MOT: Motoring and car ownership trends in the UK

Chatterton, Tim and Anable, Jillian and Cairns, Sally and Wilson, R.E. (2017) MOT: Motoring and car ownership trends in the UK. UWE https://researchdata.uwe.ac.uk/id/eprint/172/

Brief summary of project

THIS DATASET IS NOT PUBLICLY AVAILABLE. THIS IS A METADATA ONLY RECORD. Efforts to reduce the emissions from car travel have been hampered by a lack of specific information on car ownership and use. In 2010, the Department for Transport released a dataset containing annual MOT test records for cars from 2005 onwards, with regular updates promised. By providing relatively comprehensive information about British car ownership and use, this dataset provides a key opportunity to address a number of issues in transport and energy debates. For the first time precise links can be made between car use and car type, and changes in use over space and time can be examined on a relatively complete basis. When combined with a wealth of other existing data sets (not least the new information from the 2011 Census), a range of new and important insights should emerge. Having already worked together as a project team to scope the use of this data in a small EPSRC-funded study in 2011, we now propose to use it as a platform upon which to develop a set of interlinked modelling and analysis tasks using multiple sources of vehicle-specific and area-based data. A set of interdependent workpackages will span three years to investigate spatial and temporal differences in car ownership and use, the determinants of those differences, and how levels may change over time and in response to various policy measures. The relationships between car ownership, car use, fuel use and vehicle emissions, and the demographic, economic, infrastructural and socio-cultural factors influencing these will be tested mathematically using spatial statistics, regression modelling and scenario analyses. Linkages will also be made with spatial patterns of domestic gas and electricity usage in order to understand relationships within and between these end-user energy demands. The new analysis capability will be tested through case study evaluation of local transport policies. By enabling car ownership and use to be examined at relatively fine spatial and temporal scales, and via techniques to identify areas sharing important 'background' characteristics, it should be possible to answer key questions for sustainable transport policies such as, what difference to car ownership and use have particular policies achieved (compared with areas where these policies were not in place)? It will also be able to calculate figures for fuel use and emissions to contribute to the development of policies specifically targeted at the most energy intensive or polluting drivers or localities. We will also be able to link energy use from cars, with domestic energy usage through household electricity and gas. This will allow us to build up a much better picture of energy and carbon footprints across the country. When linked to patterns of income, multiple deprivation and other socio-economic factors, there will be insights for the design of much more effective climate and energy policies, and to ensure that the burden of these is borne equitably. The project will be supported by an Applied Statistics Expert Panel, and includes provision for workshops with key stakeholders to help shape the work. The project will also help develop a specification for a possible web-based tool to enable a wide community of users to undertake their own analysis on these sorts of issues, using the data and tools that we develop. In order to achieve our goals, we will develop methods to overcome the challenges of merging a range of important but disparate datasets, based on varying spatial, temporal and other characteristics, and subject to varying issues of data protection and sensitivity. Our scoping study demonstrated that there are very significant technical challenges to be overcome in working with datasets of this size and nature, and a wide range of disciplines may be able to learn from this work. The analysis frameworks and the new scientific understanding delivered will be the important legacies of this project.