Envisioning a Sustainable Maryland:Â

1. 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 Civil and Environmental Engineering, Virginia Tech MatthiasRuth, Director Center for Integrative Environmental Research, University of Maryland

2. Presentation Outline Project Foundations; The Maryland Scenario Project; Model Development; Nutrient Loading Model; Residential Energy Model; 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 inside priority funding areas; • Less development on green infrastructure; and • Less new impervious surfaces; • 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 conduct a quantitative assessment of each scenario; • 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.)

9. Washington Post, 7/5/08

12. Capital Diamond

13. Model Development

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

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

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

17. 3-Level Transport Model • Top Level: National View • County/state zones; Interstate road/transit network • Economic Forecast model • FAF Commodity Flow model • Long Distance Person Travel 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 • Bottom Level: MPO View • MPO TAZs; Sub-arterial network • No statewide modeling occurs • MPO model data aggregation to • compare with middle layer Statewide model BMC MWCOG

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. High Energy In 2040 High Energy

22. High Energy In 2040 High Energy

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

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

25. Growth - 2040

26. Effects of Transportation Investments on Development Patterns

27. Nutrient loading model Forecast Data (housing, employment) RESAC Land Cover Future Land Use Current Land Use CBPO in GISHydro Chesapeake Bay Program Model Loading Coefficients Current Nutrient Loads (N, P, Sed.) Future Nutrient Loads (N, P, Sed.)

28. What is Forecast Data? • 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)

29. Converting Forecast Data into Future Land Use – Heuristic Rules • 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.

30. 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.

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

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

33. Land Use and Nutrient Loading changes in PG Case 1 Case 2 Left Figure shows how agricultural land changes within PG County and Right Figure shows corresponding change in nitrogen loading Darker shade means bigger Ag loss Green = Loading Decrease Red = Loading Increase

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

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

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

37. 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) (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 #2 converts more agricultural land than forest land (e.g. Caroline) Current Case #1 Case #2 Future

39. Interpretation and Future Work: • 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?!

40. Residential Energy Model • 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 Vintage Model (MDP) (EIA)

42. Methodology Climate (NCSD) (UCS) Average Household Total Energy Consumption (by County) Housing Mix (County Level) Number of Households (County Level)

43. Methodology

44. Housing Characteristics (RECS)

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

46. 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