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| 1 |
ID:
098256
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2010.
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| Summary/Abstract |
Urban form - for example, sprawl versus infill development - impacts people's daily travel patterns and annual vehicle-kilometers traveled (VKT). This paper explores how urban form impacts greenhouse gas (GHG) emissions from passenger-vehicles, the largest source of urban transportation GHG emissions. Our research uses a recently published urban scaling rule to develop six scenarios for high- and low-sprawl US urban growth. We develop and apply a Monte Carlo approach that describes ensemble statistics for several dozen urban areas rather than forecasting changes in individual urban areas. Then, employing three vehicle- and fuel-technology scenarios, we estimate total passenger VKT and resulting GHG emissions for US urban areas. Our results indicate that comprehensive compact development could reduce US 2000-2020 cumulative emissions by up to 3.2 GtCO2e (15-20% of projected cumulative emissions). In general, vehicle GHG mitigation may involve three types of approaches: more-efficient vehicles, lower-GHG fuels, and reduced VKT. Our analyses suggest that all three categories must be evaluated; otherwise, improvements in one or two areas (e.g., vehicle fuel economy, fuel carbon content) can be offset by backsliding in a third area (e.g., VKT growth).
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| 2 |
ID:
098553
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| Publication |
2010.
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| Summary/Abstract |
Urban form - for example, sprawl versus infill development - impacts people's daily travel patterns and annual vehicle-kilometers traveled (VKT). This paper explores how urban form impacts greenhouse gas (GHG) emissions from passenger-vehicles, the largest source of urban transportation GHG emissions. Our research uses a recently published urban scaling rule to develop six scenarios for high- and low-sprawl US urban growth. We develop and apply a Monte Carlo approach that describes ensemble statistics for several dozen urban areas rather than forecasting changes in individual urban areas. Then, employing three vehicle- and fuel-technology scenarios, we estimate total passenger VKT and resulting GHG emissions for US urban areas. Our results indicate that comprehensive compact development could reduce US 2000-2020 cumulative emissions by up to 3.2 GtCO2e (15-20% of projected cumulative emissions). In general, vehicle GHG mitigation may involve three types of approaches: more-efficient vehicles, lower-GHG fuels, and reduced VKT. Our analyses suggest that all three categories must be evaluated; otherwise, improvements in one or two areas (e.g., vehicle fuel economy, fuel carbon content) can be offset by backsliding in a third area (e.g., VKT growth).
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| 3 |
ID:
133119
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2014,
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| Summary/Abstract |
The built environment can be used to influence travel demand, but very few studies consider the relative energy savings of such policies in context of a complex urban system. This analysis quantifies the day-to-day and embodied energy consumption of four different neighborhoods in Austin, Texas, to examine how built environment variations influence various sources of urban energy consumption. A microsimulation combines models for petroleum use (from driving) and residential and commercial power and natural gas use with rigorously measured building stock and infrastructure materials quantities (to arrive at embodied energy). Results indicate that the more suburban neighborhoods, with mostly detached single-family homes, consume up to 320% more embodied energy, 150% more operational energy, and about 160% more total life-cycle energy (per capita) than a densely developed neighborhood with mostly low-rise-apartments and duplexes. Across all neighborhoods, operational energy use comprised 83 to 92% of total energy use, and transportation sources (including personal vehicles and transit, plus street, parking structure, and sidewalk infrastructure) made up 44 to 47% of the life-cycle energy demands tallied. Energy elasticity calculations across the neighborhoods suggest that increased population density and reduced residential unit size offer greatest life-cycle energy savings per capita, by reducing both operational demands from driving and home energy use, and from less embodied energy from construction. These results provide measurable metrics for comparing different neighborhood styles and develop a framework to anticipate energy-savings from changes in the built environment versus household energy efficiency.
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