Denver

1. Who did it: Researchers at the University of Colorado Denver, and the City and County of Denver

2. What they did: A research team led by Anu Ramaswami of the University of Colorado Denver prepared a paper for Environmental Science & Technology titled “A Demand-Centered, Hybrid Life-Cycle Methodology for City-Scale Greenhouse Gas Inventories". In short, the research team developed a hybrid inventory methodology that separately reports the GHG impact associated with direct end-use of energy (consistent with traditional inventories), as well as the impact of extra-boundary activities including air travel and production of “key urban materials”. The materials evaluated in this study include vehicle fuels, water, cement, and food.

Specific to cement and food, the two “materials” most familiar to the materials/waste community, two different methods were used to evaluate the associated GHG emissions. For cement, the quantity of cement used (in metric tonnes) was derived from the U.S. Economic Census. This was combined with data on the life-cycle impacts of producing one metric ton of cement in order to estimate emissions. For food, the US Consumer Expenditure Survey was used to estimate household consumption (in dollars) of food. This was scaled up to the community’s number of households, then multiplied against an emissions factor (CO2e/$) derived from Carnegie-Mellon’s EIOLCA software.

In addition, the hybrid inventory incorporated a credit for recycling and methane capture from landfilling, computed from ICLEI methods.

3. Why they did it: The authors point out that in the United States, national-level policies to reduce GHG emissions are increasingly supplemented by city-scale actions. “Because cities contain a large proportion of the global human population, and exert huge direct and indirect demands on our natural capital, city-scale climate actions have the opportunity to engage vast segments of human populations. . . therefore, understanding GHG emissions at the spatial scale of the city becomes very important in the context of global efforts to mitigate climate change. Including key urban materials in a community inventory has policy relevance as cities can influence these emissions through practices such as procurement standards for “low carbon” cement and efforts to encourage diet shifting.

In a subsequent paper, Tim Hillman and Anu Ramaswami provide the following discussion of policy implications:

“While refinements will be ongoing, the expanded Scope 1+2+3 GHG emissions footprint in this paper shows promise in its results for US cities (Figure 3), and has immediate policy relevance. First, consistent inclusion of activities such as food and airline travel across scale – from the home to the city to the nation - can help in public communication about GHG emissions. Second, including Scope 3 activities in a city’s GHG footprint can facilitate innovative cross-boundary and cross-sector strategies for GHG mitigation. For example, changes in diet can significantly reduce GHG emission from food consumption [29]; such material shifts would be invisible in traditional Scope 1+2 GHG accounting in cities. Likewise, green materials policies would be invisible in a boundary-limited Scope 1+2 accounting. Lastly, innovative information communication technologies such as teleconferencing would only record increases in electricity use in buildings within city boundaries, without being able to account for associated decreases in cross-boundary airline travel. A strict boundary-limited Scope 1+2 method may also unintentionally credit GHG emissions shifts outside city boundaries, e.g., zero-emission hydrogen fuel use within city boundaries while GHG from hydrogen production from coal or natural gas shifts outside the city.

Present carbon trading programs focus largely on the production-side (e.g., cleaner electric power plants, cement factories and oil refineries), and do not provide credit for innovative cityscale policies that change the nature of materials demand in cities, illustrated in the examples above. To promote holistic GHG mitigation strategies in cities, including cross-sector (e.g. teleconferencing), supply chain (e.g., green concrete) and lifestyle change (e.g., healthy diets) strategies, we propose that both the required existing Scope 1+2 GHG emissions inventory for a city (5), and the expanded Scope 1+2+3 emissions footprint developed in this paper, be applied together with two logic rules:
  • 1. Credit GHG reduction strategies that reduce a city’s Scope 1+2 GHG inventory only if they also reduce the city’s broader Scope 1+2+3 GHG emissions footprint; credit is recommended for the smaller of the two reductions. This prevents unintended incentives to shift GHG emissions across city boundaries.
  • 2. Incorporate flexibility to award cities credit for innovative strategies that demonstrate additionality and can quantifiably reduce their Scope 1+2+3 GHG footprint, even if the Scope 1+2 emissions inventory does not show reductions. For example, GHG mitigation credit could be distributed between fly ash suppliers and a city, if the latter’s green concrete policy explicitly demonstrates additional fly ash use to displace cement in concrete, when compared to business-as-usual.”

4. Results/outcomes/successes/failures/lessons learned: In the example of Denver, cement use added 0.3 million metric tons of “indirect” CO2e emissions and home food purchases added 1.4 million metric tons of “indirect” CO2e emissions to “traditional” emissions of 11.1 million metric tons of CO2e (from electricity and natural gas use and surface vehicle transportation). Airline travel by Denver area residents (as opposed to all travel in and out of the regional airport) added another 0.9 million metric tons, and emissions from fuel production added another 1.1 million metric tons. In contrast, the credit for recycling and landfill gas recovery was quite small, at -0.2 million metric tons.

The inclusion of the emissions associated with airline travel and key urban materials brought Denver’s per-capita GHG emissions very close to per-capita averages for the State of Colorado and the US as a whole. In a subsequent paper, Ramaswami et. al. extended this analysis to include 7 other cities (including Portland and Seattle) and found similar results.