Grantee Research Project Results
Final Report: Reality Check Plus: Envisioning a Sustainable Maryland
EPA Grant Number: R833346Title: Reality Check Plus: Envisioning a Sustainable Maryland
Investigators: Knaap, Gerrit , Moglen, Glenn E. , Ruth, Matthias
Institution: University of Maryland - National Center for Smart Growth
EPA Project Officer: Hahn, Intaek
Project Period: March 1, 2007 through April 30, 2010 (Extended to February 28, 2012)
Project Amount: $274,060
RFA: Collaborative Science And Technology Network For Sustainability (2006) RFA Text | Recipients Lists
Research Category: Pollution Prevention/Sustainable Development , Sustainable and Healthy Communities
Objective:
The objective of this project is to explore alternative development scenarios for the State of Maryland and provide estimates of how land development can affect energy consumption and surface water quality. The project builds on the Maryland Scenario Project—a long-term, multi-disciplinary effort to explore alternative futures for the State of Maryland supported by funds from a variety of sources. Work under this grant focuses on two specific areas:
(1) Energy consumption and surface water quality. Water quality impacts of alternative development scenarios will be estimated using a GIS-based model that yields estimates of nitrogen, phosphorus, and sediment loadings into stream segments throughout the state.
(2) Impacts on energy consumption will be estimated using statistical models that capture interactions among urban development patterns, local climate change, and energy consumption.
Summary/Accomplishments (Outputs/Outcomes):
In 2007, the NCSG began the process of elaborating several loosely coupled models to evaluate alternative development strategies for the state development plan. Several of these models were funded from other sources and some were not even under consideration when the CNS grant was awarded. These models, designed both to help create spatially explicit development scenarios and provide quantitative measures of their consequences, include land use, transportation, fiscal impact, water quality, and energy consumption models. The modeling system includes a national econometric model, a land use model, a transportation model and several impact models. A model exercise begins with a national economic forecast of employment, output, and prices of 95 sectors of the U.S. economy. The output of this model is disaggregated to the state level where it is fed into the land use model. The land use model then allocates jobs by industrial sector and household by income level into State Modeling Zones (SMZs). Jobs and households at the SMZ level are fed into the state transportation model, which then forecasts traffic volumes on each link in the model. The outputs of the transportation and land use models are fed into impact models that identify the impacts of each scenario on public expenditures, stream quality, residential energy use, and more.
Preliminary results reveal that the models produce sensible results and can be loosely coupled. What’s more, the output from a series of models tells a coherent story. Higher gas prices lead to declines in economic activity, a more compact urban development pattern, less need for highway expansion, less impervious surface, and lower energy consumption. The nutrient loading and energy consumption models must be interpreted carefully, however. More compact development leads to less energy consumption, but sprawl development, if it results in the urban development of poorly managed farms (defined as farming without the use of best management practices), can lead to lower levels of nutrient loading.
Since the last progress report, and with funding from other sources, we have expanded the geographic scope and precision of the models. Specifically, we have expanded the scope of the models to run from Wilmington, DE, to Virginia Beach, VA, and we have calibrated the transportation and land use models to meet standards in transportation research. We also have developed five basic land use scenarios and conducted several transportation experiments on each scenario. These scenarios reveal that investments in transit and more transit friendly development patterns lead to fewer vehicle miles traveled and more transit ridership.
Conclusions:
Analyses of preliminary scenarios reveal that all the models produce plausible results and connect well. Preliminary results suggest that development patterns are sensitive to economic and policy parameters and differences in development patterns are manifest in measurable differences in nutrient loading, residential energy consumption, vehicle miles traveled, impervious surface, and other environmental variables. Combined, the results reveal that managing land use and urban development patterns can serve as an effective tool for environmental conservation and improvement.
Journal Articles on this Report : 14 Displayed | Download in RIS Format
| Other project views: | All 33 publications | 14 publications in selected types | All 14 journal articles |
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Chakraborty A. Scenario planning for effective regional governance:promises and limitations. State and Local Government Review 2010;42(2):156-167. |
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Chakraborty A, Kaza N, Knaap G-J, Deal B. Robust plans and contingent plans: scenario planning for an uncertain world. Journal of the American Planning Association 2011;77(3):251-266. |
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Chakraborty A. Enhancing the role of participatory scenario planning processes:lessons from Reality Check exercises. Futures 2011;43(4):387-399. |
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Chakraborty A, Mishra S, Kim YW. Planning support systems and planning across scales:comparing scenarios using multiple regional delineations and projections. URISA Journal 2012;24(1):53-62. |
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Chakraborty A, Mishra S. Land use and transit ridership connections: implications for state-level planning agencies. Land Use Policy 2013;30(1):458-469. |
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Cimren E, Bassi A, Fiksel J. T21-Ohio, a system dynamics approach to policy assessment for sustainable development: a waste to profit case study. Sustainability 2010;2(9):2814-2832. |
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Kaza N, Towe CA, Ye X. A hybrid land conversion model incorporating multiple end uses. Agricultural and Resource Economics Review 2011;40(3):341-359. |
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Kaza N, Knaap GJ, Knaap I, Lewis R. Peak oil, urban form, and public health:exploring the connections. American Journal of Public Health 2011;101(9):1598-1606. |
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Mejía AI, Moglen GE. Spatial patterns of urban development from optimization of flood peaks and imperviousness-based measures. Journal of Hydrologic Engineering 2009;14(4):416-424. |
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Mejía AI, Moglen GE. Spatial distribution of imperviousness and the space-time variability of rainfall, runoff generation, and routing. Water Resources Research 2010;46(7):W07509 (14 pp.). |
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Mejía AI, Moglen GE. Impact of the spatial distribution of imperviousness on the hydrologic response of an urbanizing basin. Hydrological Processes 2010;24(23):3359-3373. |
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Mishra S, Ye X, Ducca F, Knaap G-J. A functional integrated land use-transportation model for analyzing transportation impacts in the Maryland-Washington, DC region. Sustainability: Science, Practice & Policy 2011;7(2):60-69. |
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Mishra S, Welch TF, Jha MK. Performance indicators for public transit connectivity in multi-modal transportation networks. Transportation Research Part A: Policy and Practice 2012;46(7):1066-1085. |
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Mishra S, Welch TF, Chakraborty A. Experiment in megaregional road pricing using advanced commuter behavior analysis. Journal of Urban Planning and Development 2014;140(1):04013007. |
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Supplemental Keywords:
Urban development, residential energy consumption, nutrient loading. , RFA, Air, climate change, Air Pollution Effects, AtmosphereProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.