examples of DMDU applied to transport infrastructure projects even though transport planning is facing most of the same challenges and issues.

This paper builds upon these two concepts; network-level analysis and decision-making under deep uncertainty techniques. It presents the integration of these two concepts into a prioritization study (in section I) and economic analysis (in section II) of different road interventions in rural Mozambique. The methodology presented in this paper captures network-level climate adaptation co-benefits in the costbenefit analysis. Additionally, the methodology presents an approach, grounded in a robust decisionmaking framework, that properly deals with the large range of plausible future scenarios in which the investments will operate.

# **Section I: Prioritization of roads and districts**

The goal of this first section is to present a comprehensive methodology for prioritization of districts for roads interventions based on geospatial data, network analysis and risk assessment. The prioritization is based on two pillars; (i) socio-economic criticality and (ii) current and future hazard risk to the roads. Socioeconomic criticality is a composite indicator based on (1) lack of network redundancy, (2) proximity to potential agriculture clusters, (3) proximity to potential fishery clusters, (4) current agriculture production and (5) poverty rate in the district. The second pillar, hazard risk, is calculated using flood maps, for current floods and under climate change scenarios, and damage functions for the different pieces of infrastructure in the road system. The hazard risk is calculated as expected annual damage to the infrastructure using 10 return periods, in four climate scenarios (current climate, low, medium and high climate change).

The following sections describe in detail each of the two pillars and its components and present the results for two provinces in central and northern Mozambique; Zambezia and Nampula.

# **Methodology**

## **Criticality**

To help decision makers prioritize investment in Mozambique, we identified the most critical links in the road network. Here we define the criticality of a link as the loss of network performance if this link is removed from the network (Rozenberg et al. 2017). The metrics for evaluating the performance are:

- Cumulated Road user cost or RUC, defined as $-vehicle and based on road condition (IRI) and data from the HDM4 model

- Total kilometers traveled

- Total travel time, based on average speed from HDM4 based on road condition (IRI) (we assumed free flow speed in all the network – traffic congestion was not considered as traffic volumes are very low in the study area, under 2,000 AADT).

We first calculate these three variables in a baseline with the complete existing network. We then remove each of the links one by one and we recalculate the performance of the network with respect to these three indicators based on the assumption that travelers will take the secondary best route when the optimal route is disrupted (see Figure 1 for an example of the approach). The optimal and second best routes are the routes that minimize the total road user cost between the origin and destination. The additional cost, time and length of travel due to the disruption of a link are aggregated over all Origin-Destination pairs and assigned to the disrupted link to determine its criticality.

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