Estimating greenhouse gas emissions of European cities — Modeling emissions with only one spatial and one socioeconomic variable
Introduction
Climate change is upon us (EEA, 2014), and cities play a key role in managing its anthropogenic drivers and environmental effects (Heidrich et al., 2013, Reckien et al., 2014). They are home to more than 53% of the global population with numbers rising (The World Bank Group, 2014) and will inevitably have to cope with the impacts of changing climatic patterns due the additive microclimatic characteristics of cities (Hallegatte, 2009, IPCC, 2014a, McDonald et al., 2014, Patz et al., 2014). Concurrently, concentrating on the main drivers of greenhouse gas (GHG) emissions they are expected to be responsible for up to 70% of global emissions (United Nations Human Settlement Programme, 2011) and thus directly affecting the intensity of future climatic changes (IPCC, 2014b). Minding their importance to the global climate, cities are urged to promote sustainable development and lifestyles leading to lower GHG emissions per inhabitant (Glaeser and Kahn, 2010, Hoornweg et al., 2011, Ala-Mantila et al., 2014).
Therefore, more knowledge is needed to understand the interactions between cities and the climate system (Romero Lankao and Dodman, 2011) to generate standardized approaches to assess urban GHG emissions. Although international standards for GHG inventorying are currently being developed (Bhatia and Ranganathan, 2004, UNFCCC united NF on CC, 2007; C40 Cities, 2012), a more applicable and easily reproducible approach that integrates the biggest emission drivers is still lacking, especially for cities.
In the presented study, it is therefore hypothesized that considering only few socioeconomic and spatial indicators is sufficient to estimate a city's GHG emissions. By analyzing previously found potential GHG determinants, the presented study identifies the most significant emission drivers and thus, essentially contributes to the closing of this research gap. In this regard, it was shown that the specific energy mix can strongly influence urban GHG emissions (Kennedy et al., 2009a, Baur et al., 2013). Moreover, certain socioeconomic and demographic variables, as well as spatial patterns and land use and land cover (LULC) characteristics of cities were found to correlate with GHG emissions (Baur et al., accepted for publication, Kennedy et al., 2009b, Marcotullio et al., 2013).
Focusing on the most significant emission drivers of 44 European cities, multiple statistical analyses are conducted to develop an easy-to-apply model that supports estimating urban GHG emissions and finding possible emission mitigation strategies. In particular, the consideration of spatial data is expected to help in comparatively assessing emissions. By investigating ten further EU urban areas the quality of the presented model is validated in order to prove its usefulness for estimating urban GHG emissions. In the end, the results can be used to derive possible climate change mitigation strategies or compare respective best practice examples. The findings will also help to draft appropriate urban planning policies to adapt Europe's cities to changing climatic conditions.
Section snippets
Data
For analyzing the most influential GHG emission drivers of European cities, both socioeconomic as well as spatial properties were investigated. The cities under investigation present different city archetypes, according to specific socioeconomic structures, spatial patterns, or regional distributions.
Information about the GHG emissions per capita is based on the emission data set developed by Baur et al. (2013). In the original data set, urban scope 1 and 2 CO2eq emissions (Bhatia and
Identification of significant GHG emission drivers
Statistical analyses (Section 2.2) resulted in the following final multiple linear model (Eq. (1)). The corresponding statistics are shown in Table 1.
In Eq. (1), GHG emissions (CO2eq-m) are estimated by a linear model of the independent variables household size (HH) and edge density of the LULC class discontinuous dense urban fabric (ED DDUF). Both variables logarithmically correlate with GHG emissions per capita. Household size describes the average
Conclusions and outlook
The initial hypothesis was that only a few socioeconomic and spatial parameters are required to estimate urban GHG emissions in EU cities. To validate this assumption, a multiple linear model has been calculated including the parameters that were previously identified as crucial urban GHG emission drivers. The results show that information on the household size and the edge densities of the discontinuous dense urban fabric are satisfactory to enable GHG emission estimates for a variety of
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