The determinants of double energy vulnerability: A geospatial analysis

Stefan Bouzarovski, Paul McKenzie, Aileen Lonie, Amish Sarpotdar, Giulio Mattioli, Mari Martiskainen

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Abstract

This paper examines the spatial and social differences in people's lack of access to adequate energy and transport services in the UK. We respond to the need for developing a differentiated understanding of both the drivers and expressions of this ‘double energy vulnerability’ (DEV), while seeking to integrate and analyse relevant information from all four UK nations. Using a variety of statistically representative census and survey datasets, the paper develops a series of multi‐dimensional indices to map transport‐ and energy‐related injustices. This is followed by a cluster analysis to examine broad‐level geographical patterns, and a Geographically Weighted Regression (GWR) model to explore the spatial variation of vulnerabilities related to contingencies such as income, ethnicity and housing. The paper corroborates the results of previous qualitative studies, and research within selected UK nations, while revealing several unexpected territorial clusters and underpinnings of infrastructural injustice. DEV is shown to disproportionately affect coastal, highland, peripheral and rural regions, with an internal granularity that exhibits high levels of variation within urban and peri‐urban settings.
Original languageEnglish
Article numbere12610
Pages (from-to)1-13
Number of pages13
JournalGEOGRAPHICAL JOURNAL
Early online date28 Nov 2024
DOIs
Publication statusPublished online - 28 Nov 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). The Geographical Journal published by John Wiley & Sons Ltd on behalf of Royal Geographical Society (with the Institute of British Geographers).

Data Access Statement

The data that support the findings of this study are openly available with the UK Data Service at https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=857062

Keywords

  • cluster analysis
  • energy poverty
  • geographically weighted regression
  • spatial inequality
  • transport poverty
  • United Kingdom
  • Geographically Weighted Regression

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