Accessibility to economic opportunities is widely regarded as an indicator of the ability of transport and land-use in cities to connect residents with its opportunities (Nakamura & Avner, 2021). Most studies measuring access to opportunities, however, focus only on travel time (El-Geneidy et al., 2016; Foth et al., 2013; Levinson, 2013; Peralta-Quiros et al., 2019; Wu et al., 2021). While travel time is an important factor that impacts travel decisions and limits accessibility, there are many other elements that affect people’s travel behavior (Cui & Levinson, 2018), including the monetary cost of travel. Recent studies have started to incorporate monetary costs in accessibility analyses (Bittencourt & Giannotti, 2021; Conway & Stewart, 2019; Herszenhut et al., 2022; Oviedo et al., 2019), however they have focused on understanding existing relationships between travel times, travel costs, and other socio-economic factors, rather than assessing the impact of a policy mechanism.

In this paper, we quantify the impacts of a carbon price induced increase in fuel prices on transit services through a study on changes in access to employment opportunities and inequality in two cities: Kinshasa, Democratic Republic of Congo, and Rio de Janeiro, Brazil, leveraging General Transit Feed Specification (GTFS) data and socio-economic data. The aim is first to understand the magnitude of the impacts of an increase in transit fares on accessibility to jobs in both cities in aggregate, by income group and location; and, second to provide information to support the development of more targeted and nuanced policies that can offset negative distributional effects of carbon pricing policies on transit accessibility. The paper addresses four main research questions:

1. How do carbon price induced fuel price increases affect employment accessibility in aggregate?

2. Are any specific neighborhoods or socio-economic groups more severely affected?

3. In the absence of complementary measures, do carbon pricing policies further exacerbate existing inequalities?

4. Do carbon pricing policies risk further excluding some groups from participating in the local labor market?

Our results demonstrate that carbon pricing decreases accessibility to job opportunities. Communities in both cities that already had high levels of access to opportunities (generally higher income areas) saw the highest losses in accessibility, cautiously pointing to carbon pricing being progressive. However, we also found that some lower income residents located relatively close to jobs could be disproportionately affected, and that low-income residents lost more in relative terms in Rio but not in Kinshasa. We also found that increasing fuel prices (up to 500%) did not change the specific population groups that were impacted significantly; however, the severity of the impact increased. The effects were partly mitigated in Rio by the existence of non-petroleum-based services (such as rail and metro services) that were less sensitive to fuel price increases relative to the bus and Bus Rapid Transit (BRT). The compact nature of Kinshasa meant that the negative impacts of fare increases were mitigated to a certain extent because of the proximity between homes and jobs, which allows for walking to be an efficient way of maintaining good accessibility in large parts of the city. Kinshasa is however more sensitive to fuel price increases due to relatively high poverty levels and only gasoline-based services operating. The level of inequality (measured by the Palma ratio, which divides the average accessibility of the wealthiest 10% by the accessibility of the poorest 40%) and how it reacts to fuel price increases, crucially depends on the travel budget threshold selected. When considering commuting related spending below 30% of per capita income, inequality decreases with fuel prices, as the poorest could not afford transit services, and by construction only the middle income and wealthiest are affected. On the other hand, beyond 30% budget thresholds, inequality increases as some of the poorest could afford transit services in the baseline but 3