Mapping multidimensional energy deprivation: Socio-spatial inequalities and policy implications in Great Britain
Meixu Chen; Caitlin Robinson; Alex Singleton (2025). Computers, Environment and Urban Systems, 121, 102324. DOI: 10.1016/j.compenvurbsys.2025.102324
Abstract
This work provides a thorough Energy Deprivation Segmentation (EDS) for Great Britain, which aims to address the complex and varied aspects of energy poverty in different small regions. By proposing a reproducible analytical framework, we combine many data sources to provide a comprehensive segmentation that encompasses various dimensions such as energy efficiency, accessibility, demand and supply, housing conditions, and financial vulnerability. The results indicate notable disparities in energy deprivation based on social and spatial factors. We observed higher degrees of deprivation in the peripheral areas of major cities and suburbs in the northern regions of England, southern regions of Wales, and central regions of Scotland. The created EDS identifies six top-level Supergroups and 14 finer Groups and was validated internally and externally to confirm its robustness and applicability. This segmentation offers a more comprehensive insights into the characteristics and distribution of energy-deprived neighbourhoods than traditional measures. This research facilitates policymakers to design targeted strategies and resource allocation to combat specific vulnerabilities within communities and foster sustainable and equitable urban growth. Additionally, a practical tool is provided for monitoring and evaluating the effectiveness of policies aimed at reducing energy poverty.
Extended Summary
This research develops a comprehensive Energy Deprivation Segmentation (EDS) to map multidimensional patterns of energy poverty across Great Britain’s 42,648 small areas. The study addresses the critical gap in understanding how energy deprivation manifests differently across neighbourhoods, moving beyond traditional single-criterion measures like income or energy efficiency ratings. Using a systematic geodemographic approach, the research combines multiple open data sources including Energy Performance Certificates, Census data, energy consumption statistics, and benefit information to create a holistic picture of energy vulnerability. The methodology employs K-means clustering analysis across five key domains: energy efficiency, energy access, energy demand and supply, housing conditions, and financial vulnerability. Through rigorous data preprocessing including Box-Cox transformation and correlation analysis, 33 variables were selected from an initial 40 measures to ensure robust classification results. The segmentation identifies six major Supergroups ranging from ‘Energy Efficient Suburbs’ (least deprived) to ‘Energy Deprived Periphery’ (most vulnerable), with 14 finer Groups providing more granular insights. Spatial analysis reveals pronounced geographic inequalities, with higher energy deprivation concentrated in peripheral areas of major cities and suburbs across northern England, southern Wales, and central Scotland. These vulnerable areas are characterised by combinations of poor housing conditions, reliance on expensive prepayment electricity meters, high proportions of lone parents and disabled residents, and limited access to energy infrastructure. The research validates its findings through internal clustering metrics and external comparison with official fuel poverty indicators (LILEE) and Index of Multiple Deprivation data, demonstrating strong alignment between the segmentation and established deprivation measures. The study creates a publicly accessible data tool enabling policymakers, local authorities, and researchers to identify specific energy vulnerabilities within their areas of interest. This granular understanding supports targeted interventions, from immediate financial assistance for households using prepayment meters to long-term infrastructure investments in rural areas lacking gas grid connections. The framework’s reproducible methodology offers significant policy value by enabling monitoring of intervention effectiveness and supporting evidence-based resource allocation to combat energy poverty and promote sustainable urban development across Britain.
Key Findings
- Energy deprivation segmentation identifies six supergroups and fourteen groups across 42,648 small areas in Great Britain
- Higher energy deprivation concentrates in peripheral urban areas of northern England, southern Wales, and central Scotland
- Vulnerable communities feature lone parents, disabled residents, poor housing conditions, and expensive prepayment electricity meters
- The framework successfully validates against official fuel poverty indicators and multiple deprivation indices
- A publicly accessible data tool enables targeted policy interventions and monitoring of energy poverty reduction programmes
Citation
@article{chen2025mapping,
author = {Meixu Chen; Caitlin Robinson; Alex Singleton},
title = {Mapping multidimensional energy deprivation: Socio-spatial inequalities and policy implications in Great Britain},
journal = {Computers, Environment and Urban Systems},
year = {2025},
volume = {121},
pages = {102324},
doi = {10.1016/j.compenvurbsys.2025.102324}
}