Development over the past sixty years has created patterned growth and expansion outward from city centers, separating residences from commercial areas and employment centers. This separation of land uses has increased automobile dependency, which increases vehicle miles traveled and associated greenhouse gas emissions. California Senate Bill 375 mandates the development and implementation of a “Sustainable Communities Strategy” in order to plan regional land use and transportation in a coordinated fashion. In coordination with this effort, the Association of Monterey Bay Area Governments (AMBAG) is developing the Regional Implementation Plan for Smart Growth Development Strategies, which entails the identification of smart growth strategies that offer the greatest potential to reduce vehicle miles traveled and meet the 5% greenhouse gas emissions reduction target for the Monterey Bay Area. The major goal of this project was to assist AMBAG in determining the political feasibility of smart growth development strategies and identifying the most feasible strategies for the region. Political feasibility was determined by two factors: 1) support from the public/stakeholders, 2) “low-hanging fruit” potential, and one technical criterion: the potential to reduce vehicle miles traveled and the associated greenhouse gas emissions. The Regional Advisory Committee provided ten months of knowledge and expertise on stakeholder opinions v about strategies, barriers, circumstances for gaining stakeholder support, and resources for implementation. Additionally, survey results from planning directors the “low-hanging fruit” strategies. The quantified VMT/GHG reduction potential of smart growth strategies was another evaluation criteria and was used to inventory quantified reduction measures and their ranges of potential. The analysis identified seventeen strategies that met a set of thresholds for political feasibility. Based on these results, it is recommended that AMBAG consider these strategies in the development of their plan, by addressing the barriers to implementation, the conditions or circumstances for overcoming those barriers and gaining support from stakeholders, and developing the resources to assist jurisdictions with implementation.

abstract: Vehicle type choice is a significant determinant of fuel consumption and energy sustainability; larger, heavier vehicles consume more fuel, and expel twice as many pollutants, than their smaller, lighter counterparts. Over the course of the past few decades, vehicle type choice has seen a vast shift, due to many households making more trips in larger vehicles with lower fuel economy. During the 1990s, SUVs were the fastest growing segment of the automotive industry, comprising 7% of the total light vehicle market in 1990, and 25% in 2005. More recently, due to rising oil prices, greater awareness to environmental sensitivity, the desire to reduce dependence on foreign oil, and the availability of new vehicle technologies, many households are considering the use of newer vehicles with better fuel economy, such as hybrids and electric vehicles, over the use of the SUV or low fuel economy vehicles they may already own. The goal of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the 2009 National Household Travel Survey data it is possible to develop a model to estimate household travel demand and total fuel consumption. If given a vehicle choice shift scenario, using the model it would be possible to calculate the potential fuel consumption savings that would result from such a shift. In this way, it is possible to estimate fuel consumption reductions that would take place under a wide variety of scenarios. / Dissertation/Thesis / M.S. Civil Engineering 2013

Vehicle miles traveled (VMT) and its relationship with the built environment has been extensively studied. Most notably, five D variables of the built environment including density, diversity, design, destination accessibility, and distance to transit are the key variables included in this research to explain VMT generation from housing developments. This thesis uses prior research that developed robust statistical models and findings to create a framework to estimate VMT reduction affected by infill housing developed using incentives provided by the state compared to a regional comparator. The regional comparator is typically a suburban single-family housing development in the region. The models recommended for future application of the framework are based on ease of finding the data on variables included in the model and statistical robustness. The application of the framework in the Central Coast and San Francisco Bay Area regions of California shows that infill prototypes developed can generate an 11-27% reduction in VMT per capita. The findings are specific to a synthetic household defined for this study. The research provides ways to apply this framework to other regions of the state along with ideas to consider for future work. These ideas include exploring the VMT reduction potential based on households with different income levels appropriate for the regions, future modeling efforts, and selection of existing models. The findings of this thesis support that the combination of the five D variables can help attribute to a larger VMT reduction than the VMT reduction caused by the change of a single variable. When destinations are clustered, and jobs are available at a reasonable distance to the residence, a significant reduction in VMT is more achievable. The results inform public agencies on which locations are ripe for devoting further resources for incentivizing housing development to reach climate and housing goals.

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The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

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