by Dan McCabe
Jones and Kammen (2014) performed a remarkably thorough analysis of the average household carbon footprint (HCF) for nearly every US zip code and examined how dozens of different variables affect greenhouse gas (GHG) emissions. The authors’ analysis used detailed data from the nationwide Residential Energy Consumption Survey, the National Household Travel Survey, and other sources. Their model used these surveys to estimate local emissions due to components such as electricity, housing, transportation, and food, then evaluated possible correlations with 37 independent demographic variables.
The results of the authors’ analysis exhibit different patterns depending on geographic region, but some national trends are evident. One of the most obvious patterns is the relationship between mean HCF and population density. Metropolitan regions tend to consist of a low-HCF urban core surrounded by a ring of much higher consumption in the outlying suburbs. Rural areas farther away from urban centers have low-to-average HCF values. This trend is most extreme in larger cities, which have the lowest HCF of all in areas of particularly high population density. Unfortunately, the particularly extensive suburbanization that occurs in these areas more than counteracts the benefits of relatively low carbon emissions in the city centers. The details of emissions patterns, however, are not as predictable. The composition of total HCF, for example, varies significantly by location. For example, transportation-related emissions range from 26% to 42% across different regions, due to differences in details such as average commuting time and vehicle fuel efficiency.
A linear regression analysis found that 92.5% of the variability in HCF across all zip codes can be attributed to six variables, the most important being number of vehicles per household, followed by annual household income, carbon intensity of electricity, and home size (measured in number of rooms). The regression analysis also revealed that, when other variables are controlled for, population density itself is actually not significant. Rather, it influences home size, vehicle ownership, and other factors, which in turn affect HCF. Thus, while population density is effective in representing trends of local home size, transportation, and income, these factors are what directly impact HCF, not density itself.
The results of this research provide a set of valuable guidelines for urban planning. First, they reveal a nationwide relationship between HCF and population density: carbon emissions increase with population density up to a threshold of about 3000 persons per square mile, after which HCF decreases with increasing population density. Similarly, because of this trend and the relatively high HCF levels in large suburbs, high population density should not necessarily be an urban planning objective because it tends to be accompanied by high consumption in outlying areas. Thus, GHG emissions reduction efforts must acknowledge the location-specific impacts of different planning strategies. Given the limited capacity of clean energy initiatives to reduce carbon emissions, consumption-driven strategies are essential, and Jones and Kammen’s work adds to the body of crucial planning knowledge.
Jones, C., Kammen, D. M., 2014. Spatial Distribution of U.S. Carbon Footprints Reveals Suburbanization Undermines Greenhouse Gas Benefits of Urban Population Density. Environmental Science & Technology 48, 895-902.