Envisioning a Sustainable Maryland:
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Envisioning a Sustainable Maryland: Comparing Alternative Development Scenarios Considering Energy Consumption and Water Quality Gerrit-Jan Knaap, Executive Director and Professor National Center for Smart Growth, University of Maryland Glenn Moglen, Professor Civil and Environmental Engineering, Virginia Tech Matthias Ruth, Director Center for Integrative Environmental Research, September 9, 2009
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Envisioning a Sustainable Maryland: Comparing Alternative Development Scenarios Considering Energy Consumption and Water Quality. September 9, 2009. Gerrit-Jan Knaap, Executive Director and Professor National Center for Smart Growth, University of Maryland Glenn Moglen, Professor - PowerPoint PPT Presentation
Transcript of Envisioning a Sustainable Maryland:
Envisioning a Sustainable Maryland:
Comparing Alternative Development Scenarios Considering Energy Consumption and Water Quality
Gerrit-Jan Knaap, Executive Director and ProfessorNational Center for Smart Growth, University of Maryland
Glenn Moglen, ProfessorCivil and Environmental Engineering, Virginia Tech
Matthias Ruth, DirectorCenter for Integrative Environmental Research, University of Maryland
September 9, 2009
Presentation OutlinePresentation Outline
1.1. Project Foundations;Project Foundations;
2.2. The Maryland Scenario The Maryland Scenario Project;Project;
3.3. Model Development;Model Development;
4.4. Nutrient Loading Model;Nutrient Loading Model;
5.5. Residential Energy Model;Residential Energy Model;
6.6. Yet to do.Yet to do.
PROJECT PROJECT FOUNDATIONSFOUNDATIONS
Today’s Today’s VISIONVISION……Tomorrow’s Tomorrow’s REALITYREALITY
Baltimore Baltimore Convention CenterConvention Center
Compared with Buildout and Compared with Buildout and COG forecasts, RCP results COG forecasts, RCP results would have..would have..
More jobs and housing close to More jobs and housing close to transit;transit;
More jobs and housing inside More jobs and housing inside priority funding areas;priority funding areas;
Less development on green Less development on green infrastructure; andinfrastructure; and
Less new impervious surfaces;Less new impervious surfaces; Fewer vehicle miles traveled.Fewer vehicle miles traveled.
The Maryland The Maryland Scenario ProjectScenario Project
The purpose of the The purpose of the Maryland Scenario Maryland Scenario Project is….Project is…. To take an informed and careful look at To take an informed and careful look at
alternative long-term future scenarios;alternative long-term future scenarios; To conduct a quantitative assessment To conduct a quantitative assessment
of each scenario;of each scenario; To identify where and how public policy To identify where and how public policy
decisions will increase the likelihood of decisions will increase the likelihood of more desirable scenarios;more desirable scenarios;
(To lay the foundation for a state (To lay the foundation for a state development plan.)development plan.)
Washington Post, Washington Post, 7/5/087/5/08
Capital Capital DiamonDiamondd
Model DevelopmentModel Development
Modeling and Analysis Modeling and Analysis InfrastructureInfrastructure Regional econometric modelRegional econometric model Regional transportation modelRegional transportation model Regional land use modelRegional land use model Nutrient loading modelNutrient loading model Residential energy consumption Residential energy consumption
modelmodel Fiscal impact modelFiscal impact model Greenhouse gas modelGreenhouse gas model
Modeling FrameworksModeling Frameworks
EconometricModels
Land UseModel
TransportationModel
Nutrient Loading Model
EnergyConsumption
Model
Ind
icato
rs
Exo
gen
ou
sF
actors
Land UsePolicies
Air QualityModel
Metro
NationalGNP
LIFT model
StateGSP
STEMS model
CountyRegional
JOBS & HH(SMZ)
Metro County
UMD INFORUM
Hammer
NCSGTrends from BEA & BLS
Land Uses30m gridLEAM
Land Cover and
input data
TOP DOWN
BOTTOM UP
MDP Growth ModelEconomy
Environment
MDPNCSG
Top Down / Bottom Up Land Top Down / Bottom Up Land Use ModelsUse Models
Top Level: National View County/state zones; Interstate road/transit network• Economic Forecast model• FAF Commodity Flow model• Long Distance Person Travel model
Bottom Level: MPO View MPO TAZs; Sub-arterial network• No statewide modeling occurs• MPO model data aggregation to• compare with middle layer Statewide model
Middle Level: “Regional” View Sub-county/aggregated MPO zonesArterial network; External Stations• Short Distance Person Travel model
• Trip Generation• Trip Distribution• Mode Split• Assignment
MWCOG
BMC
3-Level Transport 3-Level Transport ModelModel
Constructing a High Energy Price Growth Scenario
Crude Oil Price Crude Oil Price light sulfur ($/bbl)
271
141
12
1990 2000 2010 2020 2030 2040
pdm5ind AEO08 HighPrice
Agriculture, Forestry and Fisheries Agriculture, Forestry and Fisheries Nominal Price Index (Base vs. Alt)
2.46
1.41
0.35
1980 1990 2000 2010 2020 2030 2040
AgPrice Alt_AgPrice
Federal Defense Spending Federal Defense Spending Base vs. Concentrated Growth
1265
776
287
1980 1990 2000 2010 2020 2030 2040
Base ConGrowth
Raise PCE of FIRE Raise PCE of FIRE Real Price Index (Base vs. Alt)
711607
397545
83483
1980 1990 2000 2010 2020 2030 2040
pcefire pcefirefix
Difference in # of jobs in the US
Difference in # of jobs in MD
Difference in # of jobs by industryin the US
Difference in # of jobs By industry in MD
In 2040
High Energy
High Energy
In 2040
High Energy
High Energy
Congested links under Congested links under alternative scenariosalternative scenarios
High Energy Price Business as Usual
SCENARIO ANALYSIS SCENARIO ANALYSIS GROUPGROUPMD-LEAM - LAND USE MD-LEAM - LAND USE MODELMODEL
LEAM LAB, University of Illinois, Urbana-LEAM LAB, University of Illinois, Urbana-ChampaignChampaign
Growth - 2040Growth - 2040
Effects of Transportation Effects of Transportation Investments on Investments on Development PatternsDevelopment Patterns
Forecast Data (housing, employment)
RESAC Land Cover
Current Land Use
Current Nutrient Loads (N, P, Sed.)
Future Land Use
Future Nutrient Loads (N, P, Sed.)
Chesapeake Bay Program Model Loading Coefficients
Nutrient loading modelNutrient loading model
30 year (?) projections of future housing and employment
Four Maryland Regions: Western, Central, Southern, Eastern Shore
Modeling done at “block” scale (from 160 to 922 acres)
What is Forecast Data?
Rule 1: RC provides estimates of both future housing and employment. All models of future land use are executed twice with each predictor acting alone – the average is simply taken at the end
Rule 2:Historical changes in housing and employment from 1990 and 2000 census data are used to provide a background for quantifying magnitude of RC changes.
Converting Forecast Data into Future Land Use – Heuristic Rules
Rule 3: Increases in housing or employment will lead to decreases in forest cover and/or agricultural land use. (currently assumed in equal proportions)
Rule 4: Different urban land uses are added in proportion current urban land use proportions
Rule 5: Measures of everything (e.g. census data, current and future land use/land cover)are disjoint at the county level. Each county acts separately.
Converting Forecast Data into Future Land Use – Heuristic Rules
Allegany
Prince George
s
Montgomery
Caroline
Land Use Distribution in Focus Counties
Percent change in nitrogen loading, Prince Georges County, current vs. various scenarios.
Reality Check
Base Case
High Energy Prices
Land Use and Nutrient Loading changes in PG
Left Figure shows how agricultural land changes within PG County and Right Figure shows corresponding change in nitrogen loading
Case 2 Case 1
Darker shade means bigger Ag loss Green = Loading Decrease Red = Loading Increase
Percent change in nitrogen loading, Montgomery County, current vs. various scenarios.
Reality Check
Base Case
High Energy Prices
Percent change in nitrogen loading, Allegany County, current vs. various scenarios.
Reality Check
Base Case
High Energy Prices
Percent change in nitrogen loading, Caroline County, current vs. various scenarios.
Reality Check
Base Case
High Energy Prices
County Measure Base Case High Gas Prices Reality Check
Montgomery Net Change 8.8 11.5 1.7Gross Shift 17.9 21.3 2.8
Prince Georges Net Change -264.8 -306.6 -137.4Gross Shift 322.9 362.0 148.4
Allegany Net Change 10.1 14.1 19.9Gross Shift 20.1 23.0 19.9
Caroline Net Change -38.3 -19.6 -26.2Gross Shift 39.9 20.2 27.4
County-Wide Aggregate Changes in Nitrogen Loading
All values in tons/year.
Results: Why future loadings may Results: Why future loadings may be more (or less) than current be more (or less) than current loadings:loadings: Loading Rates (lbs/acre-Loading Rates (lbs/acre-
year)year)(typical – though they do vary across the (typical – though they do vary across the
Bay watershed)Bay watershed)
– Agricultural: 14.6Agricultural: 14.6– Forest: 1.4Forest: 1.4– Urban: 8.9Urban: 8.9– Water: 9.8Water: 9.8
Case #1 converts forest Case #1 converts forest into urban land (e.g. into urban land (e.g. Allegany)Allegany)
Case #2 converts more Case #2 converts more agricultural land than agricultural land than forest land (e.g. Caroline)forest land (e.g. Caroline)
agriculture
forest
urban agriculture
forest
urban
agriculture
forest
urban
agriculture
forest
urban
Case #1 Case #2
Preliminary results show modest NET load changes Preliminary results show moderate GROSS load changes
(~20%, locally higher) Aside: BMPs are thought to mitigate loadings by ~10 to
20% Gross Load Changes are shifted in space so different
watersheds may be significantly affected. Sign (+/-) of loading change:
Agricultural to Urban: loading reduction Forest to Urban: loading increase Urbanization of Agricultural land as a means of load
reduction?!
Interpretation and Future Work:
Residential Energy Residential Energy ModelModel Space conditioning accounts for a significant portion of Space conditioning accounts for a significant portion of
all end use energy consumed across sectors. all end use energy consumed across sectors. – 58% of energy consumption in residential households 58% of energy consumption in residential households
(EIA, 1999)(EIA, 1999)– 40% of energy consumption for commercial buildings 40% of energy consumption for commercial buildings
(EIA, 1995)(EIA, 1995)– 6% of energy consumption in industrial facilities (EIA, 6% of energy consumption in industrial facilities (EIA,
2001)2001)– Roughly 22% of all end-use energy consumption in Roughly 22% of all end-use energy consumption in
the country is used for space conditioning (Amato, the country is used for space conditioning (Amato, 2005)2005)
Methodology
Vintage Model
(MDP)
(EIA)
Methodology
Climate
(NCSD)
(UCS)
Number of Households
(County Level)
Housing Mix
(County Level)
Average Household Total
Energy Consumption
(by County)
Methodology
Housing Characteristics (RECS)
Climate: Degree Days
Figure from Amato et al., 2005
Heating Degree Days 9.376
(6521.13)**Cooling Degree Days 5.437
(2010.08)**Single Family Attached (dummy)
-10800.890
(1369.24)**Multifamily (2-4 units) (dummy)
-11136.080
(1078.79)**Multifamily (5+) (dummy) -35516.100
(5816.30)**City (dummy) 13656.060
(1958.73)**Town (dummy) 9736.322
(1212.90)**Suburb (dummy) 16800.160
(2147.03)**totsqft 16.859
(5187.00)**afue -194513.800
(2767.32)**housing stock age 528.874
(3894.88)**Constant 145201.900
(2729.42)**Observations 4.16e+08R-squared 0.41Robust t-statistics in parentheses* significant at 5% level; ** significant at 1% level
Positive relationship between degree-days and household energy consumption.
Single-family detached households consume more energy than all other housing types.
Rural areas consume less energy than other locations, all else equal.
Positive relationship between square footage and total household energy consumption.
Efficiency improvements reduce household energy demand.
Older homes consume more energy than newer homes.
MD-Climate Divisions
MD-Heating Degree Days by Climate Division
MD-Cooling Degree Days by Climate Division
Montgomery County, various scenarios.
Reality Check
Base Case
High Energy Prices
Total Energy Consumption
BTU
Prince Georges County, various scenarios.
Reality Check
Base Case
High Energy Prices
Total Energy Consumption
BTU
Allegany County,various scenarios.
Reality Check
Base Case
High Energy Prices
Total Energy Consumption
BTU
Caroline County, various scenarios.
Reality Check
Base Case
High Energy Prices
BTU
Montgomery County, Per Capita, various scenarios.
Reality Check
Base Case
Per Capita Energy Consumption
BTU
High Energy Prices
Allegany County, Per Capita, various scenarios.
Reality Check
Base Case
High Energy Prices
Per capita Energy Consumption
BTU
Notes
The results are preliminary Energy consumption are different in
various scenarios because –– The number of households are different– The spatial arrangement of households are
different– The climate zones they are in are different– The densities they cluster around are
different (i.e. Urban vs. Rural.)– The mix of housing types (single family vs.
Multifamily etc.) are different
Where do we go from Where do we go from here?here? Refine both bottom up and top down Refine both bottom up and top down
land use models;land use models; Integrate land use and transportation Integrate land use and transportation
models;models; Link land use/transportation models Link land use/transportation models
with Bay model;with Bay model; Develop “what would it take” scenario;Develop “what would it take” scenario; Engage public in scenario evaluation;Engage public in scenario evaluation;
Scenario TestingScenario Testing
Business as usualBusiness as usual High Energy Price (Concentrated High Energy Price (Concentrated
Growth)Growth) Resource land protectionResource land protection Transit Oriented DevelopmentTransit Oriented Development What would it takeWhat would it take Build OutBuild Out
Thanks to our Thanks to our sponsorssponsors US EPAUS EPA Maryland State Highway AdministrationMaryland State Highway Administration Maryland Department of TransportationMaryland Department of Transportation Maryland Department of PlanningMaryland Department of Planning University of Maryland Transportation CenterUniversity of Maryland Transportation Center Cafritz FoundationCafritz Foundation Maryland Sea Grant ProgramMaryland Sea Grant Program Chesapeake Bay TrustChesapeake Bay Trust Lincoln Institute of Land PolicyLincoln Institute of Land Policy
The National Center for Smart GrowthResearch and Education
Suite 1112, Preinkert Field HouseCollege Park, Maryland 20742
301.405.6788www.smartgrowth.umd.edu
Dr. Glenn E. MoglenDept.of Civil and Environmental
Engineering, Virginia Tech 7054 Haycock Road
Falls Church, VA 22043 703.538.3786
Center for Integrative Environmental Research
2101 Van Munching Hall College Park, Maryland 20742
301.405.3988 www.cier.umd.edu
