European ESG regulation no longer simply asks companies to report their emissions. Today, it raises a deeper question: are critical infrastructures truly ready to withstand the impact of extreme climate events?

CSRD, EU Taxonomy and the CER Directive converge around a single principle: resilience must be demonstrated with data, not declared in words.

Companies that align compliance with the operational management of risk protect capital, avoid asset devaluation and optimize investment plans.

When uncertainty is the rule, resilience must be a systemic choice. A forward-looking strategy built on scientific foundations.

In this article, we will explore:

• What is ESG regulation and why does it concern climate resilience?
• Which regulations govern climate risk and resilience?
• CER Directive and the resilience of critical entities
• CSRD and ESRS: how to report climate risks
• EU Taxonomy, climate adaptation and sustainable investments
• European Climate Law and Fit for 55
• Transition, adaptation and resilience plans
• Climate Risk Assessment: how to assess climate risk
• How Airis supports climate compliance

What is ESG regulation and why does it concern climate resilience?

The ESG (Environmental, Social, Governance) regulatory framework has progressively expanded its scope in recent years. While the initial focus was on emissions disclosure, the regulatory horizon later shifted towards assessing the physical resilience of infrastructure.

Extreme weather and climate events introduce risk variables that have a tangible impact on companies’ financial and operational stability.

Climate compliance is no longer limited to producing documentation. Today, ESG regulation requires analytical evidence of the ability to prevent, absorb and overcome adverse events that threaten critical assets and networks.

From meeting requirements to risk management

Formal compliance required by ESG regulation shows clear limitations in the face of the frequency of climate change. The shift towards proactive risk management involves adopting data-driven decisions.

It is necessary to integrate scientific models and long-term climate scenarios directly into operational priorities. Climate risk management requires a review of predictive maintenance routines and design criteria.

Business decisions are therefore guided by precise metrics that calculate risk exposure and the structural vulnerability of infrastructure.

Which regulations govern climate risk and resilience?

The European regulatory framework consists of a set of interconnected directives and regulations. The objective is to direct investments towards sustainable activities while ensuring the resilience of the productive and infrastructure system.

Cross-cutting regulations and infrastructure regulations

It is appropriate to distinguish between cross-cutting regulations and sector-specific measures dedicated to infrastructure operators.

Cross-cutting regulations set standards for reporting, compliance and financial transparency for businesses in general.

By contrast, sector-specific regulations impose stringent engineering requirements. The focus is on structural resilience, network accessibility and the continuity of services provided by energy, telecommunications and transport network operators.

CER Directive and the resilience of critical entities

The evolution of the regulatory framework for critical entities shifts the focus towards active adaptation.

Specifically, the CER Directive introduces precise risk assessment obligations for critical entities, with the aim of protecting the operational continuity of essential infrastructure.

To ensure the construction of a resilient world, the entities concerned must conduct periodic assessments that explicitly include the impact of extreme natural events.

Technical measures must be adopted to prevent service disruptions before they occur, in line with the requirements of the directive.

What changes for DSOs and TSOs

For distribution system operators (DSOs) and transmission system operators (TSOs), compliance with the highest resilience standards involves a targeted analysis of the vulnerability of strategic nodes.

For example, it is necessary to assess the impact of a wildfire or strong wind gusts on an overhead transmission line, or the consequences of a sudden flood inundating an electrical substation.

Interconnection is also vital for the Telco sector. A prolonged heatwave can drastically reduce data center cooling capacity, generating a cascading effect across the entire civil and industrial system.

What changes for road and rail operators

Transport infrastructure operators have to deal with the impact of climate phenomena on complex linear networks. Exposure does not concern a single point, but kilometres of assets.

Landslides or extreme rainfall can suddenly block access to a road network, while an anomalous heatwave can deform railway tracks.

Integrating prevention plans helps mitigate downtime and protect travelling communities.

CSRD and ESRS: how to report climate risks

Within ESG regulation, the Corporate Sustainability Reporting Directive (CSRD) establishes rigorous reporting standards (ESRS) for European companies. Specifically, ESRS E1 requires an in-depth climate analysis.

The directive introduces the principle of double materiality. Companies must report both their impact on the environment (impact materiality) and the ways in which climate risks affect the company (financial materiality).

For companies, this translates into a twofold advantage:

• Optimizing CAPEX plans, reducing expected economic losses (Expected Loss) and protecting asset value over time.
• Mitigating network disruptions by directing predictive maintenance towards assets with greater physical vulnerability.

From ESG data to decision-making information

Primary data relating to exposure to physical risks must be transformed into decision-making metrics. Simply locating a facility on a map does not provide a real strategic advantage.

It is essential to translate weather uncertainty into estimates of probability, intensity and economic impact on operations.

The value of a model is not measured by its ability to eliminate uncertainty, but by its ability to make it readable and manageable.

Discover how Airis maps climate risk across infrastructure networks and transforms complex data into operational decisions.

EU Taxonomy, climate adaptation and sustainable investments

The EU Taxonomy defines a common language for environmentally sustainable economic activities, aligning with the broader objectives of ESG regulation.

To be compliant, investments must meet rigorous Technical Screening Criteria and the DNSH (Do No Significant Harm) principle.

Within climate change adaptation, an infrastructure project is considered compliant only if accompanied by a scientific assessment of current and future physical risks.

European Climate Law and Fit for 55

A fundamental pillar of the European Green Deal, the European Climate Law made the goal of climate neutrality by 2050 legally binding.

The Fit for 55 package also sets a 55% reduction in greenhouse gas emissions by 2030.

These parameters, fundamental within ESG regulation, push sectors such as energy and transport to rethink their network architectures. The transition towards electrified and decarbonized systems requires, in parallel, a systemic strengthening of the mechanical robustness of existing infrastructure.

Transition, adaptation and resilience plans

Systemic resilience planning requires the structuring of three complementary analytical tools:

• Climate change mitigation transition plan: defines decarbonization strategies to reduce emissions, responding to ESG regulatory obligations.
• Adaptation plan: maps physical risks through geospatial data, identifying actions to reduce asset vulnerability.
• Resilience plan: establishes operational and business continuity procedures to ensure network availability during and after an extreme event.

From climate plans to operational priorities

The effectiveness of these plans lies in their immediate applicability in the field. Strategic guidelines must feed into predictive maintenance programmes.

Every resilient decision comes from the ability to connect what could happen with what can be done today.

This translates into earlier intervention thresholds and the strengthening of assets located in areas of high climate risk.

Climate Risk Assessment: how to assess climate risk

Climate Risk Assessment is the structured methodology used to quantify climate risk. It is based on the intersection of four essential analytical vectors:

• Hazard: probability of occurrence and expected intensity of a weather-climate event (e.g. floods, heatwaves).
• Exposure: presence and density of infrastructure or strategic nodes in the affected area.
• Vulnerability: the physical propensity of an asset to sustain damage, derived from engineering parameters and construction age.
• Impact: the extent of economic consequences (direct damage) and losses resulting from service interruptions.

Assessing point assets and linear infrastructure

The assessment methodology changes radically depending on the architecture of the asset being analysed:

• Point assets: include primary substations, electrical substations or data centers. The analysis examines specific geographical coordinates and defined perimeter characteristics.
• Linear infrastructure: includes power lines, railway networks and roads. Risk estimation requires very high-resolution spatial segmentation to assess heterogeneous threats distributed over hundreds of kilometres.

Transforming the risk map into decisions

The intersection of geospatial data generates advanced visual and numerical outputs. These outputs provide decision-makers with a clear view of maximum exposures.

When the climate changes faster than infrastructure, the difference is not made by the ability to react to the event, but by the ability to understand its possible consequences in advance.

The analysis makes it possible to optimize insurance policies and demonstrate the robustness of corporate planning to auditors.

How Airis supports climate compliance

In the context of growing environmental pressure driven by ESG regulation, the Airis platform positions itself as the essential analytical engine for infrastructure operators.

Combining Earth observation data, artificial intelligence and validated scientific models, it does not simply produce theoretical reports. The platform calculates the probability, intensity and physical and economic impacts of climate events on point and linear networks with extreme precision.

This technology enables organizations to:

• Meet CSRD materiality requirements and document alignment with the EU Taxonomy DNSH principle.
• Identify strategic nodes and vulnerable sections in advance, ensuring business continuity and infrastructure robustness.
• Prioritize maintenance interventions and rationalize CAPEX budgets based on financial calculations and expected damage.

Book a personalized Airis demo and discover how to transform climate risk into operational decisions, protecting assets and communities.

FAQ

How are ESG criteria integrated with physical risk analysis?

The ESRS standards under CSRD regulation require the application of double materiality. It is necessary to report the economic impact that extreme weather-climate events (financial materiality) have on business assets and operations.

How does the EU Taxonomy affect infrastructure resilience?

To be aligned, an investment must comply with the DNSH principle. This requires a rigorous scientific assessment of current and future physical risks to ensure structural and operational resilience over time.

Why is it necessary to distinguish between point assets and linear infrastructure?

Risk estimation for linear networks (roads, railways, power lines) requires high-resolution geospatial analysis capable of processing diverse threats over long distances, unlike the single map point typical of a point asset.

How does geospatial data facilitate climate compliance?

The processing of geospatial and satellite data through artificial intelligence makes it possible to calculate probabilities and expected impacts. These objective metrics are essential for justifying CAPEX expenditure, demonstrating compliance with the DNSH principle and planning climate adaptation.

The transposition of Legislative Decree 134/2024 requires a paradigm shift for managers of essential physical infrastructure.

Carrying out the risk assessment under the CER Directive means adopting a quantitative approach to calculate the impact of extreme weather and climate events, structuring the analysis around three interdependent pillars: Hazard, Exposure and Vulnerability.

Designated critical entities are required to complete this analysis within 9 months of designation, preparing methodologies aligned with the EU Guidelines scheduled for 11 September 2025. To protect infrastructure investments and ensure service continuity, it is necessary to move beyond the exclusive use of historical data and adopt advanced geospatial methodologies.

In this article, we will explore:

• The scientific breakdown of climate risk for compliance with Legislative Decree 134/2024
• The limits of historical data in modelling forward-looking scenarios
• The management of linear infrastructure and the quantification of cascading effects
• The operational integration between physical resilience and NIS 2 requirements
• The automation of weather and climate risk analysis with the Airis platform

Scientific methodology for risk assessment on physical assets

Measuring climate risk requires a structured geospatial analysis to accurately map the potential impact of extreme events on assets. To meet the regulatory requirements of Legislative Decree 134/2024, infrastructure companies need to implement a framework based on predictive models.

The regulation establishes the definition of an initial baseline and requires a four-year update of the risk profile, a mandatory step to monitor the evolution of environmental scenarios over the long term.

To translate these requirements into operational parameters, the scientific breakdown of systemic risk is adopted.

This approach is expressed as the product of three distinct factors:

Risk = Hazard × Exposure × Vulnerability

The analysis requires the detailed characterization of each variable:

• Hazard → represents the probability of occurrence and the physical intensity of a weather or climate event in a defined geographic area (e.g. water depth for a flood or maximum temperature during a heatwave).
• Exposure → corresponds to the presence of assets, strategic nodes or operating personnel located in the areas potentially impacted by the hazard event.
• Vulnerability → measures the intrinsic susceptibility of the infrastructure to structural damage or operational interruptions, considering construction characteristics and existing redundancies.

This assessment matrix makes it possible to determine the robustness of networks and plan targeted adaptation measures.

Every resilient decision comes from the ability to connect what could happen to what can be done today.

Beyond historical data: the importance of predictive models and forward-looking scenarios

Risk assessment based exclusively on historical records presents structural limits. The acceleration of climate change has modified return periods and the frequency of extreme weather and climate events, making past statistics ineffective for estimating future stresses.

To ensure correct alignment with compliance obligations, organizations must integrate predictive models based on geospatial data. It is necessary to develop forward-looking scenarios projected to 2030 and 2050, in line with official climate projections.

This quantitative approach makes it possible to plan capital expenditure (CAPEX) for infrastructure consolidation and optimize operating expenses (OPEX) through preventive maintenance measures.

Methodological comparison between Risk Assessment approaches

The adoption of advanced projections transforms regulatory compliance from a bureaucratic burden into a strategic lever for building a resilient world.

To explore the data-based predictive approach in greater depth, it is useful to assess how the Airis platform maps climate risk directly onto the physical footprint of networks.

Mapping linear infrastructure and cascading effects

The management of linear infrastructure, such as transmission networks (TSOs), distribution networks (DSOs), railway lines and highways, presents greater complexity than the assessment of point assets. A network requires continuous monitoring along the entire geographic route to ensure accessibility and service continuity.

The analysis must include the quantification of cascading effects, namely the phenomena whereby damage to a single node causes the interruption of interconnected essential services across an entire territory.

A frequent operational example concerns the combination of multiple hazards. A prolonged heatwave deforms the tracks of a railway network and, at the same time, reduces the cooling capacity of telecommunication data centers located along the corridor. In parallel, a sudden flood risks flooding an electrical substation, generating a simultaneous disruption.

The continuous mapping of strategic nodes along linear infrastructure makes it possible to identify the highest-risk sections and prevent the domino effect between interdependent sectors.

When the climate changes faster than infrastructure, the difference is not made by the ability to react to the event, but by the ability to understand its possible consequences in advance.

Integrating risk analysis between the CER Directive and NIS 2

For Security Managers, climate risk and cyber risk often remain two separate analysis processes, managed by different teams and methodologies. This separation generates duplication of activities and slows incident response.

Legislative Decree 134/2024 (CER) and the NIS 2 Directive regulate two components of the same infrastructure:

• Physical resilience
• Digital resilience

Damage to a facility can propagate to the systems that control it remotely. Therefore, data availability and command continuity depend on the physical integrity of the asset to which they are connected.

The operational solution consists of applying to physical assets the same indicators already used for cyber risk assessment. A single analysis framework measures any threat, climate-related or cyber-related, with two common parameters.

Common indicators for a unified assessment framework

• Expected Loss → the expected economic damage, calculated with the same financial metric for both weather and climate events and cyber incidents.
• Business Continuity → the service interruption time, which makes a digital blackout and a physical failure comparable on the same scale.

With these two indicators, security and risk management teams build a single risk register instead of two separate reports. One assessment feeds both the obligations of the CER Directive and those of NIS 2, eliminating duplicate audits and accelerating incident response times.

A real example

A flood inundates the control center of an electricity distribution operator. The water damages the electrical switchboards and, at the same time, disables the SCADA system that manages the network remotely: technicians lose both the physical facility and digital visibility over it.

Treating the two damages as separate events leads to two disconnected impact estimates. A joint analysis, on the other hand, measures in a single step the physical damage and the resulting loss of digital control, returning the real severity of the incident.

Optimizing compliance with Airis by Eoliann

Airis combines satellite geospatial data, artificial intelligence and physical models to calculate the probability, intensity and economic impact of floods, wildfires, extreme wind and landslides.

Eoliann’s proprietary platform processes both point assets and linear infrastructure homogeneously at European scale, returning high-resolution maps and forward-looking climate projections up to 2050.

Benefits for decision-makers:

• Maintenance prioritization → targeted interventions on the highest-risk sections and facilities.
• CAPEX protection → planning of infrastructure investments based on verified climate scenarios.
• Scientific quantification of expected damage → calculation of Expected Loss on a geospatial basis and scientific models.

Airis simplifies risk assessment for the CER Directive because it automatically generates the hazard, exposure and vulnerability metrics required by Legislative Decree 134/2024 to quantify climate risk on critical assets.

Plan your climate resilience strategies with scientific data and reduce the exposure of your physical assets.

FAQ

What is the correct method to assess climate risk according to the CER Directive?

The recommended scientific method is based on the quantitative analysis of three key factors: hazard, meaning the probability and intensity of extreme events in the area; exposure, meaning the presence of infrastructure in that area; and vulnerability, meaning the structural capacity of the asset to withstand the phenomenon without interrupting the essential service.

Why is historical data on weather and climate events no longer sufficient?

Climate change is rapidly modifying the frequency and intensity of extreme natural phenomena. Relying only on the historical past does not make it possible to predict unprecedented future scenarios. To ensure long-term resilience, companies need to integrate geospatial predictive models and forward-looking scenarios projected up to 2050.

What are cascading effects in infrastructure risk assessment?

Cascading effects occur when the interruption of an essential service negatively impacts other connected infrastructure.

How do the obligations of the CER Directive integrate with those of NIS 2?

The obligations are integrated by applying the same framework: every physical or climate threat to a critical asset is also assessed for its impact on connected digital systems, using the same indicators.

Climate risks represent the possibility that current or future climate phenomena may produce negative consequences for people, activities, assets, infrastructure and territories.

The level of risk does not depend only on the intensity of an event, but on the interaction between hazard, exposure and vulnerability.

Understanding these factors is essential to assess impacts, define intervention priorities and turn climate data into operational decisions.

In this article, we will explore:

• What climate risks are and what factors they depend on
• What the main categories of climate risk are
• How climate risks impact businesses and production facilities
• Climate risks for infrastructure and linear networks
• Climate risks in Italy and Europe
• From assessment to operational management with climate intelligence and analytics

What climate risks are and what factors they depend on

Climate risk is the probability that a climate phenomenon will produce negative impacts.

Unlike a climate hazard, which indicates only the possibility that an event may occur, risk measures the effects that the same event can generate in a specific context.

Its level is determined by the combination of four elements:

• Hazard → probability and intensity with which an event may occur
• Exposure → presence of elements that may be affected
• Vulnerability → capacity to withstand or suffer damage
• Potential consequences → effects that the event may generate

The same event can generate different risks

A flood represents a climate hazard. Risk, on the other hand, depends on what may be affected, the level of vulnerability of the assets present and the consequences that potential damage may produce.

For this reason, the same event can have very different impacts. A flood affecting critical infrastructure may interrupt essential services, while the same flood in a less exposed area may cause limited consequences.

Risk does not depend only on the climate event, but also on the characteristics of the territory and the elements exposed.

What are the main categories of climate risk

Acute and chronic physical risks

Physical risks derive from the direct effects of climate change on people, infrastructure and economic activities.

They are divided into:

• Acute risks, associated with sudden events such as floods, landslides, wildfires, intense precipitation, strong wind gusts and falling trees
• Chronic risks, linked to progressive changes such as drought, heatwaves, sea-level rise and long-term variations in climate conditions

These phenomena can cause damage to assets, interrupt production, compromise critical infrastructure and reduce operational continuity.

Transition risks

Transition risks are linked to the shift towards a low-carbon economy and should not be confused with physical risks.

They may arise from:

• Regulatory developments
• Technological innovation
• Market changes
• New investor expectations
• Reputational factors

For many organisations, these elements can influence investments, competitiveness and development strategies as much as the physical impacts of climate events.

Liability risks

These are legal and regulatory risks arising from a failure or inadequate consideration of climate impacts in business decisions, risk management and communication with stakeholders.

They arise when an organisation cannot demonstrate that it has correctly identified, assessed or managed climate risks, or when the information communicated is incomplete, misleading or not aligned with the available evidence.

Examples of climate risk for infrastructure and networks

How climate risks impact businesses and production facilities

The impacts of climate risks are not limited to visible damage to assets. An extreme event can compromise the organisation’s operational continuity and produce economic consequences that extend far beyond the site directly affected.

For this reason, it is important to distinguish between:

• Physical damage, affecting buildings, plants, machinery, warehouses and infrastructure
• Operational and economic impact, concerning the company’s ability to continue producing, provide services and meet delivery times

Even in the absence of significant structural damage, an organisation may experience:

• Production interruptions
• Power shortages
• Difficulties accessing facilities
• Working conditions that do not allow operations to be carried out safely

These effects are compounded by indirect repercussions on supply chains, logistics, suppliers and demand, which can amplify the initial impacts.

A concrete example

A landslide interrupts the only access road to a manufacturing plant.

The facility suffers no damage, but employees cannot reach the site and raw materials are not delivered.

Production stops, orders are delayed and the company incurs additional costs despite the absence of direct damage to its assets.

Climate risks for infrastructure and linear networks

For those managing distributed networks and infrastructure, climate risk does not depend only on the exposure of a single asset.

Service continuity is influenced by the interaction between sections, nodes and strategic components, which may respond differently to the same climate event.

A power line, a transmission network, road or railway infrastructure and a telecommunications network cross territories with very different characteristics.

Flooding, landslides, wildfires, extreme heat, erosion or the interruption of access routes can compromise individual elements of the network and generate effects that propagate far beyond the point where the event occurred.

The resilience of a network is built by understanding how risk is distributed across its connections, rather than looking at individual assets in isolation.

For this reason, the analysis must compare the level of risk across different network segments, identify the most critical nodes and define intervention priorities.

Climate risks in Italy and Europe

Climate risks are not uniform across Italy. Intensity and exposure vary depending on the morphology of the territory, local climate conditions, the level of urbanisation and the presence of infrastructure.

The main phenomena affecting Italy include:

• Floods
• Landslides
• Drought
• Wildfires
• Heatwaves
• Coastal erosion

Understanding where risk is concentrated is what makes it possible to turn climate information into an operational decision.

The same principle applies at European scale. Mediterranean, Alpine, coastal and continental areas have different risk profiles and require specific assessments.

The first European Climate Risk Assessment by the European Environment Agency identifies 36 major climate risks for Europe, including the 7 that directly affect infrastructure:

1. Pluvial and fluvial flooding
2. Coastal flooding
3. Damage to infrastructure and buildings
4. Energy disruption due to heat and drought (hotspot: Southern Europe)
5. Energy disruption due to flooding
6. Maritime transport
7. Land transport

For this reason, risk assessments cannot be based on uniform analyses.

A high level of geographical granularity makes it possible to compare territories, identify the most exposed areas and support more effective decisions to protect assets and infrastructure.

From assessment to operational management with climate intelligence and analytics

Climate intelligence integrates historical and forecast climate data, scenarios, scientific models, satellite imagery, geospatial data and technical information on assets to turn them into an operational view of risk.

It makes it possible to identify where risk is concentrated, how it may evolve over time and which assets require priority intervention.

This approach supports decisions such as:

• Prioritising interventions
• Planning maintenance
• Guiding investments
• Protecting operational continuity
• Defining adaptation strategies

When risk becomes measurable, it also becomes manageable.

Risk conditions change over time. For this reason, analyses and priorities must be continuously updated to reflect the evolution of climate, territory and assets.

Airis is the climate intelligence platform developed by Eoliann to turn complex climate data into operational decisions.

By integrating climate, geospatial and asset-level data, it enables organisations to analyse physical risk across infrastructure and strategic assets, visualise how it evolves under different climate scenarios and define intervention priorities to support maintenance, investments, operational continuity and adaptation strategies.

Climate Risk Analysis is the process used to identify, analyse and quantify the impacts of climate change on activities, assets, infrastructure and networks.

By integrating climate and geospatial data with operational information, it turns risk into useful insights to support decision-making, protect business continuity and plan adaptation measures.

When risk is interpreted within the context in which people, assets and territories operate, it becomes a guide for making more informed decisions.

In this article, we will explore:

• What is Climate Risk Analysis and which risks does it consider
• What data is needed to analyse climate risk
• How is a Climate Risk Analysis carried out
• Climate Risk Analysis for assets, infrastructure and linear networks
• Climate Risk Analysis, CSRD, TCFD and double materiality
• From risk analysis to decisions for climate resilience

What is Climate Risk Analysis and which risks does it consider

Climate Risk Analysis assesses how climate events may affect the functioning of organisations, infrastructure and production systems. The objective is to estimate potential impacts in order to identify intervention priorities and support more effective decisions.

The analysis considers two categories of risk.

Physical risks, which are central to the management of assets and infrastructure, include:

• Acute risks, such as floods, landslides, wildfires, extreme rainfall, wind gusts and falling trees.
• Chronic risks, such as heatwaves, drought, rising temperatures and sea-level rise.

These phenomena can compromise business continuity, safety, performance and management costs. Here are some practical examples:

• A flooded transformer substation can interrupt supply across entire service areas
• A landslide can isolate a stretch of road in a mountainous area for days
• A wind gust can bring down a power line and cause a widespread blackout

Physical risks are accompanied by transition risks, linked to regulatory, technological and market developments, which can affect asset value and corporate strategies.

Understanding where risk can disrupt the functioning of a system is the first step towards building tangible resilience.

What data is needed to analyse climate risk

An effective Climate Risk Analysis begins with the integration of data from different sources. Historical climate data makes it possible to analyse observed events, while forward-looking climate scenarios make it possible to estimate how they may evolve over different time horizons.

This information is integrated with geospatial and territorial data, such as satellite imagery, digital terrain models, maps and territorial characteristics, together with asset-specific information, including location, technical characteristics, maintenance status and operational criticalities.

The analysis connects four fundamental elements.

The value of data does not depend on its quantity, but on the ability to turn it into decisions that make infrastructure and territories more resilient.

The quality of Climate Risk Analysis depends on the ability to integrate climate data with organisation-specific information, turning it into a risk assessment that is genuinely useful for defining intervention priorities and adaptation strategies.

Discover how Airis integrates these elements into a single model and provides a measurable impact for each asset.

How is a Climate Risk Analysis carried out

A Climate Risk Analysis follows a structured process that turns climate and geospatial data into useful information for assessing risk and defining intervention priorities.

Define the scope of the analysis

The first phase consists of defining the scope of the assessment by identifying:

• Assets and infrastructure
• Networks and territories
• Operational processes

The scope may include a substation exposed to flood risk, a section of railway track subject to deformation on days of extreme heat, or a road tunnel located on an unstable slope.

The time horizons and climate scenarios most consistent with the organisation’s objectives are also established.

Analyse exposure and vulnerability

For each element, the following are analysed:

• Relevant climate hazards
• Level of exposure
• Physical and operational vulnerability
• Dependencies and interconnections with other assets
• Potential indirect or cascading effects

This phase makes it possible to understand where risk may produce the most significant impacts.

Quantify and prioritise risks

The final phase consists of estimating:

• Probability of events
• Intensity of phenomena
• Physical, operational and economic consequences

Risks are then classified according to their relevance and transformed into:

• Risk maps
• Indicators
• Risk scores
• Operational priorities

In this way, the analysis supports intervention planning and resource allocation.

Climate Risk Analysis for assets, infrastructure and linear networks

Analysing a single asset requires a different approach from analysing infrastructure distributed across a territory.

A linear network cannot be assessed as the simple sum of independent points. It is necessary to consider:

• Geographical continuity
• Interdependencies between assets
• Critical nodes
• More vulnerable sections
• Cascading effects

Protecting a network means understanding how risk propagates through its connections, not only where it occurs.

Airis analyses these dynamics at the level of both individual assets and extended networks, identifying the most exposed sections.

This approach is particularly useful for:

• DSOs and electricity distribution networks
• TSOs and transmission networks
• Road operators
• Railway operators

It is also applicable to telecommunications and other distributed infrastructure, making it possible to compare numerous network segments, identify the most exposed ones and define intervention priorities.

Climate Risk Analysis, CSRD, TCFD and double materiality

Climate Risk Analysis supports reporting, governance and strategic planning by helping organisations assess the financial impacts that climate change may have on assets, infrastructure and business continuity.

It also contributes to the assessment of double materiality by considering:

• The organisation’s impacts on the environment and society
• The climate-related risks and opportunities that may influence the organisation

The information produced supports CSRD requirements and TCFD recommendations, integrating climate risk into decision-making processes without being limited to regulatory compliance alone.

From risk analysis to decisions for climate resilience

The results of Climate Risk Analysis make it possible to:

• Define adaptation priorities
• Plan maintenance and investments
• Schedule infrastructure interventions
• Strengthen business continuity, safety and service reliability
• Optimise resource allocation

The analysis should be updated periodically to incorporate new climate data, forward-looking scenarios and changes to assets.

Climate data creates value when it is turned into operational decisions that increase the resilience of assets, infrastructure and territories.

With Airis, Eoliann provides a platform that integrates climate data, geospatial information and asset characteristics into a single analysis environment.

Thanks to high-resolution risk models, it makes it possible to assess the evolution of physical risks under different climate scenarios and identify the areas that require priority intervention.

The platform supports organisations and infrastructure operators in:

• Analysing climate risk at the level of an individual asset, infrastructure or network
• Comparing current and future climate scenarios
• Identifying exposure, vulnerability and intervention priorities
• Supporting decisions on maintenance, investments and adaptation strategies
• Integrating risk assessment into planning and governance processes

Discover how to turn climate risk into operational decisions.

FAQ

What is the difference between Climate Risk Analysis and Climate Risk Assessment?

The two terms are often used interchangeably. In general, Climate Risk Analysis focuses more on data processing and quantification, while Climate Risk Assessment covers the entire risk assessment process.

What are the main climate risks for businesses and infrastructure?

The main risks include physical risks and transition risks. Physical risks include floods, wildfires, wind gusts, landslides, extreme rainfall, drought, falling trees and heatwaves.

What data is used in a Climate Risk Analysis?

A Climate Risk Analysis integrates historical and forward-looking climate data, climate scenarios, geospatial data, satellite imagery and asset-specific information.

How is the climate risk of a linear network analysed?

The analysis considers the geographical continuity of the network, critical nodes, the most vulnerable sections, interdependencies between assets and potential cascading effects.

How does Climate Risk Analysis support CSRD and double materiality?

It supports the assessment of climate-related financial risks, sustainability reporting and strategic planning, while also contributing to double materiality analysis.

How often should a Climate Risk Analysis be updated?

The analysis should be updated periodically and whenever climate data, scenarios, assets or the operational scope change.

The CER Directive introduces a new approach to the resilience of critical entities, requiring them to assess and manage the risks that may compromise the continuity of essential services.

Directive (EU) 2022/2557, transposed in Italy through Legislative Decree 134/2024, enters a decisive phase in 2026 with the identification of critical entities and the start of operational obligations.

For organizations, demonstrating regulatory compliance is no longer enough.

It becomes necessary to understand how natural events, climate change and other risk factors may affect infrastructure and essential services.

In this article, we analyze what the legislation provides and what implications it has for energy, transport and telecommunications infrastructure.

CER Directive: what it is and why 2026 is a decisive year

The CER Directive (Critical Entities Resilience), namely Directive (EU) 2022/2557, is the European legislation that strengthens the resilience of critical entities responsible for guaranteeing essential services.

The objective is to improve their ability to prevent, withstand, respond to and recover from events that could compromise their operation.

The Directive moves beyond the previous concept of critical infrastructure and introduces the broader concept of a critical entity, including organizations that provide essential services for the functioning of society and the economy.

In Italy, the legislation was transposed through Legislative Decree 134/2024, which defines the national implementation process for the resilience of critical entities.

2026 marks the start of the operational phase of the CER Directive. The main deadlines are:

• 17 January 2026 – identification of entities considered critical according to the criteria established by the legislation.
• 17 July 2026 – adoption of the national list of critical entities and subsequent notification to the entities included.

This process is part of the first “National strategy for the resilience of critical entities”, provided for by the transposing decree and aimed at strengthening the country’s ability to address natural, technological and other risks.

What obligations does compliance with the CER Directive entail?

Key obligations at a glance

1. Assess risks that may interrupt essential services.
2. Identify critical assets, functions and interdependencies.
3. Adopt proportionate technical and organizational measures.
4. Prepare and update the resilience plan.
5. Manage and notify significant incidents.

Main obligations for critical entities

An entity is identified as a critical entity when it provides one or more essential services, operates through infrastructure located in the national territory and an interruption of its activities could produce significant negative effects for the community or the economic system.

The definition is contained in Article 2, paragraph 1, point (a), of Legislative Decree 134/2024: “‘critical entity’ means a public or private entity identified, pursuant to Article 8, among the categories of entities operating in the sectors and subsectors referred to in Annex A, which forms an integral part of this Decree”.

For these entities, the CER Directive introduces a set of obligations that go beyond simple documentary compliance.

Each organization must:

• Carry out a risk assessment covering risks that may compromise the continuity of essential services.
• Consider natural risks, human-made events, interdependencies and cascading effects.
• Identify the assets and infrastructure essential to the provision of the service.
• Adopt technical, organizational and security measures proportionate to the risks identified.
• Provide for prevention, protection, response and recovery measures.
• Integrate climate change adaptation measures.
• Prepare and update a resilience plan.
• Appoint a responsible contact person.
• Notify significant incidents according to the established procedures.

Compliance with the CER Directive does not coincide with the production of documents. Risk assessment must translate into concrete interventions, proportionate to the level of exposure and aimed at guaranteeing the continuity of essential services.

What the CER Directive means for energy, transport and telecommunications

The CER Directive adopts an all-hazards approach, which also includes natural events and climate change.

Resilience does not concern only physical or cyber security, but also the ability of infrastructure to continue operating during events such as floods, wildfires, heatwaves, landslides or extreme weather events.

Energy

In the energy sector, the assessment concerns production plants, substations, power lines and distribution networks.

The CER Directive requires organizations to identify the assets most exposed to phenomena such as floods, extreme heat, wildfires, strong winds, landslides and drought, assessing their potential impact on service continuity.

These analyses support the definition of maintenance priorities, protection interventions, infrastructure redundancy and climate adaptation measures.

Transport

In the transport sector, the focus is on rail and road networks, ports, airports and major logistics hubs.

The objective is to identify the routes and infrastructure most vulnerable to natural events, assessing possible service interruptions, network accessibility and the availability of alternative routes.

This information makes it possible to plan investments and strengthen the operational continuity of the most strategic infrastructure.

Telecommunications

For entities formally identified as critical, the assessment concerns towers, radio base stations, network nodes and distributed connections. In Italy, however, Legislative Decree 134/2024 provides that critical entities in this sector are subject to specific sectoral legislation rather than the main operational obligations under the CER framework.

Climate risk assessment requires organizations to identify the assets most exposed to extreme heat, wind, wildfires, landslides and floods, also considering energy, territorial and logistical dependencies.

These analyses support the definition of maintenance priorities, protection interventions, network redundancy and the adaptation measures needed to guarantee service continuity.

How Airis supports climate risk assessment and management

The CER Directive requires natural risk to be assessed in a structured way, considering asset exposure and their ability to guarantee the continuity of essential services.

Analyses based exclusively on historical data are not sufficient.

Climate change is modifying the frequency, intensity and distribution of extreme events, making it necessary to integrate future scenarios and predictive assessments.

Airis is Eoliann’s platform that makes climate risks predictable by analyzing the probability, intensity and physical and economic impact of events on infrastructure assets and networks.

The platform integrates satellite data, geospatial information and proprietary artificial intelligence models to analyze both current conditions and future climate scenarios, with European coverage and projections up to 2050.

Airis is designed to manage both point assets, such as plants and substations, and linear infrastructure, such as power grids, roads and railways. This makes it possible to assess extensive asset portfolios while maintaining a consistent view of risk.

The analysis provides exposure maps, compares climate scenarios and identifies the hotspots where expected impacts are most significant.

Assets are classified according to their vulnerability and potential impact on operational continuity, supporting the definition of intervention priorities.

This information helps infrastructure operators to:

• Plan resilience and adaptation measures
• Guide investments
• Schedule maintenance
• Make decisions based on objective data and forward-looking scenarios

Would you like to understand how Airis can support the climate risk assessment of your assets?
Request a demo

Airis and the CER Directive

Assess the climate risk of your assets.

Try Airis

FAQ

What is the CER Directive?

The CER Directive (Critical Entities Resilience) is the European legislation that strengthens the resilience of critical entities that manage essential services. It requires them to assess risks, adopt protection measures and prepare plans to guarantee operational continuity.

What obligations does the CER Directive establish for critical entities?

Critical entities must assess natural and human-made risks, identify essential assets, adopt resilience measures, prepare a resilience plan and notify significant incidents in accordance with the legislation.

What is the difference between the CER Directive and the NIS2 Directive?

The CER Directive concerns the physical and operational resilience of critical entities against natural events and other threats. The NIS2 Directive instead regulates the security of networks and information systems. The two pieces of legislation address different but complementary aspects of infrastructure resilience.

Does the CER Directive also apply to telecommunications infrastructure?

Yes. Digital infrastructure is among the sectors identified by the CER Directive. In Italy, however, critical entities in this sector are subject to specific sectoral legislation rather than the main operational obligations under the CER framework.

How often must the risk assessment be updated?

The CER Directive requires the risk assessment to be kept up to date and reviewed periodically, as well as whenever significant changes may alter the risk profile of the organization or its infrastructure.

How can predictive models support the resilience of critical infrastructure?

Predictive models make it possible to estimate how climate risk may evolve over time and the potential impact it may have on assets. By integrating satellite data, geospatial information and future climate scenarios, they support the prioritization of interventions, maintenance planning and adaptation decisions, strengthening the continuity of essential services.

In recent years, events such as the floods in Emilia-Romagna in 2023 and Valencia in 2024 have made it clear that flood risk is now a concrete, recurring and systemic issue. It concerns not only emergency response, but also how territories, infrastructure and economic activities can prepare for increasingly unstable climate conditions. Understanding flood hazard therefore does not simply mean describing what happens after an event: it means measuring, modelling and comparing complex physical phenomena to enable those who manage assets and territories to prevent, plan for and reduce the impact of future damage. How far can the water reach? At what depth? With what probability? And how might these conditions change under future climate scenarios? Answering these questions today requires the integration of hydrology, physical modelling, geospatial data, satellite observations and artificial intelligence. In this article, we explore this technical chain in detail: how flood hazard is estimated, which approaches are available, what their strengths are and which limitations must be understood in order to interpret the results correctly.

Estimating flood risk requires three distinct elements, which experts in the field identify as hazard, exposure and vulnerability. Hazard refers to the dangerousness of the physical phenomenon and estimates, for example, how far water may extend in the event of a river overflowing, what level it may reach and how frequently the phenomenon may recur. Exposure defines what is located within that area, such as buildings, networks and people. Vulnerability indicates how fragile these elements are in relation to the intensity of the event. While estimating these three elements is already inherently difficult, climate change introduces an additional layer of complexity by altering the hazard component in the future. Projecting flood risk into the future requires an understanding of what climate conditions may be like over the coming years. To do so, internationally shared climate scenarios are used, known as Shared Socioeconomic Pathways, or SSPs: plausible representations of future conditions based on assumptions regarding emissions, population growth and economic development.

A flood risk assessment today follows a logical chain that starts with assets and produces physical and economic indicators. The first step is to locate the elements being assessed through geocoding: a plant, a section of a network or a public building. This information is then overlaid with hazard maps, which describe where water may spread, how deep it may become and with what probability. This is the technical and scientific core of the analysis. Today, hazard maps are mainly built using two prevailing methodologies: numerical simulations, rooted in the physics of the phenomenon, and artificial intelligence models, which draw on increasingly extensive satellite archives to estimate hazard across broader geographical areas.

The first approach is based on a hydrological model, often a mathematical model, which translates rainfall into discharge: the amount of water flowing through a river at a given time in response to the rain that has fallen across the upstream catchment. A hydraulic model, often configured in one dimension along the river channel or in two dimensions across the entire floodplain, then simulates where the water spreads, how deep it becomes and how fast it flows. These models incorporate real physical constraints, including topography, embankments, bridges and drainage systems, and are extremely valuable for high-resolution local analysis. Their main limitation, however, is scalability: they are slow, costly and difficult to extend across entire river basins or at continental scale.

To meet the need to scale analysis across increasingly large areas, a second approach has emerged in recent years, based on technologies that combine artificial intelligence models with increasingly rich and widespread satellite data. Unlike numerical simulations, which begin with physical equations and detailed local data, this method relies primarily on observations of the territory from above: Synthetic Aperture Radar, or SAR, imagery, which can detect the presence of water even through cloud cover; digital elevation models, which describe the morphology of the land or allow its properties to be derived; and archives of past floods, which document where water actually extended during previous events. In practice, satellites do not directly “measure” flood hazard, but provide continuous spatial information over very large areas, often at continental or global scale, that would otherwise be impossible to cover with the same speed.

This information must then be appropriately processed by an artificial intelligence model trained to recognise the patterns connecting meteorological, climatic, morphological and human-related characteristics to flood hazard. This training process is at the heart of the method: the model is provided with thousands of examples in which both the input conditions, such as rainfall, elevation, urbanisation and proximity to watercourses, and the observed outcome, such as flood extent, depth or velocity, are known. The model gradually learns to recognise which combinations of factors are associated with floods of different intensities, without the developer having to explicitly encode every physical relationship. Once trained, the model can be used for inference: when applied to a new area, it estimates flood hazard based on what it has learned from previous examples. The advantage is speed: once developed, it can produce hazard maps over vast geographical areas much faster than a complete hydrological-hydraulic modelling chain. The limitation is equally clear: the quality of the output depends on the quality and representativeness of the data used to train the model. If a model is asked to estimate flood hazard in a context that differs significantly from the examples it encountered during training, the estimate may be less reliable.

The real potential, however, does not lie in replacing numerical simulations, but in combining the two approaches. Maps based on artificial intelligence and satellite data provide coverage and speed; physical simulations provide local accuracy and interpretability. Used together, they can compensate for each other’s limitations: a satellite-based model can provide broader territorial context on the conditions associated with potential flood hazard, while numerical simulations can validate or provide training data for artificial intelligence estimates, particularly at critical points such as embankments, infrastructure or basins with complex flood-defence systems. Within this hybrid approach, artificial intelligence does not “discover” physical laws unknown to science: it recognises statistical patterns within the data. This is why, in our work at Eoliann, we consider these tools complementary: useful for achieving scale, but insufficient when used in isolation.

The previous paragraphs describe how flood hazard is measured: how far the water may reach, at what intensity and with what frequency. Hazard alone, however, is not sufficient to estimate risk. Two additional pieces of information are required: exposure, meaning what is located within the potentially flooded area, including buildings, infrastructure and people; and vulnerability, meaning how fragile these elements are when faced with a flood of a given intensity. Without exposure and vulnerability, a hazard map remains a description of the physical phenomenon rather than a basis for evaluating its consequences. This is where depth-damage curves come into play: empirical relationships connecting water depth to the expected damage to an asset or activity. By applying them to each exposed asset and appropriately combining hazard and vulnerability information, several economic indicators can be obtained, including Expected Annual Damage, or EAD, the average expected annual loss. This is one of the metrics most commonly used to compare different risks, prioritise interventions or support insurance and investment analysis. The final steps involve projection under future climate scenarios and validation: comparing estimates with observed events or official maps produced by public authorities, where available, to assess whether the model results are sufficiently robust.

Despite significant technological and methodological progress, flood risk assessment never produces an absolute truth. Every assessment depends on the quality of the available data, the resolution of the maps, the representation of embankments, walls and flood defences, the ability of climate models to describe local phenomena and the assumptions adopted for future scenarios. Uncertainty should therefore not be treated as a flaw in the model, but as a structural dimension of the scientific problem. In this article, we address it for methodological completeness: not as an operational output that is already available, but as an essential element for interpreting any risk estimate correctly. When uncertainty is not explicitly quantified, it is still important to acknowledge its existence and indicate where results may be more robust or, conversely, require greater caution. Looking ahead, the use of climate-model ensembles and comparisons between plausible scenarios can provide a better representation of this variability, avoiding the interpretation of a single estimate as a certain value.

Flood hazard assessment therefore does not depend on a single model, but on a chain of scientific choices: which data to use, how to represent the territory, how to integrate satellite observations and physical simulations, how to validate the results and how to distinguish what can be estimated with greater confidence from what requires caution. This is where the quality of the technical work is determined. At Eoliann, we are developing a hybrid approach that combines the physics of the phenomenon, geospatial data, artificial intelligence and validation against observed events, with the aim of making climate risk assessment more scalable, transparent and interpretable. Expertise does not lie in promising certainty, but in building models that can explain complexity more effectively: transforming different sources of data into a more robust understanding of flood hazard, its limitations and the conditions under which it can become useful information for prevention, planning and resilience.

This article was produced by Eoliann’s Climate Risk Models Unit and launches Eoliann Lens, a series of in-depth articles exploring the technology and methodologies behind our work.

In two previous articles, we analysed two fundamental elements: Climate Risk Assessment as a process to evaluate exposure and vulnerability, and the Climate Risk Model, which transforms data into quantitative risk assessments.

However, one key question remains: how can these analyses be made accessible, updatable and usable by those who need to make decisions every day?

This is where a Climate Risk Assessment Tool comes in: a platform that makes it possible to integrate climate, geospatial and operational data in order to identify the most exposed assets, estimate their vulnerability, compare future scenarios and define intervention priorities.

With the right tool, complex analyses become useful information for different teams: from risk management to asset management, as well as operations, maintenance, sustainability and strategic planning.

In this guide, we explore everything related to a Climate Risk Assessment Tool:

• What it is and what benefits it provides
• Its features
• How to recognise an enterprise solution
• The advantages of a climate intelligence dashboard
• Mistakes to avoid
• The Airis platform

What is a Climate Risk Assessment Tool and what are its benefits?

A Climate Risk Assessment Tool is a digital platform designed to analyse, visualise and prioritise physical climate risks that may affect assets, infrastructure and operational activities.

To produce truly useful assessments, it must integrate information from different sources, including:

• Historical climate data
• Future climate scenarios
• Geospatial data
• Asset information
• Exposure and vulnerability models

To understand its role, it is useful to connect it to the concepts introduced in the previous articles.

Climate Risk Assessment defines the risk evaluation process.
The Climate Risk Model processes scenarios, probabilities and potential impacts.
The Climate Risk Assessment Tool makes this information accessible and usable by different people, teams and business functions. It is the meeting point between analysis and decision-making.

The real value emerges when risk stops describing what could happen and starts guiding what we can do.

Platform benefits

For those managing assets and infrastructure, the most concrete benefits include:

• Faster and updatable analyses
• More granular assessments down to the single-asset level
• Reduced uncertainty in decision-making
• Support for resilience planning
• Better allocation of investments
• Collaboration between risk managers, asset managers, operations and sustainability teams

When risk becomes readable in the context of assets, it also becomes easier to identify where to intervene today in order to reduce tomorrow’s impacts.

Knowing that an area is exposed to extreme rainfall is useful. Understanding which assets may be involved, with what operational consequences and over what time horizon, makes that information usable in decision-making.

Features of a Climate Risk Assessment Tool

Not all platforms offer the same level of decision-making support.

The choice of a Climate Risk Assessment Tool should not be based solely on the interface or the quality of the visualisations. What truly matters is the ability to support operational, financial and strategic decisions through reliable data and contextualised asset-level analyses.

It is precisely through specific features that data and analysis become concrete decision-support tools. Among the most relevant are:

Asset mapping

This makes it possible to upload and geolocate assets, classifying them by type, criticality and operational function.

A railway operator, for example, can visualise the entire network and distinguish strategic sections from secondary ones in order to assess their different levels of exposure.

Hazard screening

This allows users to analyse the main climate-related phenomena relevant to their operational context:

• Floods
• Heatwaves
• Droughts
• Wildfires
• Extreme wind
• Landslides

The goal is to understand which threats require greater attention and where they are concentrated.

Scenario analysis

This allows users to compare different climate scenarios and assess how risk may evolve over time.

For an energy infrastructure, for example, this may mean comparing the current level of exposure with the one expected in 2030 or 2050.

Risk scoring

This assigns risk scores to assets based on exposure, vulnerability and potential impacts.

This makes it possible to quickly identify the most critical elements and define intervention priorities.

Data visualization

Interactive maps, dashboards and synthetic indicators help transform complex data into information that can be read and understood by different teams.

Economic impact assessment

This makes it possible to assess potential economic damage, operational downtime and consequences for investment or management costs.

Export and integration

The most advanced platforms allow data export, API use and integration with existing enterprise systems.

How each feature contributes to decision-making

Weather and climate data integration

In the article dedicated to the Climate Risk Model, we saw how climate and geospatial data represent the necessary inputs for building risk analyses. When evaluating a Climate Risk Assessment Tool, data becomes a criterion for understanding whether the tool is able to provide reliable, up-to-date analyses that are consistent with the use case.

This is why it is important to understand what types of data are integrated and how they are used:

• Historical data, useful for analysing events that have already occurred
• Near real-time data, used for monitoring, alerts and early warning
• Future climate scenarios, essential for investments and planning
• Satellite and Earth Observation data, useful for observing the territory and its changes
• Geospatial data, necessary to contextualise risk in relation to assets
• Asset-level data, essential for connecting climate risk to the operational characteristics of infrastructure

The quality of a tool is measured by its ability to transform different types of data into a single, coherent interpretation of risk.

Checklist for evaluating weather and climate data integration

In addition to these aspects, it is important to evaluate geographical coverage, update frequency, uncertainty management and the methodological consistency of the analyses.

Difference between generic tools and enterprise solutions

The choice of a platform depends not only on the available features, but above all on the type of decisions it needs to support.

A preliminary risk assessment has different requirements from the management of networks, plants and assets distributed across a territory. This is why, in practice, it is useful to distinguish between generic tools and enterprise solutions.

Generic tools

Generic tools are often used to obtain an initial reading of climate risk.

They are generally based on synthetic indicators and make it possible to quickly identify the areas potentially most exposed to specific climate phenomena.

For this reason, they are particularly useful for:

• Preliminary screening
• Exploratory analyses
• Initial territorial assessments
• Identification of areas requiring further investigation

Although they provide an effective overview of risk, they have limitations when decisions concern specific assets, operational continuity or investment planning.

Enterprise solutions

Enterprise solutions are designed to support organisations that manage multiple assets, critical infrastructure and complex decision-making processes.

They make it possible to integrate climate, territorial and business data within a single analytical environment, working down to the level of the individual asset.

Their main features include:

• Asset-level analysis
• Integration with enterprise systems and processes
• Management of large asset portfolios
• Support for climate scenarios and impact assessments
• Prioritisation of interventions
• Support for operational and financial decisions

These platforms are used in areas such as:

• Risk management
• Asset management
• Operations
• Business continuity
• Investment planning

Comparison and differences between generic and enterprise tools

The difference becomes clear in real operational situations.

A generic tool can indicate that a certain area is exposed to flood risk.

An enterprise solution helps identify the most vulnerable assets in that area, what impacts they may suffer and which interventions should be prioritised.

For critical infrastructure, up-to-date data, integration into business processes and continuity of analysis become decisive factors.

The advantages of climate intelligence dashboards

The amount of data feeding a Climate Risk Assessment Tool can quickly become difficult to interpret.

For this reason, a climate intelligence dashboard has a precise task: to make complex information understandable without oversimplifying it.

Reading risk effectively requires the ability to move from the overall picture to the individual asset, comparing scenarios, exposure levels and intervention priorities.

This type of reading requires:

• Interactive maps of the territory and assets
• Filters by asset, geographical area, hazard and climate scenario
• Exposure indicators
• Vulnerability indicators
• Synthetic risk scores
• Time trends
• Configurable alerts and critical thresholds

The goal is to provide information that helps support faster and more informed decisions.

The advantages of dashboards emerge when they are used by specific business roles:

• An operations manager can identify the assets most exposed to heat stress and plan preventive actions.
• An asset manager can define maintenance priorities for railway sections, plants or networks that are more vulnerable.
• Strategic management can compare different investment options and assess adaptation measures.
• A sustainability manager can analyse future climate scenarios and monitor their implications for assets.
• A risk manager can integrate climate risk into the company’s ERM framework and compare it with other risk factors.

When everyone observes the same scenario through a shared information base, it becomes easier to coordinate priorities, investments and adaptation strategies.

Mistakes to avoid when choosing the software

The choice of a Climate Risk Assessment Tool can directly influence the quality of the analyses and the decisions that follow. This is why it is important to look beyond the most visible features and carefully assess data, methodology and integration capabilities.

To avoid mistakes, it is necessary not to:

Choose based only on the interface

A good user experience is important, but it does not replace data quality, methodological transparency and analytical robustness.

Evaluate only the price

An inexpensive tool that is difficult to integrate may produce results that are hard to use within business processes and require additional management and verification activities.

Ignore data granularity

Asset-level decisions require sufficiently detailed data. Information that is too aggregated can hide relevant differences between apparently similar assets.

Overlook the verification of covered hazards

Every organisation is exposed to different risks. It is essential to verify that the platform analyses the hazards that are truly relevant to the specific operational and territorial context.

Neglect enterprise integration

If the tool does not communicate with enterprise systems, processes and datasets, the risk is to create yet another information silo that is difficult to maintain and use.

Confuse reporting with decision support

Producing reports does not necessarily mean supporting operational decisions. The value of the platform depends on its ability to transform data into priorities, scenarios and concrete actions.

Best practices when evaluating a platform

Before choosing a Climate Risk Assessment Tool, it is useful to:

• Evaluate the platform through a real use case
• Involve different business functions in the selection process
• Verify data, methodology, integration and scalability
• Request concrete examples of outputs on assets and climate scenarios

The quality of a tool is measured above all by its ability to support decisions concerning real assets and concrete operational contexts.

Airis: Eoliann’s Climate Risk Assessment Tool

Throughout this article, we have seen how the choice of a Climate Risk Assessment Tool depends on several factors: data quality, granularity of analysis, methodological robustness, integration capabilities and decision-making support.

Airis was created precisely to respond to these needs in contexts involving strategic assets and critical infrastructure.

The platform developed by Eoliann makes it possible to integrate climate data, geospatial data and asset information in order to analyse physical risk in a contextualised way and support planning, adaptation and operational management activities.

Its main features include:

• Integration of climate and geospatial data
• Asset-level analysis on infrastructure and critical systems
• Risk visualisation through interactive dashboards
• Support for the evaluation of future climate scenarios
• Identification of operational and investment priorities
• Support for operational continuity and resilience strategies

A Climate Risk Assessment Tool expresses its value when it transforms data and scenarios into clear operational priorities.

Discover how to support climate risk assessment on assets and infrastructure: try Airis on your own portfolio!

FAQ

Does a Climate Risk Assessment Tool require specialist climate expertise?

No. The most advanced platforms make climate risk understandable also for roles such as risk managers, asset managers and operations managers through dashboards, indicators and intuitive visualisations.

How long does implementation take?

It depends on the number of assets and the level of integration required. In general, the process includes data collection, analysis configuration and activation of the relevant features for different business teams.

How is it kept up to date?

Through the periodic updating of climate data, scenarios and asset information, keeping analyses aligned with the evolution of the operational and environmental context.

Who should take part in choosing the tool?

Risk management, asset management, operations, sustainability and business continuity teams. Involving different functions helps correctly assess operational and decision-making needs.

Can a Climate Risk Assessment Tool support ESG reporting?

Yes. It can provide data and analyses useful for documenting exposure to climate risks, potential impacts and adaptation strategies within reporting and disclosure processes.

When climate changes faster than infrastructure, what makes the difference is not the ability to react to an event, but the ability to understand its possible consequences in advance.

If Climate Risk Assessment defines the process through which hazards, exposure and vulnerability are analysed, the Climate Risk Model is the analytical engine that makes it possible to estimate the probability of climate events, the intensity of phenomena, asset fragility, economic and functional impacts, and intervention priorities.

Its goal is not to describe risk in theoretical terms, but to make it measurable and usable in decision-making.

In this article, we clarify:

• What a Climate Risk Model is
• How it differs from Climate Change Models
• What data it uses
• How it works with probabilistic scenarios
• How it is validated
• How it supports CAPEX, OPEX, operational continuity and resilience

What is a Climate Risk Model?

A Climate Risk Model is an analytical model designed to estimate physical climate risk on assets, infrastructure, networks, operational processes and business systems. It combines climate, geospatial, physical, operational and economic data to transform complex information into indicators that can support decision-making.

In a Climate Risk Assessment, three fundamental dimensions are analysed: hazard, exposure and vulnerability.

The Climate Risk Model integrates these components and translates them into quantitative or semi-quantitative assessments of expected impacts.

The goal is not to forecast the weather or determine with absolute certainty what will happen in the future. Rather, the model is used to estimate plausible risk scenarios and support concrete decisions on where to intervene, with what priority and with what level of investment.

The value of a model is not measured by its ability to eliminate uncertainty, but by its ability to make it readable and manageable.

In this way, climate risk stops being an isolated piece of information and becomes an element that can be integrated into business processes and adaptation strategies.

Difference between Climate Risk Models and Climate Change Models

The two tools are different but complementary, because they answer different questions:

Climate Change Model → “How is the climate changing?”

Climate Risk Model → “What impact could this change have on my assets?”

Climate Change Models

Climate Change Models simulate the evolution of the climate system over time. They analyse variables such as:

• Temperature
• Precipitation
• Sea level
• Frequency of extreme events
• Hazard intensity
• Future climate patterns

These models help us understand how the climate could evolve over the coming decades based on different emissions scenarios.

Climate Risk Models

Climate Risk Models add further layers of analysis to the variables of Climate Change Models, connected to the assets and activities that need to be protected:

• Physical assets
• Critical infrastructure
• Local vulnerabilities
• Operating costs
• Economic losses
• Service continuity
• Decision-making priorities

Example of integration between the two models

Let us imagine an industrial facility located near a river in an area that has recorded increasingly frequent flood events over recent decades.

A Climate Change Model can show that, over the next thirty years, the intensity and frequency of extreme rainfall will increase in that territory, making flooding events more likely.

A Climate Risk Model uses this information to assess how that change could affect the facility. It can estimate the probability of flooding in production areas, potential damage to equipment, expected days of downtime and associated economic impacts, helping to define intervention priorities and adaptation investments.

Two models, two perspectives on risk: summary of the differences

Why use a Climate Risk Model for assets and infrastructure?

Two apparently similar assets can present very different levels of risk depending on their geographical location, elevation, proximity to rivers, coastlines or landslide-prone areas, construction characteristics, maintenance level, operational redundancy, dependencies on external networks and their criticality for operational continuity.

A Climate Risk Model helps understand how risk can translate into concrete consequences for assets and which interventions can reduce impacts over time.

It makes it possible to:

• Identify the most exposed facilities
• Estimate physical and economic impacts
• Define intervention priorities
• Support adaptation plans
• Reduce downtime and unexpected costs
• Strengthen the resilience of critical infrastructure

It is through the ability to connect climate scenarios, vulnerability and possible consequences that risk becomes a concrete variable supporting decisions.

An accurate model can establish that:

• A railway line exposed to heat stress may require interventions on materials or new maintenance strategies.
• An electrical substation located in an area exposed to wildfires may require protection buffers, dedicated monitoring systems and measures to reduce the vulnerability of the most exposed infrastructure.
• A logistics hub exposed to extreme rainfall can generate delays across the entire supply chain, while a water network subject to prolonged drought periods may require investments in redundancy and alternative supply sources.

How the model supports decisions

Probabilistic climate risk models

Probabilistic climate risk models are currently one of the most advanced approaches to physical risk assessment.

Unlike deterministic models, they do not return a single absolute value, but produce a distribution of possible scenarios, assigning each one a probability of occurrence.

This approach is particularly useful because climate risk inevitably contains margins of uncertainty.

This type of model can estimate:

• Probability of an event occurring
• Expected intensity
• Frequency of critical threshold exceedance
• Potential damage
• Average economic impact

Among the most commonly used outputs are:

• Annual Expected Loss (AEL) → example: expected average annual economic loss of €500,000
• Risk Score → example: asset classified as high risk, equal to 8.5/10
• Downtime Probability → example: 25% probability of operational downtime by 2050
• Scenario 1-in-100 years → example: event with a 1% probability of occurring each year

When risk is translated into clear information, it becomes easier to assess the available options and guide decisions.

Those who manage assets and infrastructure can:

• Compare different investment options
• Assess trade-offs between possible interventions
• Define acceptable risk levels
• Establish adaptation priorities based on quantitative evidence

Input data for a Climate Risk Model

The quality of a model depends directly on the quality of the data that feeds it.

The more accurate, granular and up-to-date the data, the more reliable the estimates produced.

In Climate Risk Assessment, geospatial data already represents a fundamental component. Within a Climate Risk Model, it becomes one of the central elements for scenario simulation and impact quantification.

The main categories of input include:

Climate data

• Historical series
• Temperature
• Precipitation
• Wind
• Drought
• Sea level
• Future climate scenarios

Geospatial data

• Asset coordinates
• Elevation
• Land use
• Distance from rivers or coasts
• Hazard maps

Asset data

• Infrastructure type
• Age
• Materials
• Design standards
• Maintenance status

Operational and economic data

• Plant downtime costs
• Maintenance costs
• CAPEX
• OPEX
• Operational criticality

The role of data in building the model

How risk models are validated

Risk model validation is what makes it possible to distinguish a reliable result from an output that is difficult to interpret or use in decision-making.

For this reason, every Climate Risk Model must undergo continuous checks that assess its robustness, consistency and ability to represent risk realistically.

This requires analysis conducted on multiple levels:

• Backtesting on past data
• Control of the assumptions underlying the model
• Verification of the quality and completeness of the datasets used
• Sensitivity analysis with respect to the most relevant variables

Validation is not only used to measure the accuracy of simulations. It is also used to understand how the model reacts to changes in initial conditions and how robust its estimates are across different contexts.

For this reason, a model should not be assessed solely on the basis of its technical complexity. Equally important are:

• Transparency of the methodologies used
• Consistency of the analytical framework
• Possibility of being updated over time
• Ability to produce genuinely usable insights

One of the most common mistakes is relying on a numerical output without understanding the assumptions, limitations and margins of uncertainty that influence the result.

The most advanced best practices do not simply return a numerical result. Each output is accompanied by methodological explanations, confidence levels and alternative scenarios that help interpret the risk correctly and use it in decisions.

Integrating models into business processes

A Climate Risk Model creates value only when it becomes part of the decision-making process. It is not a tool to be consulted occasionally, but an information base that supports operational, strategic and investment decisions.

Integrating models into business processes helps improve activities such as:

• Asset management
• Predictive maintenance
• Business continuity
• Investment planning
• Procurement
• Risk management
• Climate adaptation strategies

For this to happen, the model must be embedded in a continuous process that accompanies the entire asset management cycle.

The integration of the model into business processes develops through several key activities:

• Collecting and updating asset information
• Modelling risk based on climate, territorial and operational data
• Identifying and prioritising the most critical assets
• Defining mitigation, adaptation or protection interventions
• Continuous monitoring and periodic updating of scenarios

The model should not be considered a one-off analysis. To continue supporting effective decisions, it must evolve together with assets, operating conditions and climate scenarios, becoming a living tool capable of accompanying the organisation over time.

From model to decision: the role of Airis

Every resilient decision begins with the ability to connect what could happen with what can be done today.

Airis is the platform developed by Eoliann to transform complex climate data into operational guidance.

By integrating climate, geospatial and asset-level data, it enables the analysis of physical risk on infrastructure and strategic assets, observing its evolution across different climate scenarios.

Through maps, indicators and high-resolution models, Airis makes it possible to:

• Integrate climate data, territorial data and asset information into a single analytical environment
• Model physical risk on infrastructure and networks
• Visualise present and future scenarios down to the single-asset level
• Identify exposure, vulnerability and intervention priorities
• Support decisions related to CAPEX, OPEX and operational continuity
• Plan adaptation actions based on quantitative evidence

In this way, climate risk becomes usable information to define priorities, guide investments and prepare assets and infrastructure for future conditions.

Discover how to turn climate risk into operational decisions: book a personalised Airis demo!

FAQ

What data is needed to build a Climate Risk Model?

A Climate Risk Model uses different categories of data, including historical and future climate data, geospatial information, physical characteristics of assets, vulnerability indicators, operating costs, CAPEX, OPEX and information on the asset’s criticality for the business or for operational continuity.

Why use probabilistic models in climate risk?

Climate risk is characterised by an intrinsic level of uncertainty. Probabilistic models make it possible to assess multiple possible scenarios, estimate probabilities of occurrence and quantify different levels of impact, providing a more robust basis for adaptation and investment decisions.

How does a Climate Risk Model support CAPEX and OPEX?

A Climate Risk Model helps identify where to invest to reduce future vulnerabilities and where to intervene to contain operating costs, extraordinary maintenance, downtime and inefficiencies. In this way, it supports more effective planning for both infrastructure investments and long-term operational management.

Yesterday, the territories we knew remained unchanged for decades. Then the climate began to change rapidly, transforming balances that once seemed destined to last. 

That is why, today, Climate Risk Assessment is an essential decision-making tool for protecting strategic assets, reducing operational disruptions, and planning investments with greater awareness. 

Let’s look at how operational methodologies, geospatial data, and vulnerability analysis help us understand climate risk and strengthen infrastructure resilience. 

Climate Risk Assessment for Critical Infrastructure 

Climate risk assessment for critical infrastructure is the process through which exposure, vulnerability, and the potential impacts of climate events on essential systems are analysed. 

This includes power plants, railway lines, water networks, data centres, industrial facilities, and all those systems on which essential services for everyday life and people’s safety depend. 

In an effective Climate Risk Assessment, climate risk is generally analysed through three main components: 

Hazard. This represents the climate-related threat, such as floods, heatwaves, wildfires, or droughts. 

Exposure. This concerns the presence of an asset in an area exposed to the phenomenon. Two identical infrastructures can have very different levels of risk simply because of their location. 

Vulnerability. This measures the asset’s ability to withstand, adapt to, and continue operating under climate-related stress. 

It is precisely the dynamic integration of these three dimensions that makes Climate Risk Assessment a truly operational tool. It is not enough to know that an event may occur. We need to understand which assets will be affected, how vulnerable they are, and what consequences may unfold over time. 

How to Map Asset Vulnerability 

Asset vulnerability is one of the most important elements in climate risk assessment. It is the factor that makes it possible to turn a risk map into a concrete decision. 

Two infrastructures exposed to the same climate event may react in completely different ways depending on their physical, operational, and systemic characteristics. 

For this reason, vulnerability mapping requires a multi-layered approach. 

1. Identification of Critical Assets

The first step is to identify strategic assets and classify them according to: 

• Strategic function
• Business impact
• Criticality for service continuity
• Level of interdependence with other systems 

A railway hub, an electrical substation, or a data centre can have very different systemic effects in the event of a disruption. 

2. Analysis of Physical Characteristics

The second phase concerns the structural characteristics of the asset. 

This includes elements such as: 

• Construction materials
• Age of the infrastructure
• Design standards
• Ability to withstand extreme events 

Infrastructure designed decades ago was often not built to operate under today’s climate conditions. 

3. Assessment of Operating Conditions

The way an asset is used also affects its vulnerability. 

The main factors influencing vulnerability include: 

• Workloads
• Environmental conditions
• Maintenance levels
• Thermal and functional stress 

An infrastructure already operating close to its limits tends to be more vulnerable during extreme climate events. 

4. Analysis of Systemic Dependencies

One of the most common mistakes is assessing individual assets in isolation. 

In reality, many critical infrastructures operate within interconnected ecosystems. A vulnerability can propagate across supply chains, energy networks, digital systems, and logistics hubs. 

That is why the most advanced best practices adopt a systemic approach, capable of reading relationships, dependencies, and possible cascading effects. 

Physical Risks for Critical Infrastructure

Physical risks for critical infrastructure represent the direct impacts that climate events can generate on assets, networks, and operating systems. 

In Climate Risk Assessment, these risks are generally divided into two main categories: acute risks and chronic risks. 

Acute Risks 

Acute risks are sudden, high-intensity events. 

These include phenomena such as: 

• Floods
• Storms
• Wildfires
• Landslides
• Extreme precipitation 

These phenomena can cause immediate structural damage, operational disruptions, and loss of service continuity. 

A flood can block transport infrastructure and compromise electrical substations. A wildfire can interrupt energy and telecommunications networks. A landslide can isolate entire territories. 

Chronic Risks 

Chronic risks, on the other hand, develop over the long term. 

In these contexts, the following phenomena become relevant: 

• Increase in average temperatures
• Sea level rise
• Water stress
• Progressive degradation of environmental conditions 

These phenomena tend to gradually reduce the performance, reliability, and lifespan of infrastructure. 

An energy infrastructure exposed to prolonged heatwaves, for example, may experience both reduced efficiency and peaks in energy demand. This generates growing operational stress and increases the risk of disruptions. 

Operational Impacts and Future Scenarios 

When these phenomena intensify, their impacts can translate into: 

• Disruptions in the distribution of energy, gas, and water
• Blockage of logistics connections, including railways, airports, motorways, and ports
• Damage to communication systems, including data centres, servers, and telephone networks
• Increased maintenance on critical networks and facilities
• Greater frequency of failures and operational interruptions 

That is why Climate Risk Assessment cannot be limited to historical data. 

The most advanced analyses look beyond what has already happened. They integrate future climate scenarios and probabilistic models to understand how risk may evolve over time and which systems may become more exposed in the coming decades. 

Geospatial Data for Climate Risk Assessment 

The real qualitative leap in Climate Risk Assessment occurs when risk is read in relation to the territory, the assets, and the scenarios that may change their exposure over time. 

To build this understanding, different sources are needed, capable of capturing both the climate dimension and the physical and operational dimension of the infrastructure: 

• Global and regional climate models, such as CMIP and IPCC scenarios
• Earth Observation satellite data
• Topographic datasets Historical meteorological data
• Geolocated information on assets 

The integration of geospatial data for climate risk makes it possible to connect each infrastructure to its environmental and climate context, observing how exposure and vulnerability may evolve across different future scenarios. 

In this way, it becomes possible to locate assets, overlay them with risk maps, and simulate future scenarios to understand where the most relevant vulnerabilities, operational disruptions, or physical impacts may emerge. 

To transform climate and territorial data into indications that can be used in decision-making, these analyses generally follow a multi-step process. 

Reading risk with greater precision means being able to guide decisions more effectively, prepare in advance for adverse events, and protect the environment and people more effectively. 

Operational Continuity and Asset Resilience 

The ultimate goal of Climate Risk Assessment is not to produce data. It is to strengthen operational continuity and asset resilience. 

When risk is clearly understood and contextualised at asset level, it becomes possible to move from reactive management to predictive planning. This means preparing infrastructure and systems to operateeven under climate conditions that differ from the past. 

The main operational levers concern mitigation, adaptation, and planning. 

1. Mitigation

Mitigation strategies aim to directly reduce exposure and impacts. In these cases, interventions such as the following become important: 

• Physical protections
• Flood barriers
• Structural reinforcements
• Plant protection systems 

2. Adaptation

Adaptation, on the other hand, concerns the evolution of infrastructure to make it more resilient over time. 

In practice, this often leads to: 

• Infrastructure redesign
• Updating design standards
• Reviewing operating capacities
• Adapting to new climate conditions 

3. Operational Planning

Operational planning also directly affects the ability to maintain continuity and response during extreme climate events. 

To maintain operational continuity, actions such as the following become central: 

• Emergency plans
• Backup systems
• Operational redundancy
• Service continuity procedures 

The Relationship Between Analysis and Action Is Direct 

Without climate risk analysis, decisions tend to be reactive. 

With a structured Climate Risk Assessment, it becomes possible to anticipate critical issues, define priorities, and strengthen asset resilience. 

From Data to Decisions: The Role of Advanced Platforms 

The growing complexity of Climate Risk Assessment makes it increasingly difficult to manage climate data, scenarios, and vulnerabilities through traditional tools. 

Organisations need platforms capable of integrating different kinds of information, transforming them into operational insights, and supporting faster, more contextualised, and more defensible decisions. 

That is why tools capable of offering the following are becoming increasingly relevant: 

• Integrated platforms
• Advanced analytics
• Predictive models
• Intuitive visualisation systems 

The most advanced solutions make it possible to observe risk at asset level, compare future scenarios, and understand how vulnerabilities and impacts may evolve over time. 

Airis translates this approach into a reading of risk that integrates satellite data, climate models, and geospatial information on assets. 

The platform makes it possible to transform complex data into indicators and maps that can be used to support planning, operational resilience, and infrastructure investments: book a demo and try Airis on your assets.

FAQ 

What is a Climate Risk Assessment applied to assets? 

It is a process that analyses exposure, vulnerability, and climate impacts on physical assets to support strategic, operational, and resilience-related decisions. 

Which infrastructures are considered critical? 

Energy networks, transport systems, water systems, telecommunications, and strategic industrial facilities are considered critical infrastructure because they ensure the continuity of essential services. 

Why is geospatial data essential? 

It makes it possible to correlate asset location, climate scenarios, and risk levels with greater precision, supporting predictive analysis and more informed decisions. 

How can asset resilience be improved? 

Through preventive climate risk assessment, operational planning, and structural interventions based on data and evolving scenarios. 

Climate risk is still often treated as a distant issue. But for infrastructure, networks, and critical assets, it is already a factor that affects how they operate, how resilient they are, and how muchthey are worth—especially in the face of extreme climate events. 

When this perspective shifts, the way decisions are made shifts as well. 

This is why Climate Risk Analytics becomes a lever to strengthen, adapt, and evolve the systems on which services and territories depend.  

What is climate risk 

Climate risk refers to the probability that climate change–related events generate negative impacts on energy networks, transport systems, telecommunications, and critical physical assets. 

It can be analyzed across different categories: 

• Physical risks, either acute (sudden events such as floods, wildfires, or storms) or chronic (gradual phenomena such as rising temperatures or drought) 

• Transition risks, linked to changes in regulations, technologies, and markets as the energy and industrial system evolves 

• Liability risks, arising from legal and financial consequences when damages and losses are attributed to failure to prevent or manage risk 

Through a thorough analysis of climate risk, knowledge can be turned into a strategy capable of guiding decisions, actions, and investments over time.  

From risk to resilience: why we need to act now 

The climate is changing faster than many infrastructures can adapt, increasing exposure to extreme events. 

When a power line fails, a transport network is disrupted, or a facility is under stress, the issue affects service continuity, community impact, and public trust. 

This is why climate risk is now directly linked to the concept of resilience. It means protecting what exists while preparing systems and assets to operate under conditions different from those of the past. 

This requires a shift in approach: from reacting to events to making decisions before they occur. 

What is Climate Risk Analytics 

Climate Risk Analytics is a process that combines data, predictive models, and forward-looking scenarios to estimate the impact of climate on infrastructure and assets. 

It does not simply describe risk. It makes it actionable by enabling: 

• Assessment of the real exposure of infrastructure 

• Estimation of physical and economic impacts 

• Comparison of future scenarios 

• Support for operational and strategic decisions 

The break from traditional approaches is clear. 

It moves beyond the use of historical data and static maps, building a dynamic, predictive, and asset-based understanding focused on networks in energy, telecommunications, and transport. 

How climate risk is measured 

Measuring climate risk involves: 

• Data collection – climate data, satellite data, and asset information 

• Exposure analysis – where and how assets are exposed to events 

• Vulnerability assessment – how sensitive an asset is to a given event 

• Impact modeling – estimation of physical and economic damages 

The most advanced models combine Earth Observation, machine learning, and climate models to simulate future scenarios. Even small variations can be detected and analyzed, helping trace new paths toward a more resilient world. 

Components of Climate Risk Analytics 

What is Climate Intelligence and why it matters today

Climate Intelligence refers to the set of methods and tools that transform complex climate data into actionable insights. 

It enables the identification of patterns, the interpretation of weak signals, and the anticipation of how climate risk distributes across networks and infrastructure. 

Scenario Analysis: how to assess future climate changes

Climate Change Scenario Analysis uses climate models to simulate possible future evolutions of climate risk. 

These simulations are based on different emission pathways and allow organizations to assess how risk will evolve over time. They are essential for long-term investment planning. 

What is a Climate Risk Index and how it is used

A Climate Risk Index is a composite indicator that measures the level of climate risk for a given area or asset. It may include variables such as event frequency, intensity, and vulnerability.  It is used to compare territories, prioritize interventions, and communicate risk clearly. 

How climate risk analysis software works 

Climate risk analysis software transforms complex data into maps and indicators that make visible how risk develops and evolves over time. 

There are two main approaches: 

• Static tools based on predefined datasets and point-in-time analysis 

• Dynamic models that are continuously updated and capable of simulating scenarios 

The most advanced systems use AI and satellite data to deliver high-resolution analysis and projections beyond 20 years, turning risk from a reactive element into a decision-making lever. 

New approaches to Climate Risk Analytics: Airis

Climate risk analysis can no longer rely solely on static maps and historical datasets from public authorities. This approach does not allow for estimating probability, intensity, or physical and economic impacts on assets and networks. 

Airis shifts the paradigm, moving from a geographic analysis to an asset-based perspective and positioning itself as an advanced climate risk analytics solution. 

By combining satellite data, physical models, and machine learning, it enables organizations to: 

• Visualize how risk is distributed across networks and critical nodes 

• Compare scenarios over time and understand their evolution 

• Translate risk into concrete impacts and usable metrics 

Airis is a climate resilience tool that turns complex data into operational decisions. 

Why Climate Risk Analytics matters for companies, investments, and ESG 

Climate risk is now embedded in the decisions that guide investments, value protection, and long-term management. 

Regulations (such as TCFD and CSRD) increasingly require organizations to assess and integrate climate risk into decision-making. Within ESG frameworks, this factor becomes central in measuring robustness and adaptive capacity over time. 

For those managing assets, networks, and infrastructure, this means: 

• Protecting asset value 

• Improving decision-making capabilities 

• Meeting regulatory requirements 

For investors and insurers, it enables a more accurate assessment of risk and return. 

In this context, Climate Risk Analytics becomes the lever for building strategies that are both robust and adaptable over time. 

FAQ

1. How is Climate Risk Analytics integrated into corporate decision-making processes?

   It is integrated into investment, planning, and asset management processes by providing data and scenarios that help assess risks and priorities before decisions are made.
   

2. What data is needed to start a climate risk analysis?

   Asset data, geographic information, and both historical and predictive climate data are required to assess exposure, vulnerability, and impacts.

3. How reliable are climate risk forecasts?

   They do not provide certainty, but probabilistic scenarios based on scientific models, useful for comparing options and making more informed decisions.

4. Which sectors benefit most from Climate Risk Analytics?

   Infrastructure, energy, transport, real estate, and finance are among the most exposed, as they depend on physical assets sensitive to climate events.

5. What is the difference between climate risk analysis and risk management?

   Analysis measures and interprets risk, while management defines the actions to reduce or adapt to it.