Who, Why, and for How Long? Understanding Public Support for Low Emission Zones in the UK

: (Alternative Format Thesis)

Abstract

Climate change and air pollution are commonly cited as two of the greatest threats of our time. A common strategy to address these challenges are Low Emission Zones, which are designated urban areas that charge the most polluting vehicles to enter. Across Europe, their implementation has generated considerable public debate – often resulting in their withdrawal or redesign. To avoid this, policymakers must design Low Emission Zones to be fair, effective, and informed by their community’s needs.

Previous quantitative research has begun to explore the determinants of Low Emission Zone support before their introduction, with a smaller body of work investigating this post-implementation. However, very little is understood about the factors that influence long-term support, why support changes over time, and the public’s reasons for support and opposition. Similarly, previous work has sparsely investigated novel, mixed-methods methodologies to better understand policy preferences in large populations.

Across four empirical studies, this thesis uses a mixed-methods approach to understand evolving support for Low Emission Zones. Chapter 2 explores the determinants of support before Low Emission Zone introduction in a UK city – finding that both psychological and practical factors contribute to support. In Chapter 3, a longitudinal panel design was used to track people’s opinions about a Low Emission Zone, from two months before implementation to a year after. This design built a firm understanding of the determinants of long-term support, and the variables that explain changes in support over time.

In response to the financial and practical challenges policymakers face in analysing large amounts of free-text consultation data, Chapter 4 develops the Deep Computational Text Analyser (DECOTA), a novel Machine Learning methodology that automatically analyses free-text policy data. The methodology was validated against four independent free-text datasets analysed using thematic analysis, achieving approximately 92% overlap in sub-themes and 90% in themes, while being 378 times faster and 1920 times cheaper. Finally, Chapter 5 uses DECOTA to understand people’s nuanced and evolving reasons for support and opposition, in a longitudinal qualitative dataset. Importantly, this work notes that people cannot be neatly characterised as ‘supporters’ or ‘opposers’.

Taken together, this thesis provides a nuanced conceptual understanding of evolving Low Emission Zone support, whilst providing policymakers with an efficient, cost-effective, and accessible tool to better understand their community’s policy needs.

Date of Award	25 Jun 2025
Original language	English
Awarding Institution	University of Bath
Supervisor	Lorraine Whitmarsh (Supervisor) & Christina Demski (Supervisor)