Insights
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:
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:
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.
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.
| Characteristic | Traditional historical analysis | Forward-looking predictive models |
|---|---|---|
| Data source | Historical incident records and past time series | Geospatial data (Earth Observation) and climate projections |
| Time horizon | Retrospective (analysis of events that have already occurred) | Predictive (evolution scenarios projected up to 2050) |
| Decision-making capability | Reactive emergency management based on known events | Preventive planning of CAPEX and OPEX for extreme stresses |
| Spatial resolution | Aggregated at regional or provincial scale | High geographic granularity on the physical footprint of the asset |
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.
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.
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:
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.
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 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.
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:
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.
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.
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.
Cascading effects occur when the interruption of an essential service negatively impacts other connected infrastructure.
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.
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