How much behaviour change is required to break even on investing in cycling infrastructure?

Abstract: 
Background Population physical inactivity is putting substantial and avoidable pressure on health systems and public budgets. In recognition of the complexities of improving physical activity behaviours in other domains, over the last decade and particularly since the start of the COVID-19 pandemic, active travel has increasingly gained traction among policy makers as a ‘win-win’ solution to creating healthy and resilient places with benefits for population health, the environment and the local economy. Returns of these often-substantial capital investments most predominantly rely on improvements in population cycling behaviours and associated health and economic benefits. Against this backdrop and in a context of financial constraints, estimating the expected economic value that an additional regular cyclist can generate will be important for policy decision-makers. This will enable identifying the minimum behaviour change required at the population level for the expected intervention cost to be offset by the associated benefits.

Methods A break-even point analysis of a case study cycling infrastructure intervention was conducted using an open-access health economic tool. The intervention impact on the susceptible subset of the local population was simulated, considering four domains: physical activity benefits, air pollution, crash risk and carbon emissions. An iterative computational approach was applied to identify the behaviour change targets (regular cycling) which would lead to a return of 100% on investment. A series of deterministic sensitivity and scenario analyses were conducted to assess robustness of the base case results.

Results On average, an additional regular cyclist was found to generate an international $726.47 (£485.18) per year in economic gains, when considering a ten-year time horizon. At a local population level, an additional 1,762 regular cyclists would be therefore required to break even on the construction of a new 6 km-long segregated cycleway in the UK. This behaviour change estimate was around 20% lower or higher depending on baseline cycling levels. Estimates were found to be particularly sensitive to variations to assumptions regarding age group, cycling volume and evaluation time horizon.

Conclusions Active travel policy-makers planning to invest in cycling infrastructure should consider these reproducible order-of-magnitude estimates when taking resource allocation decisions to ensure that the investment is justifiable from an economic perspective on cost-neutrality grounds. Future research should consider using the economic modelling approach illustrated in this study and replicating the analysis for other healthy behaviours. 

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