Abstract
Anthropogenic climate change is a global problem that affects every country and each individual. It is largely caused by human beings emitting greenhouse gases into the atmosphere. In general, a small percentage of the population is responsible for a large amount of emissions. This paper focuses on high emitters and their CO2 emissions from energy use in UK homes. It applies a cluster approach, aiming to identify whether the high emitters comprise clusters where households in each cluster share similar characteristics but are different from the others. The data are mainly based on the Living Cost and Food survey in the UK. The results show that after equivalising both household emissions and income, the high emitters can be clustered into six groups which share similar characteristics within each group, but are different from the others in terms of income, age, household composition, category and size of the dwelling, and tenure type. The clustering results indicate that various combinations of socioeconomic factors, such as low-income single female living in an at least six-room property, or high-income retired couple owning a large detached house, could all lead to high CO2 emissions from energy use at home. Policymakers should target each high-emitter cluster differently to reduce CO2 emissions from energy consumption at home more effectively.
Original language | English |
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Pages (from-to) | 612-625 |
Number of pages | 14 |
Journal | Frontiers in Energy |
Volume | 13 |
Issue number | 4 |
Early online date | 15 Dec 2019 |
DOIs | |
Publication status | Published - Dec 2019 |
Bibliographical note
Funding Information:This work was funded by the School of Mechanical, Aerospace and Civil Engineering and the Sustainable Consumption Institute at the University of Manchester.
Publisher Copyright:
© 2019, The Authors.
Keywords
- cluster analysis
- emissions reduction
- energy use
- high emitters
- household energy consumption
- socioeconomic factors
ASJC Scopus subject areas
- Energy Engineering and Power Technology