Multi-hazard environmental risk assessment for electricity substations: integrating climate projections with atmospheric corrosion modelling *

Abstract Electricity substations face accelerating environmental stress from climate change, atmospheric corrosion, seismic hazard, and ageing infrastructure, yet quantitative multi-hazard risk frameworks at substation resolution remain scarce. We present an open, replicable framework that integrates CMIP6 climate projections, ISO 9223:2012 atmospheric corrosion classification, and a five-state Markov degradation model to produce location-specific expected time to critical-condition (ETTC) estimates for 159 720 substations across 23 OECD countries plus Greenland. The framework combines (i) a four-sub-index climate physical risk composite (R1) capturing projected changes in heat extremes, extreme precipitation, ice-storm frequency, and seismic peak ground acceleration (PGA); (ii) an atmospheric corrosion acceleration factor ( κ corrosion ) derived from coastal-distance salt aerosol modelling and Gaussian-plume industrial SO 2 exposure mapped to ISO 9223 categories C1–C5; and (iii) a Bayesian Markov transition model anchored to CIGRÉ Technical Brochure 761 and IEEE Standard C57.91–2018 informative priors, updated by national DSO/TSO inspection records where openly published. R1 is computed under two complementary climate scenarios—SSP2-4.5 (production central reference) and SSP5-8.5 (academic stress test)—enabling explicit climate-scenario sensitivity analysis. Monte Carlo simulation with 10 000 iterations per substation, using a Gaussian copula across 20 normalised metrics, yields ETTC point estimates and bootstrap confidence intervals. Validation against 1220 documented substation failures from 2018 to 2023 (Pearson ρ = 0.72 between predicted and observed risk rank density; AUC = 0.78) supports the framework’s discriminatory power. Sensitivity analysis identifies atmospheric corrosion as the leading risk acceleration pathway under current modelling resolution—a finding robust to climate-scenario choice—with explicit acknowledgment that flood sub-index resolution lags corrosion modelling quality and warrants future improvement via global flood hazard map integration. All input data sources are public and verifiable through country-specific Data and Download pages; the framework is released under CC BY-SA 4.0 as part of the Systemic System Infrastructure Index, operated as a non-profit public-interest infrastructure resilience initiative.