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. 

Resilience to climate change: what everyone needs to know

In May 2023, Emilia-Romagna in Italy experienced severe flooding, affecting residents, businesses, and infrastructure. Roads and railways were disrupted for days. In November, a powerful storm hit Tuscany, causing industrial losses, hospital evacuations, and infrastructure damage. These events highlight the increasing frequency of natural disasters and their significant economic impact. As climate-induced events rise globally, the critical question is whether advanced technologies can help estimate, prepare for, and respond to such disasters. Eoliann believes they can, and this article explores how.

Beyond the Damage: Understanding Climate Risks

Natural disasters, while evident in their immediate destruction, have lasting impacts that go beyond the physical realm. According to the European Environment Agency, flood-related damages in Europe exceed €12 billion annually, with the cost of climate-induced events reaching an astounding €145 billion over ten years. And for the infrastructure companies, insurance companies and banks, these aren’t just statistics. They represent claims that need to be processed, businesses to be supported, and, ultimately, profits that are affected.

A reactive approach is not the solution. By solely acting after disasters unfold, we miss out on the broader financial and societal benefits of preemptive actions. In Emilia-Romagna, the reactive measures post-floods had to address an estimated €8.8 billion in damage. Harnessing innovative technological methodologies like those that Eoliann has introduced could be the required game-changer. Proactive strategies not only lead to significant cost savings but also can effectively shield communities.

As the climate’s volatility continues to persist, the underlying challenge extends even beyond that. It challenges the very methodology of risk assessment. The reliance on historical data is not effective. With the ever-changing climate patterns and increasing severity of natural events, relying solely on the past to predict the future is prone to underestimation. Are traditional systems for risk assessment robust enough to adapt to the ever-intensifying climate challenges?

Artificial Intelligence: The Eyes in the Sky

This is where the power of technology comes into play. Advanced tools such as AI and Earth Observation Satellites are revolutionizing our approach to climate risk estimation.. These aren’t mere weather forecasting tools; they analyze patterns and trends, helping businesses make informed decisions well in advance. With the advent of powerful models, we can now anticipate potential risks rather than merely react to them.

For instance, by analyzing the morphological, topological, atmospheric and urbanistic data, AI can help forecast potential flood zones or areas vulnerable to wildfires. Moreover, Earth Observation Satellites offer another layer of accuracy. Orbiting our planet, these satellites provide real-time data, capturing atmospheric anomalies and man-made changes.

Eoliann has been at the forefront of advancing risk assessment methodologies tailored for the modern world. Our solution involves combining AI with geospatial analytics. As traditional models can be limiting in the face of rapid climatic changes, our approach, on the other hand, integrates real-time environmental data streams, allowing for dynamic risk assessment. Delving deeper into how the systems function, we can break down the key components of the risk assessment:

• AI-Driven Analysis: Advanced Artificial Intelligence models, trained on decades of data, can forecast the probability of extreme weather events with increased accuracy. For vulnerable regions, it could mean the difference between safety and disaster.
• Earth Observation Satellites: With the data being updated in real-time, it allows to foster the decision-making process that would ensure minimal service disruption.
• Granular Risk Analysis: By combining the strengths of AI and satellite technology, we can gain a nuanced understanding of risk as well as cover more remote areas. Such insights empower regions to build resilient infrastructures and businesses to prepare.

The Road Ahead

While the scars of events like Emilia-Romagna are deep, they highlight the pressing need for a reliable solution. With the rise of global temperatures in the next decade, more extreme weather events will occur, from scorching heat waves, floods and wildfires in regions like Europe to intensified cyclonic activities in the tropics. The seamless integration of AI-driven models and real-time satellite data paves the way for a responsive, resilient, and proactive system. And by integrating AI-driven risk models and satellite data, organizations across various sectors can take more proactive role, strengthening economies and communities against unforeseen challenges.

In the broader perspective, it’s more than merely adopting a new technology. It’s about entering a new era where smart data insights blend with human tactics, building a world that can adapt and thrive despite our planet’s changing patterns.