2017年-世界发展银行全球_Improving_the_Resilience_of_Perus_Road_Network_to_Climate_Events_34页_3mb

Summary of "Improving the Resilience of Peru's Road Network to Climate Events"

Core Content

This working paper proposes a methodology to enhance the resilience of Peru's road network against climate-related disruptions. The study focuses on identifying critical links in the network and determining the most robust interventions to reduce the economic and logistical impacts of natural hazards.

Main Objectives

• To prioritize interventions in Peru's road network that reduce vulnerability to climate events.
• To develop a system-wide approach for assessing criticality and exposure of road links.
• To apply a robust decision-making (RDM) framework to evaluate interventions under deep uncertainty.

Key Findings

• Critical Links: The most critical roads are identified based on their impact on network performance, particularly in terms of road user cost, kilometers driven, and total daily cost. These include:

  • Carretera Central near Lima
  • Piura in the north
  • A southern part of the Pan Americana road

• Exposure to Natural Hazards: These critical links are exposed to floods, landslides, and storm surges. The study uses hazard layers and flood scenarios from the GLOFRIS model to assess exposure, including:

  • Flood scenarios for different return periods (RP5 to RP1000)
  • Coastal flood data
  • Landslide susceptibility maps

• Vulnerability Assessment: Vulnerability is defined as the economic consequences of a hazard event, including:

  • User costs due to rerouting
  • Government or concessionary costs for reconstruction or rehabilitation
  • Indirect costs such as missed work hours and perishable goods loss (not quantified in this study)

• Disruption Scenarios:

  • Flood Duration: Based on historical data and expert validation, curves were constructed to estimate flood duration based on depth.
  • Structural Damages: Assumptions were made about the required interventions based on water levels:
    • Full reconstruction if water remains for more than 30 days
    • Rehabilitation if water stays 10–30 days
    • Cleaning and minor maintenance if water remains less than 10 days
  • Traffic Rerouting: Three scenarios were considered for traffic disruption due to floods:
    • Optimistic: 30% traffic rerouted at 15–25 cm water depth; 100% at >60 cm
    • Pessimistic: 100% traffic rerouted at 30 cm water depth
    • Intermediate: 80% traffic rerouted during reconstruction

• Interventions:

  • Proactive Interventions:
    • Increased maintenance frequency (doubling periodic maintenance)
    • Road upgrades (e.g., tunnels, elevation)
    • Adding redundancy by upgrading alternative routes
  • Reactive Interventions:
    • Rebuilding or rehabilitating roads after a disaster

• Robust Decision-Making (RDM):

  • A framework is used to evaluate interventions under multiple scenarios and uncertainties.
  • The study generates 500 plausible futures based on different combinations of uncertainties, including:
    • Intensity, frequency, and duration of climate events
    • Structural impact of water levels
    • Traffic rerouting amounts
    • Reconstruction time and cost

• Performance Metrics:

  • A metric called $P$ is used to evaluate the profitability of interventions over a 30-year horizon:
    $$
    P = \sum_{t=1}^{30} \left(\frac{-costs(t) + AAAL(t)}{(1 + d)^t}\right)
    $$
    Where $AAAL(t)$ is the avoided average annual losses with an intervention compared to doing nothing.

Methodology Overview

• A GIS-based network model is used to identify critical links.
• Indicative corridors are defined as the least-cost routes between origin-destination (OD) pairs.
• Traffic-weighted criticality is calculated using a gravity model:
  $$
  w = \frac{T_O * T_D}{km_{OD}^2}
  $$
  Where $T_O$ and $T_D$ are the total traffic in and out of origin and destination nodes, and $km_{OD}$ is the distance between them.
• Disruption costs are calculated for different scenarios and used to rank critical links.
• RDM is applied to select the most robust interventions that perform well across a range of plausible futures.

Conclusion

The study emphasizes the need for a system-wide, robust decision-making approach to prioritize interventions in Peru's road network. It highlights the importance of proactive measures to reduce vulnerability to climate events and provides a framework for evaluating these interventions under uncertainty. The findings support policy makers in making informed decisions that enhance the resilience of Peru's transport infrastructure.