this is my presentation - nc state university€¦ · low-carb land presentation outline •...
TRANSCRIPT
Low-Carb Land:Informing Your Land Use Decisions to Consider Travel and CO2 Impacts
620000-4081
Song Bai, Douglas Eisinger, Jason Amador, Lyle Chinkin (STI)Joyce Phillips, Washington State Department of Commerce
Thera Black, Thurston Regional Planning CouncilMia Waters, Washington State Department of Transportation
Presented toSouthern Transportation and Air Quality Summit
Raleigh, North Carolina, July 20, 2011
2
3
Low-Carb Land Presentation Outline
• Background• Overview: motivation and tool purpose• Tool description• Tool menu overview (Beta v4.0)• Case studies (hypothetical)
– Thurston County, Washington– Marin County, California
4
Background
• Representative greenhouse gas (GHG) reduction goals– By 2050, reduce to 50-80% below 1990 levels
NRC, 2010: America’s Climate Choices
• Vehicle controls: fuel economy, fuel type, activity– By 2050, smart growth could reduce CO2 < 1% up to 11%
NRC, 2009: Driving and the Built Environment
• Smaller agency needs– “…to analyze smart growth strategies… training is
needed… [for] local jurisdictions and smaller MPOs.”Caltrans, 2007: Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies
Overview: Motivation
Land Use• Density• Diversity• Destination• Distance• Design
Travel• Vehicle miles traveled (VMT)• Vehicle trips started (VT)
GHG• CO2 emissions
5
• Helps small- to medium-sized regions– Reduce analysis barriers for agencies with few resources
• Informs decision making processes– Provide easily used tool for policy or public settings
Examine interactions among land use patterns,travel activities, and CO2 emissions.
Growth• Population• Jobs
Overview: Tool Purpose
• Lets users test growth planning scenarios– Future growth and spatial allocation of population/jobs
• Compares CO2 impacts– High-level directional insights for CO2 emissions changes
6
Sample CO2 emissions results from Low-Carb Land (tonnes/day).
Tool Description: Overview
Web-based graphical user interface• Interactive maps and spatial analysis functions• Data query and management functions
7
Data Modeling Scenarios “D” Variables CO2
Tool Description: Data
• Pre-loaded census tract-level data– Most U.S. areas (45 of 50 states)– Households, jobs, travel activities (2002-2006)– Sources
• U.S. Census Bureau• U.S. Bureau of Transportation Statistics• Oak Ridge National Laboratory
– Managed in SQL Server database
• Interactive GIS maps– State, county, census tract– Managed in open source platform with
OpenLayers and MapServer
Data
8
Tool Description: Scenarios
Modeling Scenarios• Baseline
– Sub-regions and area types– Aggregation of pre-loaded
census tract-level data
• Alternatives– Future growth patterns– Land use strategies and options
9
Tool Description: “D” Variables
“D” Variables • Definitions and metrics– Density: degree of compactness– Diversity: degree of multiple land uses– Destination: level of regional accessibility– Distance: level of transit service– Design: street network characteristics
• Elasticities– From EPA’s literature review – Quantified impacts on travel activities
10
Tool Description: Emissions
CO2
• Emission factors– Derived from EPA’s MOVES model– Running and start exhaust for on-road traffic– Vary by area type and speed range
• Total emissions– Aggregated for sub-regions– Summarized results by scenario
11
Tool Menu OverviewHome
Log in Tool background
About
DefinitionsCreate and review projects
Define sub-regions by area type
Create scenario for a base year
Create future growth scenarios
Comparison Summary
Outputs: travel activity and CO2 emissions
My Scenarios
Select Region
Base Case
Alternatives
12
Thurston County (WA) Case Study
13
Fast population growth: ~ 2% per year
255,000 in 2010 → 373,000 in 2030
Source: Thurston Regional Planning Council
14
15
16
17
18
19
20
21
22
Embedded data: D variable elasticities
23
Embedded data: trips, VMT, speeds
24
Embedded data: emission factors
Output: base case emissions
Thurston County Hypothetical Scenarios
25
Scenario Year Land Use Pattern
Base Case 2005 Existing baseline
BAU 2030 “Business as usual”
Compact 2030 Compact, urbanized growth
Sprawl 2030 Sprawl growth in suburbs
Illustrations assume 50% growth in population and jobs over 25 years.
26
27
Output: emissions by scenario
Thurston County Results
• Future scenarios (2030) vs. base case (2005)– 50% pop./job growth about 35–42% CO2 increase
• Compact vs. BAU or Sprawl– shift to urban about 4–5% CO2 reduction
28
Marin County (CA) Case Study
29
Slow population growth: ~ 0.5% per year
251,000 in 2010 281,000 in 2030
Source: County of Marin, http://www.co.marin.ca.us
30
31
User-created scenarios
32
Output: emissions by scenario
Marin County Results
• Future scenarios (2030) vs. base case (2005)– ~14% CO2 increase
• Shift to urban vs. shift to suburban– ~1% CO2 reduction
33
34
Summary
• Assists policy-level land use analyses – Screening resource; high-level directional insights
• Helps small- to mid-sized agencies– Easy to use; requires minimal inputs– Complements models requiring more expertise, funds– Could be used in public settings to facilitate dialogue
• Enables quick sensitivity testing– Scenarios take a few hours to develop and run
Future improvements– Map functions, data updates, further validation work
35
AcknowledgmentsThe tool development team wishes to thank the following individuals for their support:
Jason Beloso, WSDOTMike Claggett, FHWA Jeff Houk, FHWA Karin Landsberg, WSDOTSteve Ludewig, STIBharath Paladugu, TRPC Steve Reid, STIJohn Thomas, EPASupin Yoder, FHWA
Question and Answer Discussion
Sample Forecasted CO2 Reductions
Source: NRC, 2009: “Driving and the Built Environment,” Report in Brief.
U.S. National Research Council: 1-11% by 2050(land use packaged with TCMs)
“… the reduction in vehicle miles traveled (VMT), energy use, and CO2 emissions resulting from more compact, mixed-use development would be in the range of less than 1 percent to 11 percent by 2050…”
Sample Forecasted CO2e Reductions
Source: EPA-ICF, 2011: “Potential Changes in Emissions Due to Improvements in Travel Efficiency,” Final Report.
U.S. EPA: 3% by 2030, 9% by 2050(land use packaged with TCMs)