Quantifying and visualising anthropogenic emissions of CO2 in the urban environment
Exploring trends in the spatial distribution of emissions
K.C.K. Vierling (TU Delft - Technology, Policy and Management)
Sander van Cranenburgh – Mentor (TU Delft - Transport and Logistics)
N. Goyal – Graduation committee member (TU Delft - Organisation & Governance)
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Abstract
About 75 percent of CO2 emissions caused by the combustion of fossil fuels stem from cities. Climate policy minimising the amount of emitted carbon can not be evaluated without accurate quantification of CO2 emissions.
Not only the amount of emitted carbon is of interest, but also being able to place emissions in both the spatial as well as the temporal context is necessary. This gives policy makers and scientist the ability to compare the climate impact of neighbourhoods and cities. For doing so, it is necessary to have a rasterised emission estimate with a high resolution.
This study proposes a new method using a Random Forest model trained on the Hestia dataset. The model utilises spatial co-variates, such as population density, night time lights, to estimate emissions in a resolution of 1 X 1 km.
To assess the spatial generalisability of the model, the emission observations of the Hestia dataset are compared against model estimates. The explained variance and R2 is the highest in log transformed form at ∼0.47. The features and hyper-parameters
selected through this method are found to be stable under a variety of model specifications. This provides evidence that the presented model performance generalisability are the that could be achieved given the current data. The relevant covariates for the model are the following, in order of importance: Nightly radiance, intersection density, population, GDP, PM25 and Pedestrian intersections. Inspection of the partial dependence shows that each of these variables, except PM25, have a positive increasing relationship with the emission estimate. This relationship quickly converges for higher values. The highest relative errors mainly occur around the peripheries of cities, for cells with low emission values.
This study lays forward a new scalable approach to estimate emissions and the spatial distribution in urban areas. Currently the study data is limited, but as more data becomes available a Random Forest regression approach could prove to produce accurate insights. Also the findings in model design and evaluation could be used in future studies. For city planners the results can be used to gain a first insight in the spatial distribution of emissions within their city. Pinpointing high emission zones or comparing different urban areas. Knowledge of the spatial distribution of cities can be a pathway of understanding how to develop less carbon intensive cities.