How the way we build cities and communities affects thequality of the air that we breathe

Poor air quality is still a major issue affecting a large number of Americans. In new research, Nikhil Kaza and Josh McCarty write that how urban areas are laid out can make a difference to local air quality. Using remote sensing data for continental United States, they find that both sprawling cities and mixed urban and rural counties are more associated with poor air quality. The more fragmented communities are, the worse the local air quality. They argue that increasing the amount of forest cover near to these areas will help to improve air quality

Evaluating the impacts of the clean cities program

The Department of Energy's Clean Cities program was created in 1993 to reduce petroleum usage in the transportation sector. The program promotes alternative fuels such as biofuels and fuel-saving strategies such as idle reduction and fleet management through coalitions of local government, non-profit, and private actors. Few studies have evaluated the impact of the program because of its complexity that include interrelated strategies of grants, education and training and diversity of participants. This paper uses a Difference-in-Differences (DiD) approach to evaluate the effectiveness of the program between 1990 and 2010. We quantify the effectiveness of the Clean Cities program by focusing on performance measures such as air quality, number of alternative fueling stations, private vehicle occupancy and transit ridership. We find that counties that participate in the program perform better on all these measures compared to counties that did not participate. Compared to the control group, counties in the Clean Cities program experienced a reduction in days with bad air quality (3.7%), a decrease in automobile commuters (2.9%), an overall increase in transit commuters (2.1%) and had greater numbers of new alternative fueling stations (12.9). The results suggest that the program is a qualified success

Characterizing the Regional Structure in the United States: A County-based Analysis of Labor Market Centrality

Categorizing places based on their network connections to other places in the region reveals not only population concentration but also economic dynamics that are missed in other typologies. The US Office of Management and Budget categorization of counties into metropolitan/micropolitan and central/outlying is widely seen as insufficient for many analytic purposes. In this article, we use a coreness index from network analysis to identify labor market centrality of a county. We use county-to-county commute flows, including internal commuting, to identify regional hierarchies. Indicators broken down by this typology reveal counterintuitive results in many cases. Not all strong core counties have large populations or high levels of urbanization. Employment in these strong core counties grew faster in the postrecession (2008–2015) than in other types of counties. This economic dimension is missed by other typologies, suggesting that our categorization may be useful for regional analysis and policy

A Hybrid Land Conversion Model Incorporating Multiple End Uses

The need for models that forecast land use change spans many disciplines and encompasses many approaches. Pattern-based models were the first in which projections of change at specific locations in actual landscapes could be predicted. In contrast, recent economic models have modeled the underlying behavioral process that produces land use change. This paper combines attributes from each approach into a hybrid model using a multiple discrete continuous extreme value formulation that allows for multiple conversion types, while also estimating the intensity of each type of conversion, which is an important but often overlooked dimension. We demonstrate the simulation routine, which successfully predicts a majority of growth by type, time, and location at a disaggregated scale, for a three-county region in Maryland.MDCEV, land conversion, regional planning, urban growth policy, Land Economics/Use,

Radical Uncertainty: Scenario Planning for Futures

The use of scenario planning in urban and regional planning practice has grown in the last decade as one way to face uncertainty. However, in adapting scenario planning from its origins in the business sector, planners have eliminated two key components: (1) the use of multiple scenarios, and (2) the inclusion of diverse organizations, people, and interests through deep deliberations. We argue that this shift limits the ability of planners to plan for multiple plausible futures that are shaped by an increasing number of diverse actors. In this paper, we use case-study research to examine how uncertainty was considered in four scenario-planning processes. We analyzed and compared the cases based on analytical categories related to multiple futures and diversity. We found that the processes that used multiple, structurally distinct scenarios explored a wider range of topics and issues shaping places. All four relied heavily on professional stakeholders as the scenario developers, limiting public input. Only one of the processes that included multiple futures captured the differential effects that scenarios would have on diverse people and interests. Overall, the purpose of the scenario planning drove the participant diversity and ultimately the quality and use of the scenarios

Home Energy Efficiency and Mortgage Risks: Research funded by the Institute for Market Transformation

Many have theorized that energy efficient homes should have lower default risks than standard homes because the former are associated with lower energy costs, which leaves more money to make the mortgage payment. However, few empirical studies have been conducted due to limited data availability. This study examines actual loan performance data obtained from CoreLogic, the lending industry's leading source of such data. To assess whether residential energy efficiency is associated with lower default and prepayment risks, a national sample of about 71,000 ENERGY STAR- and non-ENERGY STAR-rated single-family home mortgages was carefully constructed, accounting for loan, household, and neighborhood characteristics.The study finds that default risks are on average 32 percent lower in energy-efficient homes, controlling for other loan determinants. This finding is robust, significant, and consistent across several model specifications. A borrower in an ENERGY STAR residence is also one-quarter less likely to prepay the mortgage. Within ENERGY STAR-rated homes, default risk is lower for more energy-efficient homes. The lower risks associated with energy efficiency should be taken into consideration when underwriting mortgages

Demarcating Geographic Regions using Community Detection in Commuting Networks with Significant Self-Loops

We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.Comment: 38 page

Exploring Alternative Futures Using a Spatially Explicit Econometric Model

This paper illustrates the application of various forecasting methodologies in constructing multiple scenarios for the state of Maryland using Long term Inter Industry Forecasting Tool that tracks inter-industry outputs at a macro scale, and State Employment Model that disaggregates these outputs to the states. We then use accessibility, land availability and observed relationships of employment categories to distribute employment at a county level. In this paper, we identify the possible advantages and pitfalls of using large scale economic models to drive employment forecasts at the county level. This framework allows for simulating the implications of macroeconomic scenarios such as changes in exchange rates and unemployment levels, as well as local land use and transportation policies on local employment and demographics. In particular, we focus on two scenarios as test cases both of which involve very different ideas about how future might unfold and their effects on land use and transportation policy prescriptions. One of the scenarios involves, among others, rises in health care spending over the next few years and the other involves increases in energy prices. As will be shown, they have different spatial effects and suggest different policy actions on the part of various governments

Economic Scenarios and Development Patterns in the Baltimore‐Washington Region

This paper illustrates the use of scenarios in land use, environmental and transportation planning in and around the State of Maryland. Different assumptions about futures result in different patterns of growth with differential impacts on particular sectors of the economy. Such different patterns require formulation of contingent plans as well as robust plans. In this paper, we illustrate the quantitative modelling methodology of loosely linked economic demographic, transportation and other impact assessment models in constructing two scenarios; one of which represented the best possible guess about the continuation of the future and other involving rapid changes to energy prices and Federal spending. We illustrate the spatial development outcomes and the transportation and environmental plans that are necessary to deal with these different outcomes. Further, we illustrate that different planned actions have different efficacies in different futures and thus multiple futures should be carefully considered. Finally, we illustrate the notions of contingent plans and robust plans