Case studies
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.
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:
Through a thorough analysis of climate risk, knowledge can be turned into a strategy capable of guiding decisions, actions, and investments over time.
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.
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:
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.
Measuring climate risk involves:
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.
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.
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.
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.
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:
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.
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:
Airis is a climate resilience tool that turns complex data into operational decisions.
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:
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.
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.
Asset data, geographic information, and both historical and predictive climate data are required to assess exposure, vulnerability, and impacts.
They do not provide certainty, but probabilistic scenarios based on scientific models, useful for comparing options and making more informed decisions.
Infrastructure, energy, transport, real estate, and finance are among the most exposed, as they depend on physical assets sensitive to climate events.
Analysis measures and interprets risk, while management defines the actions to reduce or adapt to it.
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