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|Title: ||Energy use and related emissions of the UK residential sector: quantitative modelling and policy implications|
|Authors: ||Prestwood, Emily|
|Keywords: ||UK residential sector|
Greenhouse gas emissions
Data envelopment analysis
|Issue Date: ||2016|
|Publisher: ||© Emily Prestwood|
|Abstract: ||Reducing energy demand and carbon emissions from the UK housing stock through efficiency improvements is the focus of policy interest. The 2008 UK Climate Change Act set legally binding targets of an 80% reduction in greenhouse gas emissions against a 1990 baseline. The majority of emissions in the residential sector are carbon dioxide emissions arising from energy used for heating homes and water, cooking, lighting and electrical appliances. The sector s contribution to total UK emissions is significant and therefore reducing energy use in homes is an important factor if the UK is to meet its targets.
In this research an initial survey of studies of the residential sector has been conducted to review factors considered to influence energy use and related emissions in UK housing. Further review identified energy and climate change policy instruments and structural change in the energy supply sector between 1970 and the present. A subsequent time-line of policy and events describes the changing, historical policy landscape related to energy efficiency improvements in the sector. As a result of these reviews, a need to better understand how householders have responded to technical energy efficiency improvements in housing, and the influence of social and economic factors, was identified as a research gap.
In order to model householders historical behaviour Data Envelopment Analysis (DEA) was identified as an innovative approach for this field of research as a potential means to measure sector efficiency in a new way. The analysis has two stages. In the first, DEA is used to measure the relative efficiency with which the UK housing sector has managed its energy use and related emissions to deliver energy services such as space heating and lighting to householders. In the second stage, multiple regressions are used to examine whether the variability over time in the efficiency measure can be explained by policy interventions, energy market developments, and economic and social factors.
DEA is a method for modelling the relative performance efficiency with which an observed sample converts measurable inputs to quantitative outputs. In this research, samples consist of annual observations of the UK housing stock, using data largely taken from DECC s UK housing energy fact file. An efficiency frontier of performance enveloping the observed sample points as closely as possible is constructed through DEA mathematical programming. The core of the analysis lies in identifying relevant quantitative input and output measures from available data. A range of measures of comfort and energy service levels to represent energy service outputs, and household energy and emissions data to represent inputs are examined in the analysis. The result is a timeline of efficiency performance that can be related to socio-economic change and the history of policy interventions.
The analysis shows that the efficiency of the UK housing stock to manage its energy use and related emissions has not followed the steady upward trend that might have been expected from technical innovation. There is evidence of rebound effects over time, with householders behaviour in response to technical efficiency improvements acting to raise comfort levels rather than lower energy usage. Nevertheless, statistically significant roles can be identified for factors such as income, price and tenure which have implications for policy design and control and lead to a number of policy recommendations.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Sponsor: ||School of Civil and Building Engineering, Loughborough University|
|Appears in Collections:||PhD Theses (Civil and Building Engineering)|
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