Integrating environmental sustainability efforts across community boundaries: A case study with Charlottesville City, Albemarle County, and the University of Virginia

Many entities use environmental footprint tools to help evaluate and improve environmental sustainability. The objective of my research was to build an integrated greenhouse gas (GHG), nitrogen (N), and phosphorus (P) footprint tool to estimate and spatially visualize the footprints of Charlottesville City (Charlottesville), Albemarle County (Albemarle), and the University of Virginia (UVA). The tool was then used to evaluate the impact of both planned and potential climate action strategies. The evaluation explored which strategies were co-beneficial with other footprint reductions and which were not as effective. The tool model is built from several existing tools, including the Community Nitrogen Footprint Tool (C-NFT) and the Integrated Environmental Footprint Tool (IEFT). The combined model produces a new framework for evaluating GHG, N, and P footprints side by side. Chapter 1 introduces previous work on integrated footprints and the unique characteristics of the study area (Charlottesville, Albemarle, and the University of Virginia).

Chapter 2 discusses the additions made to the original community nitrogen footprint tool (CNFT) presented in Dukes et al. (2020) and Stanganelli (2020). These additions captured sectors not needed for city level footprints such as crop and animal agriculture, septic systems, and airports. This section also built the framework for integrating a city, county, and large university each with separate data into the tool. The importance of having these sectors included for the N footprint was evident. In Albemarle, the N footprint from these additional three sectors and the addition of UVA increased from 40 kg N per capita annually to 77 kg N per capita annually. The Charlottesville N footprint also increased slightly due to the addition of a large university from 32 kg N per capita annually to 34 kg N per capita annually. Combining and collecting the inventory data set the framework to calculate the other two footprints and begin scenario analysis.

Chapter 3 discusses the integration of the GHG and P footprints into the C-NFT framework and how scenarios can be run to determine the impact of reduction scenarios on these three footprints. Integrating the GHG and P footprints used previously developed methodologies from Leach et al. (2017) (GHG) and Metson et al. (2020) (P) to first add the three footprints to the analysis on a community basis. Then, scenarios were run using this combined tool to determine the impact on other footprints. There was a clear and direct relationship between the P and N footprints, likely due to the drivers of both these footprints being food purchased. The GHG and N and GHG and P footprints were not significantly correlated. However, most strategies produced co-benefit reductions. The only strategies that did not produce a co-reduction were with P and GHGs as energy strategies have no impact on the P footprint. Overall, a significant reduction the three footprints could be produced if strategies within climate action plans as well as some additional proposed strategies were employed.

The tool can be used as a model for other localities who have interconnect footprint reduction goals. The model used here for cities and counties can be used across the US as publicly available data are used for the majority of values calculated here. Many higher education institutions also track their GHG footprint (SIMAP, 2021). This means there is an opportunity here for integration of institutions within community footprint tracking. Using the integrated tool for community footprints allows for a broader footprint analysis of a community’s environmental sustainability and can help stakeholders make decisions to benefit multiple footprints.