“Cool Climate Calculator”.
The CoolClimate Calculator currently includes benchmark consumption-based carbon footprints for average households in 28 cities and all 50 US states (with more geographic resolution expected in future versions). Benchmark results can be further adjusted by household size and income on the intro page of the calculator. This tool can be used to calculate a quick consumption-based carbon footprint of any region by selecting the nearest location (or simply the US state) and the average income of a region (household size can be left at the average setting of 2.5 persons per household). The results from household energy, transportation, food, goods and services are displayed in detail throughout the calculator and on the summary page.
The online tool also allows individual households to calculate their own carbon footprint using the same emission factors used in the model. Such a tool could theoretically be used to collect carbon footprint data from a scientifically-selected sample of households in a community for more accurate results. The household results could then be compared against benchmark results from the model to determine the GHG-efficiency of the region. See http://coolclimate.berkeley.edu/.
Traditional consumption-based emissions inventories currently require extensive analysis, including the construction of a fairly complicated model. Since households are responsible for roughly 70% of “final demand” (with governments and investment expenditures making up the difference), it might be possible to derive a crude estimate of consumption-based emissions by using a household carbon calculator and scaling results to the scale of an entire community. The University of California’s CoolClimate Carbon Footprint Calculator offers a potential starting point.
Once household-related emissions are estimated, emissions resulting from non-household consumption might be estimated (albeit it very crudely) through the use of ratios. For example if consumption-based emissions from households are estimated at 2 million metric tons of CO2e, and households are known to be responsible for 70% of total consumption, then non-household consumption-based emissions could be estimated as 2 MmT CO2e x (0.3/0.7). All household consumption-based emissions could be simply inflated by the ratio (1 + 0.3/0.7). One shortcoming of this approach is that the composition of non-household consumption tends to be different from household consumption (e.g. households purchase a lot of food, while capital investments are primarily of buildings and equipment). So a more refined approach would be to estimate the overall quantity of non-household consumption-based emissions using the ratio approach above, and then allocate these non-household emissions to different categories of consumption using the results of consumption-based inventories conducted for other communities. For example, if Oregon's consumption-based emissions inventory shows that 55% of non-household consumption-based emissions are associated with construction, then the non-household consumption-based emissions for your community would be assigned to construction using the same percentage. This might provide a reasonable estimate, assuming that government purchasing patterns and investment purchasing patterns typically don’t deviate widely from one community to the next. This is not always a valid assumption, particularly for communities with unusual patterns of government expenditures (e.g., host to a large military base) or investment capital.
For information on how UC Berkeley used a consumption-based framework to evaluate its own GHG footprint, click here.