Climate risk assessment has traditionally relied on generic thresholds: a fixed temperature, a standard wind speed, a common flood depth, applied uniformly across sectors and geographies. That approach has been a useful starting point, but is no longer good enough on its own.

Organisations managing energy, infrastructure and other physical assets are increasingly asking for something more precise: custom climate value at risk, a measure of exposure built around the specific operational tolerances of each asset, instead of a one-size-fits-all benchmark. The reasoning is straightforward: a wind turbine, a port terminal and a data centre do not fail at the same temperature, the same wind speed or the same rainfall intensity. Treating them as if they do produces numbers that are technically defensible but practically unhelpful.

What is climate value at risk?

Climate value at risk (CVaR) is a metric that estimates the potential financial loss to an asset or portfolio caused by future climate hazards, such as floods, droughts or extreme heat. It translates physical climate risk into monetary terms, moving beyond a hazard map or a qualitative rating. 

Take, for example, a real estate investor reviewing acquisitions in Southern Europe. They ask for CVaR data to understand how future drought and wildfire risk could affect long-term asset value, which helps them compare investment options and avoid costly surprises further down the line.

CVaR is useful well beyond real estate. Investment teams and insurers use it to evaluate long-term exposure, and consultants use it to prepare CSRD-aligned reports that include physical risk metrics. Mitiga's EarthScan calculates CVaR using return periods, climate scenarios and asset-specific attributes, and it's that last input - the asset's own attributes - that makes a custom version of the metric possible.

CVaR works by estimating the percentage of direct damage to an asset based on the intensity of a given climate hazard event. Financial modelling connects hazard exposure to asset-level vulnerability through damage curves, statistically estimating potential loss across scenarios, geographies and time horizons. 

In practice, this runs through Mitiga's Multiple Futures Model, a Bayesian framework that combines global climate model projections with more than 40 years of observational data from over 100,000 weather stations, downscaled and bias-corrected down to a specific site. For each climate variable that matters to an asset, whether that's wind speed, temperature or rainfall, EarthScan works out the return periods: how often a given intensity is expected at that location, under a given climate scenario, from the present day out to 2100. Those return periods are then measured against the custom threshold set for that asset, which is what turns a statistical likelihood into an expected financial loss, rather than a hazard map with no monetary figure attached.

Why generic thresholds fall short

Standard thresholds exist for good reason. A benchmark such as 40°C heat exposure gives organisations a common reference point, and it is a reasonable starting point for a first-pass risk screen. The problem appears once that threshold is applied uniformly across industries, geographies and asset types that behave very differently under heat stress.

A steel bridge, a solar inverter and an office building each have their own operational limits, shaped by design standards, materials and maintenance regimes. A single generic threshold cannot capture that variation, which means the resulting risk score may flag exposure where none is operationally meaningful, or miss exposure that matters a great deal to a specific asset.

A closer look: the 50°C threshold example

Consider a heat-sensitive asset, such as a transformer or a piece of rotating equipment, where the manufacturer's specifications indicate that performance and lifespan degrade sharply above 50°C, rather than the generic 40°C benchmark. Applying the standard threshold to this asset understates its risk profile. Applying the 50°C threshold that actually reflects its engineering tolerance gives asset managers and underwriting teams a number they can act on: a clear signal of when and where heat exposure becomes financially material, in place of a generic warning that may or may not apply. 

That shift, from a fixed benchmark to a threshold grounded in how an asset actually behaves, is the core idea behind custom climate value at risk methodology. It replaces a generic risk score with one that is tied directly to the point at which a hazard starts to cost money.

Custom climate value at risk methodology in practice

Building a genuinely custom approach requires more than swapping one number for another. It means identifying the full set of climate variables that matter for a given asset class, establishing the thresholds at which each variable becomes operationally significant, and translating that into a financial outcome: lost production, degradation and ageing, or the frequency and severity of damage.

Two recent examples from the infrastructure space illustrate how this plays out.

Renewable energy assets

A large renewable-only energy company, with more than three decades of experience in wind and solar, faced a familiar limitation: conventional climate risk assessments tend to under-represent extreme events and stop short of translating projections into loss estimates that operations and finance teams can actually use.

To address this, Mitiga’s assessment identified more than 40 wind- and solar-specific variables, ranging from extreme gust speeds to temperature-driven degradation rates, and set risk thresholds tied to the asset's own operational tolerances. On top of this, more than 10 sector-specific impact models, projecting through to 2050, were used to quantify three dimensions of risk: power-production losses, cumulative weather ageing, and the frequency and severity of damage from acute climate extremes. A bias-correction method designed to preserve the statistical integrity of extreme events achieved 92% accuracy when validated against observed data, generating close to a million data points made available through an API.

The result was a set of insights that could feed directly into maintenance planning, site selection and capital allocation, rather than a generic risk rating sitting in a report.

Ports and critical infrastructure

The Port of Barcelona faced a different but related question: how to give a large, operationally complex site a measurable estimate of how climate events affect its day-to-day activity, so that resilience investments could be prioritised with confidence rather than guesswork.

Mitiga’s resulting assessment covered container operations and intermodal logistics at three locations, across four risk categories: precipitation, flooding, hail and wind. It looked at two time horizons, the present day (2026), to identify immediate risks and prioritise near-term adaptation measures, and the long term (2050), to inform future-proofing decisions. The output was a quantified estimate of operational and financial impact that the port could use directly in its own decision-making, rather than a general statement about climate exposure in the region.

How custom climate value at risk improves risk assessment

Both examples point to the same underlying shift. Generic thresholds tell an organisation that a hazard exists somewhere on the horizon. Custom thresholds tell it when that hazard becomes a cost, and how large that cost is likely to be. That distinction matters for three practical reasons.

• Investment decisions benefit from thresholds that reflect an asset's real engineering tolerances, since these translate more directly into due diligence, site selection and capital allocation choices, rather than a generic score that needs further interpretation before it is useful.
• Adaptation planning becomes easier to prioritise once an organisation knows which specific climate variables are business-critical for a given asset, instead of treating every hazard as equally urgent.
• Stakeholder communication, including disclosures under frameworks such as CSRD and TCFD, is more defensible when the thresholds behind a risk figure are grounded in an asset's actual operational limits, rather than a generic industry benchmark applied by default.

Climate value at risk for asset portfolios

The same logic scales beyond a single site. For organisations managing a portfolio of assets, across different technologies, geographies or asset classes, climate value at risk for asset portfolios only holds up if the thresholds behind each asset-level figure are grounded in how that specific asset behaves. Aggregating generic scores across a diverse portfolio can obscure the assets that carry the most material exposure. Aggregating custom, asset-specific thresholds gives portfolio and risk teams a clearer picture of where resilience investment will have the greatest effect, and a stronger basis for prioritising capital across a diverse set of holdings.

The takeaway

Standard climate thresholds remain a reasonable starting point for a first look at exposure; however, they are not a substitute for understanding how a specific asset, in a specific location, actually responds to heat, wind, precipitation or flooding. Custom climate value at risk closes this gap, turning a generic risk score into a figure that reflects an asset's real operational tolerances, and that a business can act on with confidence.

Mitiga’s EarthScan platform was built around this principle: physical climate risk data at individual asset level, developed with the technology-specific detail that renewable energy, ports and other critical infrastructure assets require. 

If you would like to see how a custom approach applies to your own portfolio, get in touch with our team.

‍