Greenhouse gas (GHG) accounting for individual cities is confounded by spatial scale and boundary effects that impact the allocation of regional material and energy flows. This paper develops a demand-centered, hybrid life-cycle-based methodology for conducting city-scale GHG inventories that incorporates (1) spatial allocation of surface and airline travel across colocated cities in larger metropolitan regions, and, (2) life-cycle assessment (LCA) to quantify the embodied energy of key urban materialssfood, water, fuel, and concrete. The hybrid methodology enables cities to separately report the GHG impact associated with direct end-use of energy by cities (consistent with EPA and IPCC methods), as well as the impact of extra-boundary activities such as air travel and production of key urban materials (consistent with Scope 3 protocols recommended by the World Resources Institute). Application of this hybrid methodology to Denver, Colorado, yielded a more holistic GHG inventory that approaches a GHG footprint computation, with consistency of inclusions across spatial scale as well as convergence of city-scale per capita GHG emissions (∼25 mt CO 2 e/person/year) with state and national data. The method is shown to have significant policy impacts, and also demonstrates the utility of benchmarks in understanding energy use in various city sectors.

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Cities around the United States are investing in bicycle infrastructure, and to secure additional transportation funding, cities are reporting bicycle use and safety improvements. Data on bicyclist traffic volume is necessary for performing safety studies and reporting facility use. Meeting the need for data, available manual bicycle counting programs count cyclists for a few hours per year at designated locations. A key issue in the design of counting programs is determining the timing and frequency of counts needed to obtain a reliable estimate of annual average daily bicyclists (AADB). In particular, in which days of the week, hours of the day, and months of the year should counts be collected? And, most important to program cost, how many hours should be counted? This study used continuous bicycle counts from Boulder, Colorado, to estimate AADB and analyze the estimation errors that would be expected from various bicycle-counting scenarios. AADB average estimation errors were found to range from 15% with 4 weeks of continuous count data to 54% when only 1 h of data was collected per year. The study found that the most cost-effective length for short-term bicycle counts is one full week when automated counting devices specifically calibrated for bicycle counting are used. Seasons with higher bicycle volumes have less variation in bicycle counts and thus more accurate estimates.

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