Name
Abstract | ||
|---|---|---|
The proliferation of online emission calculators and the growing popularity of carbon footprint assessments recently underscores an emerging interest among Americans in understanding their personal environmental impacts, especially in relation to greenhouse gas emissions. While studies have quantified carbon footprints at a variety of geographic scales using economic data, or a combination of economic and census data, few have produced results that were immediately useful for local-scale emission reduction efforts. The authors explore the feasibility of utilizing block group level census data to estimate the residential carbon footprint of an American city. A census-based emission model was adapted from the United States Environmental Protection Agency's Individual Emission Calculator. Block group census data were used as surrogates for household energy consumption and transportation related carbon emissions. Although lacking some of the finer nuances of individual behavior assessments, this approach enables analysis of a continuous urban landscape with a relatively high degree of data resolution using Geographic Information Systems GIS and standard desktop-software. The model output, paired with choropleth and dasymetric visualizations, illustrate that census data can be successfully adapted to estimate the residential carbon footprint for Austin, Texas, and by extension, any other American city with equivalent census data coverage. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4018/jagr.2012100106 | IJAGR |
Keywords | DocType | Volume |
Estimating Residential Carbon Footprints,American City,residential carbon footprint,data resolution,carbon footprint,carbon emission,census data,carbon footprint assessment,economic data,equivalent census data coverage,block group census data,American city | Journal | 3 |
Issue | ISSN | Citations |
4 | 1947-9654 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew H. Connolly | 1 | 1 | 0.68 |
| Ronald R. Hagelman | 2 | 0 | 0.34 |
| Sven Fuhrmann | 3 | 192 | 22.38 |