SearcharxivSearch

arXiv · 2605.23053

A Comparative Multi-Hazard Risk Assessment of the US High-Voltage Transmission Network

Abstract

Modern economies depend critically on high-voltage power transmission networks. Yet this infrastructure is routinely disrupted by natural hazards ranging from earthquakes and floods to tornadoes and geomagnetic storms. Risk assessments have historically addressed hazards in isolation, leaving no common basis for comparing economic impacts across the full hazard portfolio. This study addresses this gap by developing an integrated framework linking hazard characterization, fragility modeling, and macroeconomic impact propagation. The framework is applied consistently across nine primary hazards and one compound freezing rain and wind gust hazard. Using national hazard datasets and a US high-voltage transmission network of over 13,000 line segments and 10,000 substations, we derive failure probabilities, expected damage, affected population, and downstream economic output losses. Among individual hazards, tropical cyclone wind produces the largest expected daily damage at $137 M/day, followed by lightning at $87 M/day, earthquake at $47 M/day, flood at $46 M/day, tornado at $42 M/day, and landslide at $34 M/day. Downstream economic output losses are largest for tornado at $4.93 B/day, followed by flood at $3.59 B/day and earthquake at $3.02 B/day. A 250-year geomagnetic storm produces $2.07 B/day, placing space weather within the range of major terrestrial hazards. The compound freezing rain and wind gust scenario produces the largest stress-test disruption, affecting 237.4 M people and yielding a modeled downstream output loss of $85.16 B/day. These results should be interpreted as first-order bounding estimates, with the compound scenario representing an upper-bound stress test. Overall, the framework establishes a consistent baseline for prioritizing investments in transmission network resilience.

Explore related subjects

Keep this discovery

BibTeXRIS

D. Bor, E. J. Oughton, R. S. Weigel, R. Yang, T. Clower, A. Newman, A. R. Valle. 2026-05-21. A Comparative Multi-Hazard Risk Assessment of the US High-Voltage Transmission Network. https://arxiv.org/abs/2605.23053

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM