to the 25% of households farthest from jobs. We decompose the welfare impacts of the fuel price increase by income class and spatial quartile (Figure 7).

For income classes 3 and 4, as expected, we can see that the direct impact of the fuel tax strongly depends on the location, with spatial quartile 4 being more affected than spatial quartile 1. Employment center and mode choice adjustment slightly attenuate the welfare impact of the fuel price increase. Finally, rents increase for the households living near the city center and decrease for households living further away from the city center, leading to a nearly similar impact of the fuel tax on all households of the income class. By construction, in the long run, after adjustment, utilities of all households of each income class will be equal. However, figure 7 does not show the last step (dwellings’ sizes and housing locations adjustment), because, after housing locations’ adjustment, we do not have the same households in each spatial quartile anymore. Therefore, even if, by construction, all households of each income class will have the same utility in the end, we focus here on the convergence process: there are inequalities between spatial quartiles in the short run, that can be important if the convergence process takes time.

For income classes 1 and 2, the effects at play in the convergence process are less clear. First, for income class 2, there are few differences between spatial quartiles. Second, the direct impact of the fuel tax varies with spatial quartiles, but in a non-linear way. One explanation might be that contrary to the richest households which almost all commute by polluting transportation modes, the poorest households sometimes commute by non-polluting transportation modes. Finally, by construction, after dwelling sizes and housing locations adjustment, utilities within each income class are equal and spatial inequalities disappear. However, in the medium run, we find evidence of persisting spatial inequalities, especially within income class 2.

# 7. Aggregated impact on emissions

Table 8 shows the impact of the fuel price increase on passenger-km for each transportation mode, as well as the further decomposition between the variations due to modal shift and the variations due to workplace-residence distance changes. The last column estimates the impact on emissions, using emissions per km data from WWF (2016).

Scenario 1, in which only private cars are impacted by the fuel price increase, is more efficient in terms of emissions mitigation than scenario 2, in which private cars, buses, and minibuses/taxis are impacted. This result, which implies that taxing all polluting transportation modes does not necessarily lead to higher emissions reduction, can seem counterintuitive. There are two reasons for this. The first is that there are more modal shifts in scenario 1 than in scenario 2. Indeed, in scenario 1, taxing private cars leads to a strong reduction in their modal share, to the profit of buses and minibuses/taxis, which are less fuel consuming travel modes. In scenario 2, as private cars, minibuses/taxis and buses/taxis are taxed, the modal share of private cars is actually increasing: as buses and minibuses/taxis are slow, a tax makes them really unattractive, leading to a modal shift to the profit of private cars, more expensive due to the tax but also faster than other modes. This effect depends on the relative increase in private cars transportation costs compared to the relative increase in buses and minibuses due to the tax: it depends on the share of variable monetary cost among total transportation costs, which varies with income group and location. The second reason is that the decomposition of passenger-km variations between modal shares variations and average distances variations shows that passenger-km variations are mostly driven by modal shift and not by passenger-km variations. Indeed, informal settlements, backyarding, and subsidized housing are 20