A53 Cycling health and climate: what are the circumstances under which cycling can contribute to substantive greenhouse gas emission reductions and whose travel should be targeted to achieve the biggest health and climate benefits?. Issue 2 (June 2015)

• Record Type: Journal Article
• Authors: Woodcock, James; Lovelace, Robin; Tainio, Marko
• Is Part Of: Journal of transport & health. Volume 2:Issue 2(2015:Jun.)Supplement
• Page Start: S32
• Publication Date: 2015-06
• Subjects: Transportation -- Health aspects -- Periodicals; Transportation -- Periodicals; Public Health -- Periodicals; Noise, Transportation -- Periodicals; Air Pollutants -- Periodicals; 388
• DOI: 10.1016/j.jth.2015.04.541
• Languages: English
• ISSNs: 2214-1405

Abstract

Abstract: Background: Co-benefits are defined as the health and other benefits that can accompany greenhouse gas emission reductions. Modelling studies have identified the potential for large health co-benefits in the transport sector from a mode shift to physically active transport. Findings on emission reductions from active travel have been less clear. Cycling is usually an option for shorter trips, typically exhibiting a strong distance decay function, whilst in most settings the majority of emissions come from longer trips. Despite this there has been little systematic investigation of the circumstances under which active travel can achieve emission reductions and how such scenarios would relate to the size of the health benefits. Health and carbon benefits vary according to who is changing their behaviour and the kind of trips they make and policy goals may not always be aligned. Methods: In this study we will fill this gap using a microsimulation model based on individual level travel survey data from England combined with the ITHIM health model. The ITHIM model estimates health impacts from transport scenarios using a comparative risk assessment approach and incorporates variation in disease risk and baseline physical activity levels both between and within age and gender groups. Changes to distributions of physical activity are applied to multiple diseases, using WHO Global Burden of Disease data. The microsimulation model has been implemented in R and parameterised with the English National Travel Survey. In the model four parameters are allowed to vary: 1) the cycling mode split, 2) the distribution of travel distances, 3) the distance decay function for cycling (based on electric bikes), 4) the proportion of multimodal trips amongst longer trips. The potential for trip distance reduction from changing destinations will be allowed to vary by trip purpose, with more individual level latitude for changing non-commuting trips. Results: We will search the output of the model to identify those combinations of strategies that would achieve substantive emissions reductions and investigate how these correlate with health benefits. Using person level data we will investigate the top population groups to target for mode shift from a health, a carbon, and a co-benefits perspective. Conclusions: The results of this study will guide policy on the extent on urban planning and inform development of cycling policies that can achieve multiple societal benefits.