Browsing by Subject "Greenhouse gas emissions"
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Item Anticipating the impacts of climate policies on the U.S. light-duty-vehicle fleet, greenhouse gas emissions, and household welfare(2011-05) Paul, Binny Mathew; Kockelman, Kara; Greene, David L.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.Item Climate action strategies for the University of Texas at Austin(2010-05) Hernandez, Marinoelle; Eaton, David J.; Walker, Jim H.This report analyzes the current greenhouse gas emissions inventory for The University of Texas at Austin (UT-Austin), reviews the carbon reduction strategies being implemented at UT-Austin and other peer institutions, and offers recommendations for strategies that could reduce greenhouse gas emissions at UT-Austin in the future.Item Evaluating the uncertainty of life cycle assessments : estimating the greenhouse gas emissions for Fischer-Tropsch fuels(2011-05) Denton, Rachel Marie; Allen, David T.; McDonald-Buller, ElenaEnvironmental regulations have historically been focused on individual emission points, facilities, or industrial sectors. However, recent and emerging regulations for greenhouse gas (GHG) emissions such as those contained in the Energy Independence and Security Act (EISA) of 2007 have introduced the concept of product life cycle limits on the emissions of transportation fuels. Thus, a complete life cycle assessment (LCA) of the transportation fuel must be completed where all emissions from field to the vehicle’s fuel tank and from tank to the vehicle’s exhaust must be assessed. However, although there have been extensive analysis of the GHG emissions associated with transportation fuels, there are substantial uncertainties associated with these estimates that can be attributed to poor data quality, inconsistent methodological choices, and model uncertainties, among others. This thesis evaluates the uncertainties present in LCA through the case study of fuel production using Fischer-Tropsch (F-T) synthesis of fuels derived from coal and biomass. Specifically, GHG emission estimates for F-T synthesis process scenarios are presented and the uncertainties in the estimates are discussed. Overall uncertainties in GHG emissions due to changes in the details of the process configurations in the F-T process can be up to 11%. This finding suggests that the details of fuel refining conditions will need to be specified in determining whether fuels meet GHG emission requirements, complicating the implementation of life cycle GHG regulations.Item Mitigation of municipal biosolids via conversion to biocrude oil using hydrothermal liquefaction : a techno-economic analysis(2015-05) Bond, Cody Ray; Berberoglu, Halil; Greene, DavidIn this techno-economic analysis, we have shown that hydrothermal liquefaction (HTL) technology can be integrated with existing biosolids management facilities that utilize anaerobic digestion and biogas capture. The overall process converts raw sewage sludge to refinery-ready biocrude oil. The Hornsby Bend Biosolids Management Plant (HBBMP) in Austin, TX is used as a case study. First, the operation of the plant without any modification was modeled and validated with field data. A standalone HTL processing unit was then considered as an add-on to the existing infrastructure. Technical and economic parameters were obtained from literature and experimental data. The results showed that savings of about $32 M over current operation with a payback period of 4.35 years were achievable at HBBMP. A nation-wide implementation could result in production of almost 4.5 million barrels of upgraded biocrude oil per year while offsetting about 330,000 metric tons of CO2 equivalent greenhouse gas emissions annually.Item On integrating models of household vehicle ownership, composition, and evolution with activity based travel models(2012-12) Paleti Ravi Venkata Durga, Rajesh; Bhat, Chandra R. (Chandrasekhar R.), 1964-; Abrevaya, Jason; Pendyala, Ram; Machemehl, Randy; Boyles, StephenActivity-based travel demand model systems are increasingly being deployed to microsimulate daily activity-travel patterns of individuals. However, a critical dimension that is often missed in these models is that of vehicle type choice. The current dissertation addresses this issue head-on and contributes to the field of transportation planning in three major ways. First, this research develops a comprehensive vehicle micro-simulation framework that incorporates state-of-the-art household vehicle type choice, usage, and evolution models. The novelty of the framework developed is that it accommodates all the dimensions characterizing vehicle fleet/usage decisions, as well as accommodates all dimensions of vehicle transactions (i.e., fleet evolution) over time. The models estimated are multiple discrete-continuous models (vehicle type being the discrete component and vehicle mileage being the continuous component) and spatial discrete choice models that explicitly accommodate for multiple vehicle ownership and spatial interactions among households. More importantly, the vehicle fleet simulator developed in this study can be easily integrated within an activity-based microsimulation framework. Second, the vehicle fleet evolution and composition models developed in this dissertation are used to predict the vehicle fleet characteristics, annual mileage, and the associated fuel consumption and green-house gas (GHG) emissions for future years as a function of the built environment, demographics, fuel and related technology, and policy scenarios. This exercise contributes in substantial ways to the identification of promising strategies to increase the penetration of alternative-fuel vehicles and fuel-efficient vehicles, reduce energy consumption, and reduce greenhouse gas emissions. Lastly, this research captures several complex interactions between vehicle ownership, location, and activity-travel decisions of individuals by estimating 1) a joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length, and 2) an integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. The methodology used for estimating these models allows the specification and estimation of multi-dimensional choice model systems covering a wide spectrum of dependent variable types (including multinomial, ordinal, count, and continuous) and may be viewed as a major advance with the potential to lead to redefine the way activity-based travel model systems are structured and implemented.