# 6.3 Policy impacts: Sprawl, distances travelled, emissions and welfare

In this section we want to investigate the impact that each policy package described above will have on 1/ the size of the urbanized area: can alternative policies limit the growth of the urbanized area over time? and how substantial can the decrease be compared to a scenario with public transit subsidies?, 2/ what are the consequences of these counterfactuals in terms of distances travelled for commuting purposes and CO2 emissions stemming from these? and 3/ what are the impacts on households’ welfare of these policy packages?

## 6.3.1 Consumption-related welfare impacts of alternative policies

The consumption-related welfare and income-equivalent impacts of alternative policies with respect to the baseline are the described in Table 2. Income equivalents are the monetary translation of household utility changes and are reported here because they are more easily understandable.

||**Without the public**<br>**transport subsidy**|**With a lump sum**<br>**transfer**|**With a generalized**<br>**construction**<br>**subsidy**|**With a targeted**<br>**construction**<br>**subsidy**|
|---|---|---|---|---|
|**Consumption-related**<br>**utility in 2050(%)**|-3.73%|+1.30%|-2.67%|-2.69%|
|**Equivalent income in**<br>**2014(US$)**|- $712|+$325|- $531|- $527|
|**Equivalent income in**<br>**2014(%)**|-3.83%|+1.75%|-2.85%|- 2.83%|
|**Equivalent income in**<br>**2050(US$)**|- $910|+$323|- $644|- $651|
|**Equivalent income in**<br>**2050(%)**|-3.14%|+1.11%|-2.22%|- 2.25%|

**Table 2: Impacts relative to the baseline on consumption-related welfare and translation into income equivalent (in US$) in each scenario for 2014 and 2050**

We draw two conclusions from these figures. First, while the lump sum dominates the transit subsidy as documented in the simple model presented in section 4, the welfare gains (utility) are lower than the schematic model previously presented pointed to (1.3% vs 6.1%). The reason for this difference resides in several realistic features of NEDUM-2D. The use of generalized costs instead of pure monetary costs means that an increase in transport fares does not translate into an equivalent increase of generalized costs because time costs remain the same. As time costs represent more than 70% of the generalized costs in the baseline, the increase in the monetary component only applies to the remaining 30%. So that the subsidy only represents around 20% (0.7*0.3) of total generalized transportation costs. This would place the transit subsidy in a regions which is much closer to its optimal level (see Figure 1). In addition, while in the schematic model only one transport mode is available, in the NEDUM-2D model, households have the choice of commuting by foot, using cars, transit or a combination of these modes. As a result when transit fares increase, a share of people will switch to an alternative and cheaper transport mode, say cars or walking, so that the actual cost of commuting will increase less than the transit fare increase. The existence of other transport modes can therefore mitigate the increase in public transport fares. So lump sum transfers can increase households’ welfare but less than the schematic model would have led us to believe.

The second take-away from the comparison of alternative policies is that the construction subsidy schemes perform better than the uncompensated removal of public transport subsidy (-2.7% for both 26