Spatial Energy Footprints
Lead Research Organisation:
University of Leeds
Department Name: Sch of Geography
Abstract
It is important for the UK Government to be able to predict the future energy needs of the country to meet climate change reduction targets. Currently, the Government uses estimates of economic growth, the price of fuel and the number of households to calculate its Carbon Budget. This technique for predicting future energy needs is deficient, because it fails to take account of the fact that the major driver of industrial energy use is household demand for goods and services.
This PGR scholarship will use annual estimates of household expenditure data, provided by CallCredit, to calculate the contribution different types of households make towards the UK's total energy needs and how this has changed over time. The project will generate a time-series dataset of household energy footprints, covering the period either side of the recession, at the census output area level. Using this fine grained spatial and temporal data it will be possible to use spatial analysis techniques (such as local indicators of spatial clustering) to discover the areas of the UK where residents contribute most and least to the UK's energy use. Linkage with area based geodemographics will enable local benchmarking linked to underlying neighbourhood characteristics (e.g. as a result of population ageing), identification of change over time and possible explanations for the change in impact. For example, it might be possible to correlate the effect of localised retrofit schemes on the home energy footprint (drawing on localised cases studies using LIDA local government partners) or to consider the effectiveness of healthy eating plans on the food energy footprint. The recession changed the spending behaviours of different types of households - in which parts of the UK do we find households changing their consumption to an energy intensive basket of goods and where have we found pro-environment behaviours? The findings from this PGR Scholarship will provide useful insights as to the effectiveness of behaviour change policy.
This PGR scholarship will use annual estimates of household expenditure data, provided by CallCredit, to calculate the contribution different types of households make towards the UK's total energy needs and how this has changed over time. The project will generate a time-series dataset of household energy footprints, covering the period either side of the recession, at the census output area level. Using this fine grained spatial and temporal data it will be possible to use spatial analysis techniques (such as local indicators of spatial clustering) to discover the areas of the UK where residents contribute most and least to the UK's energy use. Linkage with area based geodemographics will enable local benchmarking linked to underlying neighbourhood characteristics (e.g. as a result of population ageing), identification of change over time and possible explanations for the change in impact. For example, it might be possible to correlate the effect of localised retrofit schemes on the home energy footprint (drawing on localised cases studies using LIDA local government partners) or to consider the effectiveness of healthy eating plans on the food energy footprint. The recession changed the spending behaviours of different types of households - in which parts of the UK do we find households changing their consumption to an energy intensive basket of goods and where have we found pro-environment behaviours? The findings from this PGR Scholarship will provide useful insights as to the effectiveness of behaviour change policy.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ES/P000401/1 | 01/10/2017 | 30/09/2024 | |||
| 2106884 | Studentship | ES/P000401/1 | 01/10/2018 | 31/12/2022 | Lena Kilian |
| ES/S50161X/1 | 01/10/2018 | 31/12/2022 | |||
| 2106884 | Studentship | ES/S50161X/1 | 01/10/2018 | 31/12/2022 | Lena Kilian |
| Description | This project focussed on estimating and evaluating household greenhouse gas footprints of UK neighbourhoods. In light of an increased involvement of local actors in climate change mitigation, understanding local greenhouse gas emission trends is vital. Particularly in countries and cities with high consumption-based footprints, recognising how local consumption contributes to global and national emissions is key for effective emission reduction. Typically, national emissions are disaggregated using consumption and expenditure microdata. To assess the reliability of such an approach, I generated emission estimates using three UK microdata datasets from the year 2016 and compared their emission outputs, when levels of spatial and product details are high. These datasets include the Output Area Classification (a publicly available geodemographic classification), the Living Costs and Food Survey (an openly available expenditure survey), and a commercial household expenditure dataset by TransUnion. Findings indicate moderate levels of similarity between most emission estimates even at detailed product and spatial levels. Importantly, stronger positive correlations are found between estimates from higher-emission products. This suggests that different microdata generate mostly similar total greenhouse gas footprint estimates at a neighborhood level. Nevertheless, levels of similarity vary by products and geographies, highlighting the importance of understanding the sources of uncertainty in different microdata. This research concludes that in order to meaningfully and accurately interpret subnational, product-level emissions, microdata selection must consider limitations and uncertainties from the data generation process, the necessary levels of disaggregation, and the availability of physical consumption units rather than expenditure data for high-emission products. |
| Exploitation Route | The footprint estimates will be made available for others to use. This allows researchers and policy makers to use the data to understand and explore spatial emission patterns. |
| Sectors | Communities and Social Services/Policy,Energy,Environment |
| Description | Some data and insight on Bristol's emissions from 2016 have been shared with Bristol Council. These helped Bristol Council understand local patterns of emissions better and highlight links between neighbourhood emissions and residents' feelings about climate change. |
| First Year Of Impact | 2020 |
| Sector | Environment |
| Impact Types | Policy & public services |