Pedalling towards low-carbon resilience : active travel for sustainable future with insights from East Dunbartonshire

Abstract

Transport represents one of the most challenging sectors for achieving decarbonization goals, contributing significantly to greenhouse gas emissions globally and representing the highest emitting sector in Scotland. This research develops a comprehensive framework for sustainable transport decarbonization in East Dunbartonshire by examining the behavioural, strategic, and quantitative dimensions of active travel adoption and emission reduction potential. The study addresses critical knowledge gaps in understanding factors that influence active travel adoption in suburban Scottish communities, comparing the effectiveness of different decarbonization strategies, and quantifying emission reduction scenarios. Data analysis employed multiple statistical techniques including descriptive statistics, reliability analysis using Cronbach's Alpha, chi-square tests for examining associations between variables, and Mean Square Ranking for barrier analysis. Multi-Criteria Decision Analysis (MCDA) was used to systematically compare different decarbonization strategies across criteria including emission reduction potential, cost-effectiveness, and behavioural feasibility. Emission calculations combined survey-derived modal split data with government emission factors to establish baseline transport emissions and model three future scenarios. The research contributes theoretically by demonstrating how individual psychological factors interact with structural barriers to shape collective mobility patterns. Methodologically, it advances the field by integrating behavioural theory with strategic decision analysis and emissions modelling, providing a more comprehensive approach than studies focusing solely on behavioural or technical aspects. Practically, it offers evidence-based recommendations for policymakers seeking to promote sustainable transport in suburban contexts. The study's limitations include relatively small sample size, cross-sectional design, and use of standard emission factors. Future research should adopt longitudinal approaches, incorporate high-resolution travel data, and explore social equity dimensions.