Date of Award

12-2014

Embargo Period

1-15-2016

Degree Type

Dissertation

Department

Engineering and Public Policy

Advisor(s)

Chris T. Hendrickson

Second Advisor

Jeremy J. Michalek

Abstract

Recent sustainability research has focused on urban systems given their high share of environmental impacts and potential for centralized impact mitigation. Most previous works rely on descriptive statistics obtained from place-based case studies representing major cities, metropolitan areas, and counties using emissions inventories that may have inconsistent and/or limited scope (e.g., transportation and residential emissions only). This limits the potential for general insights and decision support related to the role of urbanization in CO2 emissions reduction. Here, I implement generalized linear and multiple linear regression analyses to obtain robust insights on the relationship between urbanization and CO2 emissions in the U.S. I used consistently derived county-level scope 1 & 2 CO2 inventories for my response variable while predictor variables included dummy-coded variables for county geographic type (central, outlying, and non-metropolitan), median household income, population density, and climate indices (heating degree days (HDD) and cooling degree days (CDD)). There is statistically significant difference in per capita emissions by sector for different county types, with transportation and residential emissions highest in nonmetropolitan (rural) counties, transportation emissions lowest in central (most urbanized) counties, and commercial sector emissions highest in central counties. More importantly, contrary to most previous findings, there is not enough statistical evidence indicating that per capita scope 1 & 2 emissions differ by geographic type, ceteris paribus. These results are robust for different assumed electricity emissions factors. Given that emissions production rate in more urban counties are not significantly different from that of less urban ones and population is concentrated in urban counties, significant national emissions reduction could be achieved if efforts are focused on central counties. There are various climate mitigation techniques – both from the supply and demand side. Given the large contribution of transportation in total county emissions and the fact that this technology bridges the transportation and electricity sector which is currently the biggest contributor to CO2 emissions, I investigated the emission reduction benefits from driving electric instead of gasoline vehicles. Vehicle electrification has also received sustained support from the local to the supranational level and is seeing an optimistic market trend. I characterize and assess the uncertainty in CO2 emissions per mile travelled for vehicles in the U.S. given regional variation and uncertainty in electricity emissions factor (marginal vs average, generation- vs consumption-based, different regional boundaries), driving pattern, and daily vehicle miles traveled (DVMT). I also investigate vehicle emissions estimates under convenience (vehicle starts charging when it arrives at home) and delayed (vehicle starts charging at 12am) charging. Using marginal emissions factors results in electric vehicle emissions estimate that are higher than average emissions estimates in the northeastern and north central U.S., and lower emissions in the south central U.S. In other regions, using marginal emissions versus average emissions factors may lead to differences in emissions estimates by as much as 28%. Delayed charging leads to higher emissions, given that off-peak electricity demand is supplied by fossil generators in most regions (e.g., coal). Using marginal emissions estimates, the Nissan Leaf electric vehicle has lower operation emissions compared to the Toyota Prius (the most efficient US gasoline vehicle) in western U.S., and the Leaf has higher operation emissions in the north central, regardless of assumed charging scheme and estimation method. In other regions the comparison is uncertain because of regional variation and uncertainty in emission factor estimates. Consumption- and generation-based marginal emissions also significantly (5 % - 28%), enough to result unclear comparison results. Average vehicle emissions estimates under different regional boundary definitions also differ significantly (e.g., state-based estimates deviate from National Electricity Reliability Commission (NERC) region-based estimates by as much as 122%). Other factors such as driving pattern and daily vehicle miles traveled also influence vehicle emissions. I conduct a locational comparison of electric and gasoline vehicle life cycle emissions in the U.S. taking into consideration the regional variation in the joint effect of consumption-based marginal electricity emission factors, driving pattern (city, highway or combined), and daily vehicle miles traveled (DVMT) distribution. I find that electricity generation emissions rate, determined by grid mix and charging scheme, has the largest influence on electric vehicle emission levels and the emissions differences of gasoline and electric vehicles. Secondary to this is urbanization level, especially for PHEVs, as it influences driving pattern and daily vehicle miles traveled. Highest CO2 emission reductions from electric vehicles can be attained in metropolitan counties in CA, TX, FL, NY, and New England states. Policies for wider adoption of electric vehicles such as incentives and other adoption facilitating mechanisms including investments in public charging infrastructure are encouraged in metropolitan counties, especially the denser ones. On the other hand, these policies are discouraged in north central states where electric vehicles would only increase emissions because of a relatively carbon-intensive grid. These findings reflect the pivotal role of the electricity and transportation sectors nexus in achieving national goals of CO2 emission reductions. Unless the U.S. decarbonizes its electricity system further, electric vehicles will only be beneficial in climate mitigation efforts in certain locations in the country.

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