Grantee Research Project Results
2006 Progress Report: A Long Term Integrated Framework Linking Urban Development, Demographic Trends and Technology Changes to Stationary and Mobile Source Emissions
EPA Grant Number: R831841Title: A Long Term Integrated Framework Linking Urban Development, Demographic Trends and Technology Changes to Stationary and Mobile Source Emissions
Investigators: Anas, Alex , Hewings, Geoffrey
Institution: The State University of New York at Buffalo , University of Illinois Urbana-Champaign
EPA Project Officer: Chung, Serena
Project Period: November 1, 2004 through October 31, 2007 (Extended to October 31, 2010)
Project Period Covered by this Report: November 1, 2005 through October 31, 2006
Project Amount: $675,000
RFA: Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions (2004) RFA Text | Recipients Lists
Research Category: Climate Change , Air
Objective:
The project is developing an integrated modeling package, a tool for testing trends, and policies that together determine future emission levels. The precise aims of the model integration effort targeted by the project are as follows:
We seek a tool capable of producing long-term (25–50-year) projections of stationary- and mobile-source emissions in a metropolitan area. The way emission models are used is at best accurate for short horizons, taking as given local projections of local economic activity and population change. Instead of simply extrapolating these local trends, we seek to explain them, by modeling the fundamental behavioral relationships among individuals and firms and by linking these underlying economic relationships to secular national and international trends in population, economic development, and technological changes relevant to emissions. Among the specific demographic trends to be examined are the graying of the population, reductions in household size, and international immigration. We will also examine the possibility of the continued deindustrialization of U.S. manufacturing and its impact on a metropolitan area with considerable manufacturing, using the Chicago Metropolitan Statistical Area (MSA). We will consider new technologies likely to impact emissions, such as electric vehicles and hydrogen fuel-cell vehicles, as well as electricity/energy production with a higher renewable fuel mix under higher sustained energy prices.
The proposed modeling framework is a dynamic computable general equilibrium model (DCGEM) for a metropolitan area based on principles of economic behavior that can be calibrated well from existing data. The submodels are: (1) The Regional Economy and Land Use (RELU) model developed at SUNY-Buffalo , which treats the location, production, and transportation decisions of both businesses and consumers and the land development decisions of developers under a variety of government taxes and subsidies; (2) TRAN, a model of highway-transit modal choice and congested travel conditions on highway networks developed at SUNY-Buffalo; (3) a model of the energy/electricity sector and its relation to demand from industrial production, residential use, and vehicular transportation to be developed in this project; and (4) models of emissions from mobile and stationary sources, including those from production outside the electricity sector. We will interface these models to establish feedbacks among them consistently with microeconomic theory so that they will allow simulation of the workings of the Chicago regional economy.
The research will provide a tool that is portable from one metropolitan area to another. It will help identify the policies or trends that are the most effective in reducing emissions, and specify how quickly and at what cost. It will determine whether the benefits of two policies or planning actions are sub-additive or super-additive. Hence, it could help direct future research and provide guidance to future policy initiatives that are economically and politically viable. Application of the model to other regions in the future would increase understanding of whether the policy actions should vary among metropolitan regions or whether the same policies are effective everywhere.
Progress Summary:
The project’s accomplishments to date are described below.
RELU-TRAN
Development of the TRAN model and integration of the RELU model with the TRAN model for a representation of the Chicago MSA consisting of 15 geographic zones spanning the city, the suburbs and the exurban area, 4 industries (agriculture, manufacturing, business services and retail trade), 4 consumer income quartiles, 4 building types (single-family residential, multiple-family residential, commercial, and industrial), undeveloped land, and an aggregated representation of the highway network. The total number of equations solved by RELU-TRAN is 656. The model is capable of predicting the effects of changes in transportation capacities, land use policies, a variety of taxes, as well as exogenous trends of population increase, export expansion, and improved industrial productivity on the regional economy, transportation, and land use changes at the metropolitan level.
As explained in the “Objectives of the Research Project” section above, a model such as RELU-TRAN that simulates the working of the regional economy, its land use, and travel is centerpiece in an effort to correctly forecast emissions in urban areas. Since emissions are a byproduct of economic activity, they cannot be forecast well unless the underlying economic activity is forecast properly. Since RELU-TRAN is a model that relies on the state-of-the-art theory in the urban economics and transportation fields, it plays a key role in the assembly of models that the project aims to develop.
There are many examples of practical applications of the model assembly that is being developed. One is the use of aggressive land development restrictions to limit urban sprawl and the expansion of low-density suburban areas. Such policies would, in the long run, result in higher density developments in the built-up areas, resulting also in lower vehicle miles traveled, more traffic congestion and more transit use. This in turn, would alter energy/electricity consumption by residences and by vehicles (depending also on the types of vehicles being used as the vehicle fleet changes via car ownership decisions). Emissions, in turn, will be affected. The assembly of models would trace all of these effects, but it would also be able to quantify other costs and benefits of the aggressive land use policies, such as higher rents and other price distortions in the markets for labor and urban products.
Accomplishments in the Period November 1, 2005–October 31, 2006
- RELU-TRAN.
- Static RELU-TRAN: First, the stationary-state (static) RELU-TRAN algorithm was improved and finalized. This required extensive experimentation with alternative solution strategies and the best (in our experience) was selected. Depending on the starting point, the static RELU-TRAN takes anywhere from 1 to 3 plus hours to compute a solution. All of this work was done at SUNY-Buffalo.
- Dynamic RELU-TRAN: We extended the RELU-TRAN model, described above, to make it dynamic and capable of making non-stationary simulations. The dynamic version currently simulates over a sequence of years, calculating land use change in every year. At the end of the time-horizon, the forecasts of the dynamic version converge to a stationary state. The stationary/non-stationary RELU-TRAN is the most complex and complete numerical dynamic programming algorithm ever developed within urban economics. It follows a solution procedure in which the terminal stationary state, which holds at the end of the finite time horizon, is solved first. Then, this solution is used to start the non-stationary phase. The non-stationary phase is then solved by successive backward-in-time and forward-in-time recursions, until convergence to a dynamic equilibrium non-stationary path is obtained. The dynamic RELU-TRAN model is a perfect foresight model. Hence, it has the capability of predicting changes that will result from current investments and policy instruments, as well as predicting how the economy would respond in advance to known future changes. For example, investing in highways at the beginning of the time horizon would generate changes in metropolitan gross product, land use and travel in the future. Alternatively, investing in highways in the future (e.g., at the terminal time) would generate anticipatory changes in gross product and land use in earlier years and travel changes that would be influenced by these land use changes. All of this work was done at SUNY-Buffalo.
- Energy/Electricity Sectors: We have also finished most of the preliminary work (by October 31, 2006) needed in order to extend the RELU-TRAN model to incorporate the three energy-electricity related extensions. These are: (i) extending the model to generate energy demand by residential buildings (e.g., for heating/air conditioning and other uses); (ii) extending the model of the RELU consumer to introduce vehicle efficiency choice; (iii) extending the model of inter-industry trade in RELU by adding an energy industry and by modeling the interrelationships among the other RELU industries and the energy industries.
- Demographic Submodel. The demographic submodel has also been completed and requires only some minor adjustments. The purpose of the demographic submodel is to provide RELU, which is currently based on individual consumers, with a household structure on the demand side. We have developed a model that divides people into 33 demographic cohorts. Each household consists of either one or two adults and a household age is assigned to it. Households age and have children (fertility) and can either split up or adults can merge into a household. The equations of the model have been developed, and the model has been calibrated from Chicago data. The model is designed to capture/forecast the aging of the population and the changing of household sizes.
A prior award from the National Science Foundation’s 1998 Urban Research Initiative competitive proposal solicitation to Alex Anas at SUNY-Buffalo contributed to the development of the RELU-TRAN model. The article, to be published, describes the full model and its solution algorithm and presents tests of the algorithm’s convergence to equilibrium for the stationary-state RELU-TRAN model.
The article mentioned above was presented in a seminar at the University of Illinois on February 13, 2006 and at a seminar at the Department of Geography, McMaster University on November 10, 2006.
Aspects of the RELU-TRAN model (though not the full article) also were presented by Dr. Anas in three other conferences during the summer of 2006. These conferences are listed in the next section. Although the two journal articles published (Anas and Rhee, 2006; Anas and Rhee, 2007) are not directly related to the current project, they report results that are theoretical but closely related to the conceptual structure of RELU-TRAN. Therefore, their acceptance by the field supports the current project by fleshing out at the conceptual level transportation and land use interaction issues that form the basis of more extensive, empirical investigation in the current project. An article entitled, A unified theory of consumption, travel and trip chaining, accepted for publication in Journal of Urban Economics, was written entirely with the current project in mind and support of the current project is cited in the Acknowledgments section. The article develops a theory of complex trip-making in urban areas. Such a theory has strong relevance to energy expenditure and emissions because the fastest growing part of trip-making in urban areas has shifted from commuting to complex personal discretionary trips.
Future Activities:
The work remaining in the project is the consistent integration into RELU-TRAN of the vehicle choice, residential energy demand, and inter-industry energy demand sectors and the calibration of each of these submodels in a way that is consistent with the calibration of RELU-TRAN. Collaboration with Regional Economics Applications Laboratory (REAL) University of Illinois at Urbana-Champaign (UIUC) will be essential in these tasks. Finally, the U.S. Environmental Protection Agency (EPA)’s emission models must be linked to the modeling assembly.
Subsequently, the whole modeling assembly must be tested for consistent unified behavior.
After this testing is accomplished, the entire modeling assembly will be used to make dynamic simulations and to focus on how various policies, taxes, and finance schemes are affecting urban development, land use, energy use, and emissions.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
Other project views: | All 24 publications | 13 publications in selected types | All 8 journal articles |
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Type | Citation | ||
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Anas A, Rhee HJ. Curbing excess sprawl with congestion tolls and urban boundaries. Regional Science and Urban Economics 2006;36(4):510-541. |
R831841 (2005) R831841 (2006) |
Exit Exit |
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Anas A, Rhee HJ. When are urban growth boundaries not second-best policies to congestion tolls? Journal of Urban Economics 2007;61(2):263-286. |
R831841 (2006) |
Exit |
Supplemental Keywords:
air, air quality, analytical, behavioral models, business location, clean technologies, consumer behavior, cost-benefit analysis, decision making, demand for electricity, demographic change, dynamic computable general equilibrium (DCGE) modeling economic behavior, economics, electric vehicles, electricity production, emissions, EMME/2, environmental regulation, future emissions, general equilibrium, global change, greenhouse gas, greenhouse gases, housing location, hydrogen vehicles, industrial location, infrastructure, land use, land use modeling, land use policies, long run, long-run effects, long term, long-term impact, long-term effects, manufacturing, mobile source, mobile sources, modeling, motor vehicle emissions, nitrogen oxides, ozone, particulate matter, particulates, preferences, public policy, regional development, residential location, sensitivity analysis, smart growth policies, social science, socioeconomic, stationary source, stationary sources, technological change, traffic congestion, transportation, transportation infrastructure, transportation modeling, travel demand, travel pattern, uncertainty, urban development, vehicle emissions, vehicle miles traveled, vehicle ownership, vehicular technology, VMT, VOC, Illinois (IL), Midwest, Chicago MSA,, RFA, Scientific Discipline, Air, Air Quality, Environmental Chemistry, climate change, Air Pollution Effects, mobile sources, Environmental Monitoring, Ecological Risk Assessment, Ecology and Ecosystems, Urban and Regional Planning, Atmosphere, ecosystem models, infrastructure systems, traffic, engine exhaust, modeling regional scale ozone, vehicle emissions, human activities, motor vehicle emissions, Emissions Inventory Modeling System, air quality models, ozone, automotive emissions, traffic patterns, automobiles, automotive exhaust, green house gas concentrations, modeling, mobile source emissions, emissions impact, atmospheric pollutant loads, tropospheric ozone, regional emissions model, global warming, predicting ecological response, climate variability, community structure, Global Climate Change, ambient air pollutionRelevant Websites:
http://www.acsu.buffalo.edu/~alexanas/Current%20Project.html
Progress 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.
Project Research Results
- Final Report
- 2009 Progress Report
- 2008 Progress Report
- 2007 Progress Report
- 2005 Progress Report
- Original Abstract
8 journal articles for this project