Influence of State Planning Environments on Urban Sprawl
Himanshu Grover (Alabama A&M University) and Jerry Anthony (University of Iowa)
Urban sprawl has been a dominant feature of U.S. urban expansion over the past five decades. Sprawl has many well-documented negative consequences, such as the degradation of prime agricultural land, higher per-unit costs of land development and urbanized land and housing, higher municipal costs to maintain services and amenities, longer and more frequent travel distances in single-occupancy vehicles, and even poor health. Unsurprisingly then, many U.S. cities, regions, and states have tried to combat urban sprawl since the 1960s using four main approaches: state growth management laws, urban service area boundaries, local government regulations, and smart growth strategies. Many studies have examined the efficacy of these approaches, and the findings are mixed. Over the past two decades, it has also become politically difficult to enact new policies to reduce urban sprawl.
In our study, we examined whether existing state-level planning environments have a sprawl-mitigating effect, hoping that if they were effective, then existing planning tools, rather than specialized anti-sprawl policies, could be employed to help check sprawl. Our findings indicate that strong state planning mandates seem to be effective in checking sprawl. Stronger state planning environments can amplify the impacts of common planning tools and could support targeted instruments such as urban growth boundaries; this portfolio approach is less divisive and more feasible in many contexts while still being able to accommodate stronger controls where warranted and politically acceptable.
Our methods
We examined the effectiveness of state planning environments (SPE) in checking urban sprawl, using data from all 50 states from 2001 to 2021. We used data from two time points separated by 20 years, since the effectiveness of land development policies cannot be assessed over a short time interval. We examined state-level policies because they have four distinct advantages over local policies: they could a) be required of all communities within a state; b) reduce the possibility of negative spillovers from cities/counties that have strong-local level planning to those that do not; c) be supported with state funding and administrative support for implementation; and d) be given serious attention by cities and counties since local governments receive about 40-45% of their annual budgets from state governments.
While urban sprawl can be measured in several ways, the simplest and most intuitive measure is urban density, and this measure is quite commonly used in urban sprawl studies. We used the change in density between 2000 and 2021 as our measure. We constructed our primary experimental variable, SPE, using data from the American Planning Association’s (APA) 2022 comprehensive survey of planning laws in all 50 States. The survey collected information on eight core planning themes emphasized in state policy and assigned a binary 0/1 score to each state for each theme. States were categorized into groups of varying degrees of SPE stringency based on the sum of scores across these eight themes: 5 or more as very stringent, 1-4 as moderately stringent, and 0 as low. By these criteria, there were 11 very stringent states, 31 moderately stringent states, and 8 low-stringency states. We used five variables to control for alternate hypotheses: a) urban density in 2000; b) change in urban population; c) change in urban land; d) change in rural land; and e) change in service sector employment as a measure of the growth/decline in the state economy from 2001-21 (service sector employment employs the largest number of people of any sector, is very sensitive to economic conditions, and excludes defense employees). We conducted a path analysis for the effects of all the above factors on urban sprawl using the PROCESS algorithm in the Smart Partial Least Squares (SmartPLS) program.
Findings and discussion
While we found the overall path model explaining incremental density to be statistically significant, with a high adjusted R-square (0.89), it is standard practice in SmartPLS to focus on the statistical significance of relationships rather than the predictive power of the overall model. We found that 10 of the 12 relationships in our model were statistically significant. Notably, we found that our experimental variable, SPE, had a statistically significant positive effect on incremental density, indicating that a robust state planning mandate increased incremental density. SPE also had a statistically significant negative relationship with change in developed area, indicating that states with stronger state-wide planning environments experienced a lesser increase in developed area than other states. Considered together, these findings suggest that SPE had a clear, urban-sprawl-controlling effect. Therefore, strong state planning environments that support a slew of local development control policies, such as comprehensive planning, zoning, impact fees, and growth controls, could be effective in reducing urban sprawl. Local land-use regulations are near-ubiquitous across the U.S. Our findings suggest that, when supported by a robust state planning environment, these regulations can be quite effective at curbing sprawl.
Himanshu Grover is Chairperson and Associate Professor of Community and Regional Planning at Alabama A&M University. His research explores how planning systems and public policy shape urban development, with a focus on achieving more efficient, equitable, and sustainable patterns of growth.
Jerry Anthony, PhD, FAICP, is an associate professor at the School of Planning & Public Affairs, University of Iowa. He researches US Land and Housing Policy, and International Planning Issues, with support from several US federal and state entities. He is a HUD Urban Scholar and a Fulbright Senior Scholar.