the transport index, coupled to the elimination of subsidies by 2024 and an oil price increase of 20%, the fall in emissions amounts to around 26 and 32 percent respectively. Compositionally, the share of emissions accounted for by the various types of public transport increases although not by very large magnitudes.

# **7.3 Linking CO2 to PM10 emissions and DALYs**

There is limited information on the exact relationship between CO2 and concentration of particulates or PM10 emissions and hence on the appropriate conversions rates to be applied.<sup>8</sup> What evidence is available suggests that changes in CO2 emissions are associated with roughly proportional changes in PM10 emissions. Using data from the World Bank (Table 2.2; World Bank’s Cairo Pollution study), we now project changes in the concentration of particulates (PM10) in Cairo for the scenarios used above. Our main assumption is that there is a linear relationship in the increase of CO2 and PM10 emissions. However, there are a few additional assumptions that we make in projecting PM10 emissions. First, we assume that an increase in PM10 particulates will be associated with an increase in the overall concentration of particulates in the Greater Cairo region. Second, based on the World Bank Greater Cairo Pollution Study, we assume that transport pollution represents roughly 15 percent of total pollution in the city. Hence, a doubling of PM10 due to transport would be equivalent to 15 percent of the change in overall PM10 emissions. It should be emphasized that this exercise is static as we hold everything else _ceteris paribus_ , i.e. we do not consider changes in emissions/concentration due to industrial pollution and other factors. It is quite feasible that the policy changes we are simulating might also have an impact on those sectors. As such, we are probably underestimating the overall impact of fuel pricing changes.

The results of this exercise are presented in _Table 3_ . Given the assumptions, changes in PM10 concentrations in various scenarios correspond to changes in CO2 emissions from _Table 1_ above. (Clearly, if changes in CO2 and PM10 emissions were not proportional, the estimates would have to be adjusted.) For the four scenarios to 2024, decreases in PM10 concentration range between 8 - 25 percent compared with the base case. The drop in PM10 concentration in the shorter-term scenario (up to 2020) ranges between 15 and 20 percent. Adding the changes in the transport index to simulate an increase in public transport, we can observe at the outer limit that there is a decrease of over 30 percent in the concentration measure; a very substantial decline indeed.

After projecting PM10 emissions/concentration, we again use information from the World Bank Great Cairo Pollution study (Table 5.1: Total DALYs lost to pollution) and the ratios between PM10 concentrations and DALYs lost to project the total DALYs lost to _transport_ pollution in the six scenarios. The results of this exercise are reported in _Table 4_ where the central calculation is of _cumulative_ savings when comparing a particular scenario with the baseline case over the reference period. The table shows that a gradual 50% fall in subsidies by 2024 is associated with

> 8 See Haikun Wang, Lixin Fu and Jun Bi, ‘CO2 and pollutant emissions from passenger cars in China’, _Energy Policy,_ 39, 5, May, 2011, pp3005-3011 and M. Madireddy et al, ‘Micro-Simulation of a Traffic Fleet to Predict the Impact of Traffic Management on Exhaust Emissions’, Ghent, mimeo, 2010

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