Projecting the Effects of Land Use and Technology Change on

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Projecting the Effects of Land Use and Technology
Change on CO2 Emissions in the Upper Midwest
Brian Stone, Ph.D., City and Regional Planning
Program, Georgia Tech Tracey Holloway, Ph.D.,
Nelson Institute of Environmental Studies,
UW-Madison Adam Mednick, URPL, UW-Madison Scott
Spak, SAGE, UW-Madison
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Overview

• Research Approach
• How to model change in population and vehicle
  use over a six state region?
• Results of Scenario Modeling
• How would vehicle travel and emissions
  respond to smart growth and full
• hybridization of the light duty
  vehicle fleet over 50 years?
• Major Findings and Conclusions
• Can smart growth benefit climate change
  management and air quality?

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Upper Midwest Study Region
Milwaukee
Grand Rapids
Detroit
Minneapolis / St. Paul
Cleveland
Madison
Chicago
Columbus
Dayton
Cincinnati
Indianapolis
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Land Use and Technology Change Scenarios

• Business as Usual Projection to 2050 of five
  demographic determinants of travel population
  density, employment rate, income, vehicle
  ownership, and housing population based on
  historical rates of change between 1970 and 2000
• Moderate Smart Growth (SG1) Urban share of new
  population growth based on Portland, Oregon
• Aggressive Smart Growth (SG2) Urban share of new
  population growth increases by 10 percentage
  points per decade
• Vehicle Fleet Hybridization (HEV) Assumes BAU
  population growth patterns and full
  dissemination of conventional hybrid-electric
• vehicles

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SG2 Scenario Future Growth Shares based on
Fixed Urban Growth Rates (10 / Decade)
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Median Metro Density Change by Scenario
2000-2050
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Federal Highway Administration Transferability
Framework
2000 2050
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Household Daily VMT Rates by Community Type
Daily HH VMT Rate
Census Tract Community Type Designation
Mean level of error in base period 3.1
(Reuscher et al. 2002)
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Vehicle Emissions Modeling
MOBILE6
Vehicle Fleet Characteristics Regional Climate
Parameters Average Travel Speeds
(U/S/R) Coldstart Fractions (100/0)
CO gm/mile NOx gm/mile PM2.5 gm/mile VOC gm/mile
Conventional Fleet CO2 Total VMT / Average
MPG X 8.877 kg
HEV Fleet CO2 Adjustment Factor MPGBASE /
MPGHEV X 1 VMT Rebound
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Change in Metro VMT by Scenario (2000 Base)
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Change in Metro VMT by Scenario (2000 Base)
Relative to BAU, VMT growth at median under SG1
and SG2 is 15 and 24 lower, respectively.
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Change in Median Metro VMT and CO2 by Scenario
(2000 Base)
Growth in CO2 is 25 lower than BAU under
SG1, and 34 lower under SG2.
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Change in Median Metro CO2 by SG and HEV
Scenarios (2000 Base)
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Change in Median Metro CO2 by SG and HEV
Scenarios (2050 BAU Base)
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Median Metro Elasticities (Density) by SG Scenario
A doubling in population density is associated
with reductions in VMT and emissions of 30.
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Median Metro Elasticities (Density) by SG Scenario
A doubling in population density is associated
with reductions in VMT and emissions of 30.
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Major Findings

• Full hybridization of the vehicle fleet was found
  to fully offset the impacts of 50 years of
  population growth on CO2 emissions. An aggressive
  smart growth scenario was found to reduce the
  growth in emissions under BAU by 34.
• Technological change alone will not be sufficient
  to achieve Kyoto based targets for CO2 reductions
  from the light duty vehicle fleet.
• High levels of metropolitan densification hold
  the potential to meet or exceed reductions in CO2
  brought about through HEV technologies over 50
  years.