Piloting the Use of Network Analysis and Decision-Making under Uncertainty in Transport Operations : Preparation and Appraisal of a Rural Roads Project in Mozambique under Changing Flood Risk and Other Deep Uncertainties

This paper presents a methodology to identify key priority areas for transport investments. The methodology uses a geospatial data-driven approach and then proposes an innovative economic analysis for project appraisal. The two main steps involve...

Full description

Bibliographic Details
Main Authors: Espinet, Xavier, Rozenberg, Julie, Rao, Kulwinder Singh, Ogita, Satoshi
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2018
Subjects:
Online Access:http://documents.worldbank.org/curated/en/787411529606457222/Piloting-the-use-of-network-analysis-and-decision-making-under-uncertainty-in-transport-operations-preparation-and-appraisal-of-a-rural-roads-project-in-Mozambique-under-changing-flood-risk-and-other-deep-uncertainties
http://hdl.handle.net/10986/29943
Description
Summary:This paper presents a methodology to identify key priority areas for transport investments. The methodology uses a geospatial data-driven approach and then proposes an innovative economic analysis for project appraisal. The two main steps involve (i) prioritization of road interventions based on a set of economic, social, and risk reduction criteria; and (ii) assessment of monetized and nonmonetized costs and benefits of road interventions under many scenarios covering the uncertainty on future risks and other factors. This methodology is used at different stages of project preparation for a rural roads lending operation to the Government of Mozambique. In the two regions of Mozambique considered, the analysis prioritizes regions along the coast when combining agriculture, fisheries, poverty, network criticality, and hazard risk criteria. With a limited budget of US$15 million per district, the results show that investing in repairing and rehabilitating culverts and bridges is the intervention that performs better under most of the scenarios.