The purpose of this paper is to describe global urban greenhouse gas emissions by region and sector, examine the distribution of emissions through the urban-to-rural gradient, and identify covariates of emission levels for our baseline year, 2000. We use multiple existing spatial databases to identify urban extent, greenhouse gas emissions (CO 2 , N 2 O, CH 4 and SF 6) and covariates of emissions in a "top-down" analysis. The results indicate that urban activities are significant sources of total greenhouse gas emissions (36.8 and 48.6 % of total). The urban energy sector accounts for between 41.5 and 66.3 % of total energy emissions. Significant differences exist in the urban share of greenhouse gas emissions between developed and developing countries as well as among source sectors for geographic regions. The 50 largest urban emitting areas account for 38.8 % of all urban greenhouse gas emissions. We find that greenhouse gas emissions are significantly associated with population size, density, growth rates, and per capita income. Finally, comparison of our results to "bottom-up" estimates suggest that this research's data and techniques are best used at the regional and global scales. 1 Introduction Decision makers need baseline data, analysis and monitoring of urban greenhouse gas emissions to verify the effectiveness of policy measures. For example, while urban planners take

The world's metropolitan carbon footprints have distinct geographies that are not well understood or recognized in debates about climate change, partly because data on greenhouse gas emissions is so inadequate. This article describes the results of the most comprehensive assessment of carbon footprints for major American metropolitan areas available to date, focusing on residential and transportation carbon emissions for the largest 100 metropolitan areas in the United States. These findings are put into the context of efforts across the country and the globe to characterize carbon impacts and policy linkages.

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The paper explores relationships between seven dimensions of land use in 1990 and subsequent levels of three traffic congestion outcomes in 2000 for a sample of 50 large US urban areas. Multiple regression models are developed to address several methodological concerns, including reverse causation and time-lags. Controlling for prior levels of congestion and changes in an urban area's transport network and relevant demographics, it is found that: density/ continuity is positively related to subsequent roadway ADT/lane and delay per capita; housing centrality is positively related to subsequent delay per capita; and housing-job proximity is inversely related to subsequent commute time. Only the last result corresponds to the conventional wisdom that more compact metropolitan land use patterns reduce traffic congestion. These results prove two points: that the choice of congestion measure may substantively affect the results; and that multivariate statistical analyses are necessary to control for potentially confounding influences, such as population growth and investment in the transport network.

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