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Built Environment

Energy Analysis Tool 

Prepared by: Chris Porter

Cambridge Systematics, Inc.Cambridge, Massachusetts

NREL Technical Monitor: Laura Vimmersted

March 2013

NREL/PR‐6A20‐58101
Built Environment Energy Analysis Too

Subcontractor: Chris Porter

Cambridge Systematics, Inc.

100 Cambridge Park Drive, Suite 400

Cambridge, MA 02140

Period of Performance: June 2011‐February 2013

NREL Technical Monitor: Laura Vimmerstedt

Prepared under Subcontract No. DGJ‐1‐11857‐01This publication was reproduced from the best available copy submitted by the su

received no editorial review at NREL.

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Overview

Developed for National Renewable Energy Labo(NREL) in support of U.S. Department of Energy Transportation Energy Futures StudyDesigned for scenario analysis at a national levelObjective – Assess impacts of future land use/buenvironment patterns on transportation energy agreenhouse gas (GHG) emissionsTool can be used to evaluate scenarios of differendistributions by density and urban design charact2030 and 2050

3
Models of Person-Miles of Travel

Four models of daily PMT/capita developed from 

» HBW & Other trip type; for Top 50 & Other MSA pop» Estimated at Census Tract level

4

IndependentVariable CategoriesHousehold Income <$30K, $30-60K, >$60KSex M, FAge (worker) 36-50, otherHome type Single-family, multi-familyPopulation density <750, 750-7000, >7000 persons per sq. mi.Employment density <100, 100-1000, >1000 jobs per sq. mi.Region of U.S. Northeast, Midwest, South, WestArea type Urban, ruralMSA population size <3M, >3M (Top 50); <200K, 200-500K, >500K (OtherInfrastructure (top 50) Congestion Index; Ratio of freeway to arterial miles
Fixed/User-Modifiable Inputs

Population, Demographics, and Travel

» Total population» Labor force participation rate» Percent of population in MSA» Distributions of HH income, 

workers by age

» Trips per day»Average vehicle occupancy» Mode shares by population 

density

Energy and G

» Energy and CO» Fuel-cycle and C

factors

»% energy by fue» GHG intensity 

generation

» Future vehicle e» Transit – averag

percent of VMT 

Infrastructur

» Congestion leve» Freeway versus 

dominance

5
Population Density Distributions

Percent of population by density, 2030 (baseline)

6

<750

750 - 7,000

>7,000

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

<100 100 -

1,000 >1,000

Employment Density (jobs/sq mi)

Top 50 MSAs

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

<100 100 -

1,000 >1

Employment Density (jo

Other MSAs
Sample Scenario Inputs (2030, Top 50 M

7

Baseline – Percent of Population Change – Percent of Population

Urban and Rural Urban and RuralPopulationDensity

Employment Density

Population

Density

Emp

Low Med High Total Low Me

Low 14.4% 0.5% 0.0% 14.9% Low ‐5.0% Medium 31.2% 25.1% 1.5% 57.8% Medium ‐5.0% High 5.9% 17.3% 4.1% 27.3% High 0.0% 

Total 51.5% 42.8% 5.6% 100.0% Total ‐10.0% Urban UrbanPopulationDensity

Employment Density Population

Density

Emp

Low Med High Total Low Me

Low 2.9% 0.3% 0.0% 3.3% Low ‐1.0% 

Medium 29.3% 24.9% 1.5% 55.8% Medium ‐4.7% High 5.9% 17.3% 4.1% 27.3% High 0.0% Total 38.2% 42.6% 5.6% 86.4% Total ‐5.7% 

Rural Rural

PopulationDensity

Employment Density Population

Density

Emp

Low Med High Total Low Me

Low 11.5% 0.1% 0.0% 11.6% Low ‐4.0% Medium 1.9% 0.2% 0.0% 2.0% Medium ‐0.3% 

High 0.0% 0.0% 0.0% 0.0% High 0.0% Total 13.4% 0.3% 0.0% 13.6% Total ‐4.3% 
Sample Scenario Inputs (2030, Other M

8

Baseline – Percent of Population Change – Percent of Population

Urban and Rural Urban and RuralPopulationDensity

Employment Density

Population

Density

Emp

Low Med High Total Low Me

Low 39.0% 0.6% 0.0% 39.6% Low ‐5.0% 

Medium 30.5% 23.1% 1.0% 54.6% Medium ‐5.0% High 1.6% 3.6% 0.7% 5.8% High 2.5% Total 71.1% 27.3% 1.7% 100.0% Total ‐7.5% Urban UrbanPopulationDensity

Employment Density Population

Density

Emp

Low Med High Total Low Me

Low 6.3% 0.4% 0.0% 6.7% Low ‐0.8% 

Medium 27.4% 22.7% 1.0% 51.1% Medium ‐4.5% High 1.6% 3.6% 0.7% 5.8% High 2.5% Total 35.2% 26.7% 1.7% 63.6% Total ‐2.8% 

Rural Rural

PopulationDensity

Employment Density Population

Density

Emp

Low Med High Total Low Me

Low 32.8% 0.1% 0.0% 32.9% Low ‐4.2% 

Medium 3.1% 0.4% 0.0% 3.5% Medium ‐0.5% High 0.0% 0.0% 0.0% 0.0% High 0.0% Total 35.8% 0.6% 0.0% 36.4% Total ‐4.7% 
Sample Scenario (continued)

Population Density

(persons per sq mi)

Change in Ped

Design Index

(Scenario)

Elasticity of transit

trips w/r/t sidewalk

coverage

e

<750 0% 0.00 750 – 7,000 10% 0.20 >7,000 20% 0.20 

9

Outputs represent scenario in which 10% (2030) (2050) of future population is redirected from lointo higher-density/more mixed-use neighborhooPedestrian design index is increased in medium adensity neighborhoods
Four Test Scenarios

10

Scenario Description

% Total Pop

Shift – 2030

% Total Pop

Shift – 2050

% of Pop

Growth

Shifted

A Modest shift 5% 10% 33% 

B Moderate shift 10% 20% 66% C High shift 15% 30% 100% 

D High shift (no ped improvement) 15% 30% 100% 

aPed improvements in medium and high density areas only
Scenario Results

11

-10.0%

-9.0%-8.0%-7.0%-6.0%-5.0%-4.0%-3.0%-2.0%-1.0%

0.0%

Modest shift Moderate shift High shift

High shift (no

ped improv.)

Change in VMT vs. Baseline

-10.0%

-9.0%-8.0%-7.0%-6.0%-5.0%-4.0%-3.0%-2.0%-1.0%

0.0%

Modest shift Moderate shift 

Change in GHG vs. Baselin

2030 2050

-10%

0%

10%20%30%40%50%60%

Modest shift Moderate shift High shift

High shift (no

ped improv.)

Change in PMT vs. Baseline, 2050

AllAutoTransitNMT
Conclusions

Empirically estimated model based on disaggregate daModel allows for analysis of joint population/employmscenarios as well as pedestrian environment factorResults were validated against VMT/capita, total energGHG emissions from other sources

» Total and auto validation performed well, but transit overesti

Model also allows for tweaking of energy and GHG fprovides full fuel-cycle outputs

12
Conclusions (continued)

Shifting 10-15% of 2030 population from lower-duse areas into higher-density, mixed-use areas coVMT by 4-5% and GHG by around 3% vs. baselinExtrapolating trend to 2050 could reduce VMT bGHG by 5-6% vs. baselineEnergy and GHG savings are less than VMT savinsignificant increase in transit use (30-50%)Impacts of land use change are of similar magnitufound in other recent studies (e.g., Moving CooleCooler, TRB Special Report 298)

13
Limitations and Potential Enhancemen

Land use variables still limited 

» With NHTS-based model can only use NHTS app

variables and aggregate indicators for top 50 MSA

Mode choice model could be estimated including variables

14
EXTRAS
Sample Scenario Results

16

2010

2030

Baseline

2030

Scenario

20

Bas

MSA Population and

Workers (1,000s)

Population 262,000 309,423 

Workers 169,514 182,341 

Vehicle‐Miles of Travel

Total Auto VMT (MM) 1,989,012 2,284,460 2,198,408 2,750% Change vs. 2010 14.9% 10.5% 3

% Change vs. Baseline ‐3.8% 

Auto VMT per Capita 7,592 7,242 6,969 7
Sample Scenario Results (Continued)

17

2010

2030

Baseline

2030

Scenario

20

Bas

PMT per Capita 12,566 12,026 11,578 1

% Change vs. 2010

All Modes 15.2% 12.4% 

Auto 15.2% 10.9% Transit 15.1% 33.6% Nonmotorized 16.7% 30.3% 

% Change vs. Baseline

All Modes ‐2.4% 

Auto ‐3.7% 

Transit 16.1% 

Nonmotorized 11.7% 
Sample Scenario Results (Continued)

18

2010

2030

Baseline

2030

Scenario

20

Bas

Energy ‐ quadrillion Btu

Auto 15.22 13.12 12.62 

Transit 0.72 0.71 0.82 

Total 15.94 13.83 13.45 

% Change vs. 2010 ‐13.2% ‐15.6% 

% Change vs. Baseline ‐2.7% 

GHG ‐ mmt CO2e

Auto 1,162.6 993.5 956.1 1

Transit 56.8 55.1 64.0 

Total 1,219.4 1,048.6 1,020.0 1

% Change vs. 2010 ‐14.0% ‐16.4% 

% Change vs. Baseline ‐2.7%