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Do Smart Growth Strategies Have a Role in Curbing Vehicle Miles Traveled? A Further Assessment Using Household Level Survey Data The B.E. Journal of Economic Analysis & Policy Topics Volume 12, Issue...

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Do Smart Growth Strategies Have a Role in Cu
ing Vehicle Miles Traveled? A Further Assessment Using Household Level Survey Data
The B.E. Journal of Economic
Analysis & Policy
Volume 12, Issue XXXXXXXXXXArticle 37
Do Smart Growth Strategies Have a Role in
ing Vehicle Miles Traveled? A Furthe
Assessment Using Household Level Survey
Sudip Chattopadhyay∗ Emily Taylor†
∗San Francisco State University, XXXXXXXXXX
†San Francisco State University, XXXXXXXXXX
Recommended Citation
Sudip Chattopadhyay and Emily Taylor (2012) “Do Smart Growth Strategies Have a Role in Cu
ing Vehicle Miles Traveled? A Further Assessment Using Household Level Survey Data,” The B.E.
Journal of Economic Analysis & Policy: Vol. 12: Iss. 1 (Topics), Article 37.
Copyright c©2012 De Gruyter. All rights reserved.
Do Smart Growth Strategies Have a Role in
ing Vehicle Miles Traveled? A Furthe
Assessment Using Household Level Survey
Sudip Chattopadhyay and Emily Taylo
This paper draws on McFadden’s location choice theory and incorporates households’ resi-
dential choice decisions as a hierarchical process in a structural travel demand model. The pa-
per argues that such an approach can effectively tackle the problems of self-selection and multi-
collinearity. Contrary to previous findings, empirical results based on OLS and 3SLS reveal that
travel demand is highly elastic to certain smart-growth features, if they are measured at different
spatial scales. The results are robust against alternative sequencing of the hierarchical choice pro-
cess. An analysis of the quantitative impact of a change in the smart-growth and fuel-tax policies
eveals significant returns under both policies. Finally, a simulation based on California suggests
that smart growth policies substantially reduce household travel demand.
KEYWORDS: transportation demand, land use policies, self-selection, multicollinearity, hierar-
chical choice theory, structural equations model, three stage least squares
∗An earlier version of the paper was presented in the Summer 2011 Association of Environmental
and Resource Economists (AERE) conference held in Seattle, Washington, during June 9 – 10,
2011. The authors would like to thank the editor and the two referees for their valuable comments
that improved the paper. The authors also thank the participants of the conference for helpful
feedback. Data for this research are all from published sources and the findings of this research
have no potential conflict with any agency.
1. Introduction

The role of “smart growth” policies on auto-dependent transportation demand has
een extensively investigated in the economics and transportation literatures.1
Smart growth policies specifically focus on redevelopment of u
an areas to
educe auto-dependence. Key features of redevelopment include policies to
develop neighborhoods with high residential/employment densities and help build
communities with adequate variety of housing and employment mix that can cater
to a wide range of income levels. Another important feature of redevelopment is
the availability of or access to public transit. Policies to achieve high density
levels and a transit oriented u
an design are mutually reinforcing; on the one
hand transit stations located in high density neighborhoods are accessible to more
people, efficient transit network in cities with higher density neighborhoods, on
the other hand, can reduce trip lengths and time, thereby providing a low cost
alternative to private transportation. The proponents of the concept of “new
anism” that emphasizes smart growth believe that such an approach to
edevelopment stimulates integration of neighborhoods and thereby reduces
automobile travel (Ewing and Cervero, 2010).
The key finding of the studies investigating the role of smart growth on
auto-dependence is that the land use policies that characterize smart growth have
only a modest role in reducing the demand for vehicle miles traveled (VMT). For
the choice of an appropriate policy instrument to cu
energy use, this finding is disconcerting because, if a non-price instrument, such
as an innovative land use policy, does not work, the only recourse is to use a
price-based instrument, such as fuel or congestion tax, which is often not
politically expedient.
It is widely recognized in the existing literature on VMT demand that
households choose their residential location and consequently, the u
features of the location, such as residential/job densities, access to public transit
facilities that characterize “smart growth” or the lack of it. This process of self-
selection causes endogeneity in the VMT demand model. The literature also
ecognizes that different u
an-form features may have different spatial extent of
influence on travel behavior (Boarnet and Sermiento, 1998; Guo and Bhat, 2007).
For example, a job center can be identified with a larger geographic region, such
as a county, compared to a neighborhood with a certain level of residential
density. Unfortunately, linking the two aforementioned aspects of the rational
housing choice behavior has not been explored in the VMT demand literature.
1 Interesting studies include Bento et al. (2003, 2005), Boarnet and Sarmiento (1998), Brownstone
and Golob (2009), Crane (2000), Guo and Bhat (2007), Bhat and Guo (2007), Vance and Hadel
(2007), Su XXXXXXXXXXEwing and Cervero XXXXXXXXXXpresent a meta-analysis of the impact of smart
growth on VMT demand based on a comprehensive set of studies.
Chattopadhyay and Taylor: Smart Growth and Vehicle Miles Traveled
Published by De Gruyter, 2012
Building on past studies, this paper presents a simple, but an innovative modeling
approach that draws in part on the theory of residential choice – originally
developed by McFadden (1978), which models households’ choice of residential
location as a hierarchical choice process.
Following McFadden (1978), this study incorporates households’
esidential choice process in the VMT demand model as a sequence of
hierarchical decisions in which households choose, in sequence, certain
commonly used u
an-form features of land use at varying spatial scales. 2 Using
the 2001 National Household Travel Survey data (NHTS-2001), the study
empirically shows that the OLS estimation of such a demand model can
effectively tackle the multicollinearity problem frequently faced in VMT demand
estimation and can yield statistically as well as quantitatively significant impacts
of u
an-form features on VMT demand. Also, the three-stage least squares
(3SLS) technique developed to incorporate the aforementioned choice process to
estimate the VMT demand shows further improvement over the OLS in
addressing the endogeneity problem and in yielding statistically and quantitatively
significant impacts. Moreover, the findings based on the 3SLS model are shown
to be robust to alternative sequencing of the hierarchical choice process. Finally,
a simulation exercise for California, based on the estimated 3SLS model suggests
that innovative land-use mixes can result in substantial benefits for the state.
The paper addresses the aforementioned policy question with a special
focus on California, since understanding the role of smart growth strategies in
California is important and highly policy relevant. In California, the
transportation sector cu
ently accounts for nearly 38% of the greenhouse gas
(GHG) emission. This figure is expected to rise, since VMT in California is
increasing at a rate that far outpaces its population growth. SB 375 – a regional
transportation and land use planning statute and The California Global Warming
Solutions Act, 2006 (AB 32) are two recent initiatives that are expected to set the
direction of GHG mitigation strategies for the future. While SB 375 has a direct
focus on identifying and implementing smart growth strategies to reduce
transportation-related GHG emissions, AB 32 aims to achieve this goal indirectly
through its innovative ca
on offset policies linked to the cap-and-trade program.
SB 375 requires each region in California to create a prefe
ed growth scenario
that will minimize GHG emissions, and then ties state transportation funds to
projects that conform to that prefe
ed growth scenario. Global Warming Act
(AB 32) requires that by 2020 the state's greenhouse gas emissions be reduced to
2 Based on the theory propounded by McFadden (1978), Quigley XXXXXXXXXXand later Chattopadhyay
(2000) model household residential choice in a hierarchical, probabilistic choice framework and
apply the nested logit model for valuing housing amenities.
The B.E. Journal of Economic Analysis & Policy, Vol. 12 [2012], Iss. 1 (Topics), Art. 37
1990 levels, a roughly 25% reduction compared to the projected emission
estimate under the business-as-usual scenario.3
The next section discusses the econometric challenges su
ounding VMT
demand modeling and explains the methodological steps adopted in this paper to
address those challenges. Section 3
iefly discusses the household residential
choice process and the econometric methodology to incorporate such a process in
the VMT demand model. Section 4 presents the data sources and definitions.
The results of the estimation are presented and analyzed in Section 5. A
simulation is presented in Section 6 to assess the potential for California’s “smart
growth” initiatives. Section 7 provides a sensitivity analysis of the VMT demand
model against alternative hierarchical residential choice processes. Finally,
Section 8 provides conclusion.
2. Econometric Issues of VMT Demand Modeling

Modeling VMT demand is fraught with a number of challenging econometric
issues su
ounding the use of the common u
an-form features that affect VMT
demand. Chief among them are the problems of endogeneity and collinearity.
While the former is caused by the fact that households self-select in to
communities with prefe
ed u
an-form features, the latter is due to the fact that
commonly used measures of u
an form tend to move together. Recent research
has provided guidance in designing innovative u
an-form measures to tackle
collinearity and disentangle their independent impact on VMT demand (Bento et
al., 2003, XXXXXXXXXXInstrumental variable approaches (Boarnet and Sarmiento, 1998;
Vance and Hadel, 2007), including three-stage least squares (3SLS) (Brownstone
and Golob, 2009) and joint choice models of location choice and vehicle
ownership (Bhat and Guo, 2007; Guo and Bhat, 2007) have proven to be
successful in effectively addressing the problem of endogeneity and in
consistently estimating the effect of the u
an-form features on VMT.
A major concern in all these studies is that the effect sizes of the key
an-form features are, although statistically significant in many instances, they
are quantitatively too small to suggest policy action. Ewing and Cervero (2010)
in their meta-analysis report that the weighted average of the magnitude of
elasticities of VMT with respect to population density, job density, and distance to
transit are 0.04, 0.00, and 0.05, respectively. These extremely low magnitudes of
Answered 1 days After Mar 19, 2022


Komalavalli answered on Mar 20 2022
77 Votes
Smart development strategies place a particular emphasis on redeveloping metropolitan areas in order to lessen dependency. Policies that establish high density residential/employment zones and assist in the creation of communities with a diverse variety of housing and employment combinations that can support a diverse range of incomes are key characteristics of redevelopment. Another crucial aspect of the reconstruction is the availability of or accessibility to public transportation.
Land use development is a complicated process driven by a plethora of decisions made by governments, corporations, and individual consumers about location (where activity occurs), density (number of people or occupations within an area), and accessibility. These decisions frequently include trade-offs, such as those between density and accessibility, as well as between different forms of transportation infrastructure. In many circumstances, a choice favors one sort of development model while excluding another pattern that excludes another.
The association between higher-density communities and lower automotive traffic may be attributable, in part, to the fact that certain individuals who loathe driving and prefer transit, walking, or bicycling may have self-selected into neighborhoods. Policies to achieve high density levels and transit-oriented u
an design are mutually reinforcing; on the one hand, transit stations located in high density neighborhoods are more accessible to more people; on the other hand, an efficient transit network in cities with higher density neighborhoods can reduce trip lengths and time, providing a low-cost alternative to private transportation. Proponents of the "new u
anism" idea, which stresses smart expansion, think that such an approach to redevelopment encourages community cohesiveness and, as a result, minimizes car traffic.
Fuel taxes pros and cons
Fuel taxes, or indirect taxes levied on fuel usage, account for around 60­70% of environmental tax collections. They are largely taxed on transportation fuels. With notable exceptions (such as kerosene and coal), it is a progressive type of taxation in many poor nations. It is an economically effective and non-distortive kind of taxes that co
ects market flaws. It is simple to implement and has a minimal risk of tax evasion/avoidance. It has a substantial tax base. Only a very high tax rate can significantly reduce gasoline usage, making other choices (e.g., fuel/vehicle regulations) more effective. Fuel taxes may have a significant economic impact in specific circumstances or at certain times, such as when energy intensive businesses play a significant role or when international prices are rising.
Land use policies pros and cons:
Land use planning is an important growth framework: flourishing u
an places undoubtedly have a goal that must be realized through a framework in order to achieve well-ordered development. As a result, land use planning serves as a framework.
Good land use planning has a favorable...

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