Feng, Yuan, Miao, Ying and Turner, Ed (2025). Examining spatial variations in the relationship between domestic energy consumption and its driving factors using multiscale geographically weighted regression: a case study in Nottingham, England. Energy, Sustainability and Society, 15 ,
Abstract
Background Domestic energy consumption contributes to over a quarter of the UK’s carbon emissions, understanding how it is driven can be helpful for delivering a fair energy transition to net zero. Energy usage is noted as a spatial phenomenon, however, the spatial variability of how it is driven is rarely considered in existing UK studies. To contribute to this research gap, this study examines the spatial variations in the relationship between domestic energy consumption and its driving factors using the local spatial statistical modelling technique multiscale geographically weighted regression (MGWR). With explanatory variables on dwelling and household characteristics, this study analyses data at Lower Layer Super Output Area (LSOA) level on the study area, Nottingham, a somewhat socio-economically deprived city that also has the UK’s largest district heating (DH) system supplying low-carbon residential heating. Results The study reveals domestic energy consumption is driven by factors at different spatial scales with spatially varied or even spatially heterogeneous patterns. Specifically, higher domestic energy consumption is affected differently across local areas by larger percentages of dwellings with 4 or more bedrooms, unemployment, terraced dwellings, whilst by smaller percentages of social-rented housing tenures and central heating type of district heating. The impacts of dwelling energy efficiency, median household income, percentage of households with 3 or more people, fuel poverty, and central heating with renewable energy, vary across different local areas. Therefore, while there are identifiable relationships between these factors and domestic energy consumption, they differ by locality, and aggregated level analysis may fail to accurately to capture these patterns. Conclusions Nuanced local patterns of how domestic energy consumption is driven suggest placed-based approaches and more local deliberation to devise policies may be more suitable than “one-size-fit-all” policy plans to achieve the envisioned outcomes of rapid and fair domestic energy decarbonisation and just energy transition to net zero.
Publication DOI: | https://doi.org/10.1186/s13705-025-00523-1 |
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Divisions: | College of Business and Social Sciences College of Business and Social Sciences > School of Social Sciences & Humanities College of Business and Social Sciences > School of Social Sciences & Humanities > Politics, History and International Relations College of Business and Social Sciences > School of Social Sciences & Humanities > Aston Centre for Europe Aston University (General) |
Funding Information: | This research is funded by the Engineering and Physical Sciences Research Council (EPSRC), UK under Grant No. EP/V041452/1, held at University of Nottingham (Prof Jo Darkwa) and Aston University. |
Additional Information: | Copyright © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. |
Uncontrolled Keywords: | Domestic energy consumption,Geographically weighted regression,Multiscale geographically weighted regression,Lower layer super output areas,SDG 7 - Affordable and Clean Energy,SDG 11 - Sustainable Cities and Communities |
Publication ISSN: | 2192-0567 |
Data Access Statement: | This study uses publicly available data sources as outlined in section ‘Data Collection’. The data on Nottingham analysed in this study can be accessed via https://doi.org/10.17036/researchdata.aston.ac.uk.00000629. |
Last Modified: | 02 Jun 2025 16:21 |
Date Deposited: | 12 May 2025 10:21 |
Full Text Link: | |
Related URLs: |
https://energsu ... 705-025-00523-1
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2025-05-11 |
Accepted Date: | 2025-04-27 |
Authors: |
Feng, Yuan
(![]() Miao, Ying ( ![]() Turner, Ed ( ![]() |