Private cars are inefficient in terms of land allocation. Car-free neighbourhoods offer a solution to this inefficiency by enforcing less car-centric urban design, expensive parking fees, and prioritising more sustainable and space-efficient modes of transportation. Carsharing services emerge as an alternative to private car ownership by providing vehicles available for short-term use. Notably, not much is known regarding carsharing services as an alternative mobility solution in car-free neighbourhoods. The present thesis investigates pricing strategies that could be implemented for business-to-consumer carsharing services in car-free residential neighbourhoods to align with the diverse needs and preferences of potential users. To this end, this thesis employs a case study of the car-free neighbourhood Merwede. Specifically, a cost-benefit analysis is conducted to estimate the potential value of carsharing in a car-free context. Emphasis is put on policies that may stimulate carsharing adoption and on determining equitable service pricing strategies.  To predict the modal split of future Merwede residents, datasets – containing mobility information for Utrecht residents clustered by income group – are used. To determine the influence of a service price change on carsharing demand, per-income-group price elasticities are estimated. This resulted in the following values for low-, middle-low-, middle-high-, and high-income groups, respectively: -0.8, -0.5, 0.4, and -0.1. Three stakeholders are considered in the analysis: carsharing service operators, users, and the Municipality of Utrecht. Results indicate that subsidies for lower-income residents in the form of a trip credit incentive (of 50 or 100 euros per month per user) yield positive total net present values and higher carsharing adoption rates. This thesis concludes by stating that while the generalisability of these findings may be limited, the CBA model synthesised for Merwede provides a case study from which future car-free neighbourhoods – aiming to implement carsharing – may learn.