Date of Award

Fall 9-2015

Embargo Period


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Civil and Environmental Engineering


H. Scott Matthews

Second Advisor

Chris Hendrickson


As concerns and understanding of climate change have continued to grow, cities and local governments have taken a leadership role in developing climate action plans to quantify and reduce greenhouse gas (GHG) emissions. A primary component of most climate action plans is the development of regular or semi-regular GHG inventories. These inventories are typically confined to the city-limits of a given area and report emissions from on-road transportation, electricity generation, residential and commercial buildings, and waste generation and disposal. Although there have been many advances in the data and methods used for forming these inventories, some challenges still remain. For example, the inventories can often be expensive and time consuming, data availability and scope/boundary choices often lead to inconsistencies between inventories across time periods or locations, and over-looked factors like climate and population change may have drastic impacts on emissions in the future. Given these challenges, this thesis seeks to develop a consistent method for evaluating metropolitan-level GHG emissions and some of the key factors that may drive emissions and cities’ ability to meet reduction targets moving forward. First, we use publically available national datasets (e.g. the EPA’s National Emissions Inventory, the EPA’s Mandatory GHG Reporting Program, etc.) to develop an integrated approach for estimating GHG emissions at the metropolitan level. Overall, this approach allowed us to form consistent production-based GHG estimates for the 100 most populated metropolitan areas in the United States for the years 2002 and 2011. During this time period, the overall GHG emissions for these metropolitan areas decreased by roughly 18%. The largest decreases in emissions were typically driven by decreases in industrial activity, and the largest increases in emissions were typically driven by increases in electricity production and population. We also compared the iv emissions estimates from the integrated approach to those reported by the cities in their climate action plans. Overall, the integrated approach generally provides comparable estimates to those reported by the cities. However, this comparison also highlighted some of the uncertainty that can emerge due to scope and boundary choices made while developing an emissions inventory. Given the uncertainty associated with scope and boundary choices described above, and an increasing push by practitioners to expand their analysis to the regional/mega-regional level (rather than the city-limits), we next sought to gain a better understanding of how scope and boundary decisions impact emission estimates and GHG reduction targets in metropolitan areas. We first identified two categories of under-reported emissions from GHG inventories: 1) “under-reported activities” (industrial processes and transportation between urban and suburban areas), and 2) “under-reported geographies” (emissions within a metropolitan statistical area but outside of the central city/urban core). Using the integrated data from the previous analysis, we found that, on average, under-reported activities account for an additional 24% of emissions and under-reported geographies represent 55% of total metropolitan GHG emissions. Up to this point, our analysis focused entirely on recent (2011) and past (2002) emissions. However, given the forward looking nature of GHG reduction targets, it is also important to look at how different factors might impact metropolitan GHG emissions and policies in the future. For this component of the analysis, we investigated the implications that projected climatic temperature change, population changes, and the EPA’s Clean Power Plan would have on electricity-sector emissions at the metropolitan level. Using regional temperature and electricity demand data, we were able to model strong quadratic relationships between average daily temperature and total daily electricity load. We then applied future temperature projections from climate models to these quadratic relationships to see how electricity demand may change in the v future as a result of climate change. Overall, we found that climate change will likely lead to small-to-modest increases in metropolitan electricity sector GHG emissions. Depending on location and climate model, the change in emissions was found to be between -4 and 22% by the year 2030.We also found that changes in population and policy (the EPA’s Clean Power Plan) are at least as impactful (if not more impactful) on changes to metropolitan electricity sector GHG emissions by the year 2030. Overall, the analysis and results from this thesis provide insights into the importance of current and future drivers of metropolitan GHG emissions and help inform decision-making related to GHG mitigation. The integrated approach developed in the first component of our analysis could serve as a less “resource intensive” way for communities to regularly form an initial assessment of their emission profile, compare themselves to their peers, and prioritize their resource and planning efforts. The second component of our analysis reveals that as GHG inventory methods and policies continue to expand in scope and scale, the addition of previously “under-reported” emission sources will require policy makers to re-think how they develop and implement their GHG reduction plans. For example, decision-makers may need to modify the annual reduction rates they target or adjust the time horizon under which they implement their plans. Our analysis also provides a framework for expanding emissions inventories beyond the scale of the city limits. The third component of our analysis shows that the consideration of factors such as climatic temperature change, population change, and policy change should help decision-makers form a more complete understanding of their emission profile in the future and help them decide how best to prioritize their mitigation strategies.