Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable

Albert H Baur; Steffen Lauf; Michael Förster; Birgit Kleinschmit

doi:10.1016/j.scitotenv.2015.03.030

Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable

Sci Total Environ. 2015 Jul 1:520:49-58. doi: 10.1016/j.scitotenv.2015.03.030. Epub 2015 Mar 17.

Authors

Albert H Baur¹, Steffen Lauf², Michael Förster², Birgit Kleinschmit²

Affiliations

¹ Geoinformation in Environmental Planning Lab, Department of Landscape Architecture and Environmental Planning, Technische Universität Berlin, Straße des 17. Juni 145, Office EB5, D-10623 Berlin, Germany. Electronic address: Albert.H.Baur@campus.tu-berlin.de.
² Geoinformation in Environmental Planning Lab, Department of Landscape Architecture and Environmental Planning, Technische Universität Berlin, Straße des 17. Juni 145, Office EB5, D-10623 Berlin, Germany.

PMID: 25794971
DOI: 10.1016/j.scitotenv.2015.03.030

Abstract

Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available.

Keywords: Climate change mitigation; Household size; Landscape metrics; Multiple regression model; Sustainable urban development; Urban greenhouse gas emissions.