1. 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, University of Maryland September 9, 2009

2. Presentation Outline 1. Project Foundations; 2. The Maryland Scenario Project; 3. Model Development; 4. Nutrient Loading Model; 5. Residential Energy Model; 6. Yet to do.

3. PROJECT FOUNDATIONS

4. Today’s VISION… Tomorrow’s REALITY

5. Baltimore Convention Center

6. Compared with Buildout and COG forecasts, RCP results would have.. More jobs and housing close to transit; More jobs and housing close to transit; More jobs and housing inside priority funding areas; More jobs and housing inside priority funding areas; Less development on green infrastructure; and Less development on green infrastructure; and Less new impervious surfaces; Less new impervious surfaces; Fewer vehicle miles traveled. Fewer vehicle miles traveled.

7. The Maryland Scenario Project

8. The purpose of the Maryland Scenario Project is…. To take an informed and careful look at alternative long-term future scenarios; To take an informed and careful look at alternative long-term future scenarios; To conduct a quantitative assessment of each scenario; To conduct a quantitative assessment of each scenario; To identify where and how public policy decisions will increase the likelihood of more desirable scenarios; To identify where and how public policy decisions will increase the likelihood of more desirable scenarios; (To lay the foundation for a state development plan.) (To lay the foundation for a state development plan.)

9. Washington Post, 7/5/08

12. Capital Diamond

13. Model Development

14. Modeling and Analysis Infrastructure Regional econometric model Regional econometric model Regional transportation model Regional transportation model Regional land use model Regional land use model Nutrient loading model Nutrient loading model Residential energy consumption model Residential energy consumption model Fiscal impact model Fiscal impact model Greenhouse gas model Greenhouse gas model

15. Modeling Frameworks Econometric Models Land Use Model Transportation Model Nutrient Loading Model Energy Consumption Model Indicators Exogenous Factors Land Use Policies Air Quality Model

16. Metro National GNP LIFT model State GSP STEMS model County Regional JOBS & HH (SMZ) MetroCounty UMD INFORUM Hammer NCSG Trends from BEA & BLS Land Uses 30m grid LEAM Land Cover and input data TOP DOWN BOTTOM UP MDP Growth Model Economy Environment MDP NCSG Top Down / Bottom Up Land Use Models

17. 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 zones Arterial network; External Stations Short Distance Person Travel model Trip Generation Trip Distribution Mode Split Assignment MWCOG BMC 3-Level Transport Model

18. Constructing a High Energy Price Growth Scenario

19. Difference in # of jobs in the US Difference in # of jobs in MD

20. Difference in # of jobs by industry in the US Difference in # of jobs By industry in MD

21. In 2040 High Energy

22. In 2040 High Energy

23. Congested links under alternative scenarios High Energy Price Business as Usual

24. SCENARIO ANALYSIS GROUP MD-LEAM - LAND USE MODEL LEAM LAB, University of Illinois, Urbana-Champaign

25. Growth - 2040

26. Effects of Transportation Investments on Development Patterns

27. 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 model

28. 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?

29. 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

30. 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

31. Allegany Prince Georges Montgomery Caroline Land Use Distribution in Focus Counties

32. Percent change in nitrogen loading, Prince Georges County, current vs. various scenarios. Reality Check Base Case High Energy Prices

33. 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

34. Percent change in nitrogen loading, Montgomery County, current vs. various scenarios. Reality Check Base Case High Energy Prices

35. Percent change in nitrogen loading, Allegany County, current vs. various scenarios. Reality Check Base Case High Energy Prices

36. Percent change in nitrogen loading, Caroline County, current vs. various scenarios. Reality Check Base Case High Energy Prices

37. CountyMeasureBase CaseHigh Gas PricesReality Check MontgomeryNet Change 8.811.51.7 Gross Shift 17.921.32.8 Prince GeorgesNet Change -264.8-306.6-137.4 Gross Shift 322.9362.0148.4 AlleganyNet Change 10.114.119.9 Gross Shift 20.123.019.9 CarolineNet Change -38.3-19.6-26.2 Gross Shift 39.920.227.4 County-Wide Aggregate Changes in Nitrogen Loading All values in tons/year.

38. Results: Why future loadings may be more (or less) than current loadings: Loading Rates (lbs/acre- year) Loading Rates (lbs/acre- year) (typical – though they do vary across the Bay watershed) –Agricultural: 14.6 –Forest: 1.4 –Urban: 8.9 –Water: 9.8 Case #1 converts forest into urban land (e.g. Allegany) Case #1 converts forest into urban land (e.g. Allegany) Case #2 converts more agricultural land than forest land (e.g. Caroline) Case #2 converts more agricultural land than forest land (e.g. Caroline) Case #1Case #2 Current Future

39. 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:

40. Residential Energy Model Space conditioning accounts for a significant portion of all end use energy consumed across sectors. Space conditioning accounts for a significant portion of all end use energy consumed across sectors. –58% of energy consumption in residential households (EIA, 1999) –40% of energy consumption for commercial buildings (EIA, 1995) –6% of energy consumption in industrial facilities (EIA, 2001) –Roughly 22% of all end-use energy consumption in the country is used for space conditioning (Amato, 2005)

41. Methodology RECS Data (EIA) Housing Characteristics (RECS) Household Energy Consumption Climate (RECS) (NCSD) Household Equipment Efficiency Vintage Model (MDP) (EIA)

42. Methodology Household Energy Consumption Model Housing SizeHousing Type Densities (Urban versus Rural) County Level Indicators Climate (NCSD) (UCS) Number of Households (County Level) Housing Mix (County Level) Average Household Total Energy Consumption (by County)

43. Methodology Climate Variables Heating Degree Days Cooling Degree Days Housing Characteristics Housing Type (RECS) Square Footage (RECS) Age (RECS) Location (RECS) Equipment Stock Efficiency (Vintage Model)

44. Housing Characteristics (RECS) Housing Location RuralSuburbanCityTown Housing Type Single-family attached Single-family detached Multifamily (2 to 4 units) Multifamily (5+ units)

45. Climate: Degree Days Figure from Amato et al., 2005

46. Heating Degree Days9.376 (6521.13)** Cooling Degree Days5.437 (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)** totsqft16.859 (5187.00)** afue-194513.800 (2767.32)** housing stock age528.874 (3894.88)** Constant145201.900 (2729.42)** Observations4.16e+08 R-squared0.41 Robust 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.

47. MD-Climate Divisions

48. MD-Heating Degree Days by Climate Division

49. MD-Cooling Degree Days by Climate Division

52. Montgomery County, various scenarios. Reality Check Base Case High Energy Prices Total Energy Consumption BTU

53. Prince Georges County, various scenarios. Reality Check Base Case High Energy Prices Total Energy Consumption BTU

54. Allegany County,various scenarios. Reality Check Base Case High Energy Prices Total Energy Consumption BTU

55. Caroline County, various scenarios. Reality Check Base Case High Energy Prices BTU

56. Montgomery County, Per Capita, various scenarios. Reality Check Base Case Per Capita Energy Consumption BTU High Energy Prices

57. Allegany County, Per Capita, various scenarios. Reality Check Base Case High Energy Prices Per capita Energy Consumption BTU

58. 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

59. Where do we go from here? Refine both bottom up and top down land use models; Refine both bottom up and top down land use models; Integrate land use and transportation models; Integrate land use and transportation models; Link land use/transportation models with Bay model; Link land use/transportation models 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;

60. Scenario Testing Business as usual Business as usual High Energy Price (Concentrated Growth) High Energy Price (Concentrated Growth) Resource land protection Resource land protection Transit Oriented Development Transit Oriented Development What would it take What would it take Build Out Build Out

61. Thanks to our sponsors US EPA US EPA Maryland State Highway Administration Maryland State Highway Administration Maryland Department of Transportation Maryland Department of Transportation Maryland Department of Planning Maryland Department of Planning University of Maryland Transportation Center University of Maryland Transportation Center Cafritz Foundation Cafritz Foundation Maryland Sea Grant Program Maryland Sea Grant Program Chesapeake Bay Trust Chesapeake Bay Trust Lincoln Institute of Land Policy Lincoln Institute of Land Policy

62. The National Center for Smart Growth Research and Education Suite 1112, Preinkert Field House College Park, Maryland 20742 301.405.6788 www.smartgrowth.umd.edu Dr. Glenn E. Moglen Dept.of Civil and Environmental Engineering, Virginia Tech 7054 Haycock Road Falls Church, VA 22043 703.538.3786 moglen@vt.edu Center for Integrative Environmental Research 2101 Van Munching Hall College Park, Maryland 20742 301.405.3988 www.cier.umd.edu