional fuel passenger cars, promoting alternative fuel vehicles, and their mixed policy, direct energy consumption and TTW and WTW GHG emissions from 2016 to 2035 will be reduced to different levels. It is also found that the two types of policies have a hedging effect on the direct energy-consumption saving, TTW, and WTW GHG emission reductions. Sensitivity analysis of key parameters and policy settings is conducted to investigate the impact of their changes on the vehicle population projection, direct energy demand, and WTW GHG emissions. Some policy implications are suggested to provide reference for the formulation and adjustment of Chongqing's, or even China's, low-carbon road transportation policies in the future based on the analysis results.

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“…The cost of GHG was estimated using the current European cap and trade value, around 18.7 $/ton, although this value fluctuates wildly over time [17,18]. The cost of electricity is assumed to be $0.12/kWh (an average for the US) although this cost can vary greatly by time of day, location, and energy source [20]. Energy equivalence in terms BTUs for fossil fuels and electricity was estimated using coefficients from the Transportation Energy Data Book [21].…”

Section: Data Sourcesmentioning

confidence: 99%

Green and Economic Fleet Replacement Modeling: Part I

Kim¹,

Figliozzi²,

Porter³

2011

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This paper focuses on the economic and environmental optimization of vehicle replacement decisions. A new type of vehicle replacement model (VRM) that minimizes purchase, operating, maintenance, and emissions costs is proposed. An integer programming VRM is adapted from literature to represent current environmental and policy issues such as greenhouse gas (GHG) taxes and incentives for electric vehicle purchases. This research also analyzes the impacts of utilization (mileage per year per vehicle) and gasoline prices on fleet management decisions estimating energy and emissions reductions for a variety of fleet replacement scenarios using real-world data in the United States. Findings include: (a) fuel efficient vehicles such as hybrid and electric vehicles are purchased only in scenarios with high gasoline prices and/or utilization, (b) current European CO 2 cap and trade emissions price (around $18.7/ton) do not significantly alter fleet management decisions, and (c) electric vehicle incentives (i.e., tax credits) do increase the rate of purchases of hybrid or electric vehicles in scenarios with high gasoline prices and vehicle utilization. This research indicates that the proposed model can be effectively used to inform environmental and fiscal policies regarding vehicle regulations, tax incentives, and GHG emissions.

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Transportation Energy Data Book: Edition 29

Cited by 55 publications

References 0 publications

U.S. Virgin Islands Transportation Petroleum Reduction Plan

U.S. Virgin Islands Transportation Petroleum Reduction Plan

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

Green and Economic Fleet Replacement Modeling: Part I

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