Leveraging the Google Cloud to Estimate Individual Level CO2 Emissions Linked to the School Commute

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Internationally, the rates of active transport (e.g. cycling or walking) to school are in decline and the corollary switch to less sustainable modes of travel are linked with negative effects on the environment in terms of increased emissions, increasing traffic congestion around schools and negative health impacts related to lower physical activity levels or pollutant exposure. In a UK context, schools account for 15% of total public sector emissions (DCFS, 2010), which in England is estimated to be the equivalent of around 9.4 million tonnes of CO2 per year (SDC, 2006). 7% (658k tonnes) of this total is associated with the pupil-school commute, and as such, there are significant environmental benefits of pupils adopting more sustainable travel behaviours.This research project creates a national coverage and geographically sensitive model of CO2 emissions linked with the school commute. This involves the integration of a variety of public sector "big data", including the origin destination and mode choices for around 7.5 million pupils, and small area estimates of the emission characteristics of cars registered within very small geographic areas. These data are integrated to create a geographically sensitive estimate measure of an individual pupils contribution of CO2 related to their journey. The computational burden of processing such large data, and especially in estimating routes to school at a transport network level (road, rail etc) are great. The Google cloud environment is utilised in this research to reduce this computational burden.Given the spatial diversity of population characteristics and circumstance, alongside differences in local infrastructure and policy; a 'one size fits all' approach to tacking the issue of emissions linked to the school commute is unlikely to be as fruitful as interventions tailored to local context. With the increasing availability of cloud computing in an era of public sector "big data", localised and geographically intelligent modelling approaches are increasingly accessible to the social sciences. However, technical challenges aside, there are also critical ethical concerns that need to be addressed related to data disclosure and privacy. As such this project establishes both technical procedures and also makes recommendations about the ethical use of cloud technology within the context sensitive individual level data.For the first time, the ambitious spatial modelling techniques presented in this research integrate geographically localised input parameters and control for geographical context in the calibration of emissions linked to the school commute, enabling outputs to be explored down to the level of an individual. This research will map the geography of mode choice and emissions, also measure how influences on these patterns vary spatially.