# **Section II: Cost-Benefit Analysis under deep uncertainties**

In this second section, we present a methodology to calculate the costs and benefits of road interventions under deep uncertainty, using a robust decision-making framework (Lempert et al. 2006). The methodology is tested on five different investment scenarios in each of the districts selected above. The investment scenarios were defined based on discussions with local stakeholders during two workshops held in January 2017 in Quelimane and Nampula. Each investment scenario was constrained to a total of approximately US$15 million. Each investment was a combination of five engineering solutions that would lead to four kinds of cost savings: reduction of flood risk for the users, repair and rehabilitation costs, maintenance expenditure and daily road user costs. The results are presented in detail for the district of Murrumbala in Zambezia to illustrate the method. A summary of the results for the other 5 districts is presented at the end of this section.

# **Methodology**

The road interventions that are being analyzed in this study aim to improve farmers’ access to markets and adapt the road network to climate change. The main impact of climate change on the roads is through riverine flood events combined with sea level rise. For that reason, the cost-benefit analysis includes flood risk reduction as a benefit in the selection of interventions. To assess this benefit, we first estimate flood risk in a baseline scenario with no intervention.

## **Definition of a baseline**

In the baseline, it is assumed that no intervention is made, and the existing infrastructure is maintained with a low standard of routine and periodic maintenance – allowing IRI up to 16 for gravel and 18 for earth. That translates to yearly routine maintenance and periodic maintenance every 8 years (for earth roads) – these are average values based on interviews with local authorities. The existing bridges and culverts, including the ones destroyed by floods, are not rehabilitated nor reconstructed.

Traffic is expected to grow because of economic growth, at a rate of 3% per year. However, this rate is highly uncertain, and we vary this assumption later. The correlation between IRI and road user cost can be found in the annex and was obtained through the HDM4 calibrated for Mozambique (Iimi 2016). We have assumed that all traffic falls under the category of light trucks (based on discussion with locals and visual assessment).

The road network is exposed and vulnerable to floods, and we can estimate flood risk as the expected annual losses caused by floods. The probability distribution of flood events varies with climate change; thus, flood risk has to be estimated in multiple climate change scenarios.

Expected annual losses (EAL) have two components: the cost of repairing or rebuilding the damaged infrastructure ( _D_ ) and the increased road user cost that users in a district must pay when they are forced to make a detour due to flood disruptions ( _U_ ). In the results section the two components are shown in separate graphs, the first one labeled as ‘avoided damage’ and the second one as ‘reduced flood risk’.

To calculate these user losses, it is assumed that the roads and bridges are impassable when the water level on the road (����� ����∗��) is higher than 0.5 meter. Each drainage structure has a certain level of water that it can drain before water starts to accumulate on the surface of the road. Here, multiple links can be disrupted at the same time (unlike in the criticality analysis) since we simulate flood events. This means that the secondary best route identified previously may be disrupted as well. In this case user losses are associated with the use of a third best route, or fourth best, etc. In some cases, no route is available between 13