As climate change accelerates, the world faces staggering economic uncertainty. In just two decades, global warming has driven over $600 billion in insurance losses. Without swift and strategic adaptation, physical climate risks could impose $1.2 trillion in annual costs on the world’s largest companies, devalue global housing by up to $25 trillion, and place €1.3 trillion in Eurozone loans at risk by 2050. In response, insurers are already retreating from high-risk geographies, leaving critical assets exposed.
The only viable path forward is to integrate climate risk into regulatory, financial, and operational decision-making. Fortunately, Climate Tech innovation has caught up with the complexity of the crisis. A new generation of commercially deployable products, from real-time analytics platforms to dynamic risk modeling tools, is moving risk assessment beyond historical models and generic exposure metrics. These innovations enable real-time monitoring, forward-looking projections, and asset-level insights that are essential in a warming world.
To help stakeholders navigate the fast-growing innovation landscape, the Net Zero Insights Climate Tech Taxonomy offers a structured, multi-layered framework that classifies Climate Risk Assessment solutions across two core product verticals: Climate Risk Modeling and Climate Risk Analytics Platforms.
By organizing innovations into product-focused categories, this taxonomy enables clear mapping of startups and technologies based on their capabilities, whether they’re building geospatial platforms, predictive models, or supply chain risk analytics.
The following section explores four key technological advancements that are shaping the future of Climate Risk Assessment. These innovations are being extensively applied across the two core verticals – climate risk modeling and climate risk analytics platforms, improving their precision, scalability, and impact.
Artificial intelligence
Artificial Intelligence enables faster, more accurate analysis of vast and complex climate datasets. Satellite data isn’t sufficient since it’s difficult to scale and lacks real-time processing.
AI bridges gaps in climate risk assessment by:
- identifying patterns across environmental, socio-economic, and infrastructure data
- improving the accuracy of climate models
- automating many day-to-day risk management tasks
- generating granular localized insights using data from satellite imagery, drones, and sensor networks
Machine learning
Machine Learning (ML) helps overcome climate risk assessment limitations by:
- Accurately predicting long-term climate projections even with short-term or incomplete data
- holistically assess risk with dynamic modeling when multiple climate hazards overlap, such as extreme heat, drought, and food insecurity. It is achieved by leveraging real-time and spatio-temporal data from satellites, drones, sensors, and even social media.
- automatically identify and tag critical assets such as utilities or corporate assets along with asset-level features like cooling systems or emissions controls helping in exposure mapping.
Satellite data
Satellite data provides a high-resolution, real-time view of environmental changes and asset exposure across geographies. Some of the advantages of relying on satellite data in climate risk assessment are:
- Offers a scalable and cost-effective way to assess vulnerability at the asset level where on-the-ground data is sparse or unreliable. This enables more accurate risk profiling of industries or farmlands to help identify climate hazard zones for resilience planning and emergency response.
- When combined with machine learning, remote sensing data is improving the precision and frequency of data collection. This helps detect asset features such as land use, asset-level greenhouse gas estimates and physical risk exposure.
Climate modeling
Climate modeling is a method for simulating the Earth’s physical climate system using complex computer models. These models monitor the atmosphere, oceans, land surface, and ice sheet to analyze how energy and matter move through the Earth system.
By measuring parameters such as temperature, ocean currents, and atmospheric pressure etc., over time, climate models can compute climate dynamics across multiple time scales from seasonal variability to century-long trends.
One of the unique strengths of climate models is their ability to run simulations under different hypothetical scenarios. For instance, scientists can adjust levels of greenhouse gases or simulate warming sea surface temperatures to assess their specific effects on global or regional climate systems and provide granular insights for climate risk assessment.
Innovation landscape in climate risk assessment
Climate technologies embedded across the climate risk assessment value chain are divided into two core verticals – Climate Risk Modelling and Climate Risk Analytics Platforms, as defined in the Net Zero Insights Climate Tech Taxonomy. This taxonomy is used to systematically classify companies based on the specific solutions they offer across the CRA ecosystem.
Many Climate Tech companies have adopted the four technological advancements discussed above, i.e., AI, ML, satellite data, and climate modelling in various capacities to bring climate risk assessment technology from academic circles to market-ready applications. Net Zero Insights tracks and classifies these companies based on the products they develop, their challenge areas, and their alignment with core risk analysis functions.
Climate Risk Analytics Platform
Climate Risk Analytics Platforms are cloud-based tools that enable organizations to assess, quantify, and disclose climate-related risks in real time. Delivered as Software-as-a-Service (SaaS), these platforms offer on-demand, location-specific analysis across a wide range of physical and transition risks.
Companies in this category are tracked in our taxonomy for their ability to deliver actionable insights across use cases such as business operations, credit risk modeling, investment due diligence, asset planning, and supply chain resilience.
The Climate Tech Taxonomy divides Climate Risk Analytics Platform into climate financial risk analytics and supply chain climate risk analytics, which helps identify the specific business functions these platforms support..
Climate Financial Risk Analytics
Climate Financial Risk Analytics translates the physical impacts of climate change into measurable financial risks to assets, markets, and institutions. With growing regulatory and investor pressure, there is increasing demand for robust methods to quantify these risks and integrate them into financial decision-making frameworks.
These tools are categorized in our taxonomy based on how they convert projected climate impacts into financial metrics such as dollar-value losses, insurance risks, and property value depreciation. Companies building solutions in this space help investors and financial institutions gauge the vulnerability of their portfolios under different climate scenarios and make more informed strategic decisions.
Supply Chain Climate Risk Analytics
Supply chains are critical vectors through which climate risk propagates. It has a separate category in the Climate Tech taxonomy for their sole focus on identifying and quantifying climate risk across upstream and downstream value chains.
While most organizations focus on direct physical risks to their own assets, the climate vulnerability of suppliers, manufacturers, distributors, and storage facilities can be just as consequential.
Extreme weather events can damage infrastructure, delay production, disrupt logistics, and impact the quality and availability of raw materials. For example, the 2011 floods in Thailand led to $46.5 billion in economic losses, with 70% of damages hitting the manufacturing sector. Car makers, electronics firms, and agricultural exporters experienced major disruptions demonstrating how local climate events can have cascading global effects.
Understanding a company’s exposure requires examining its entire value chain including the sector, location, asset type, and operational characteristics of its suppliers. For instance:
- In the food and agriculture sector, production stages are especially vulnerable to drought and wildfire, while storage infrastructure is more susceptible to floods.
- Similarly, water-intensive industries like food and beverage processing face heightened risks in drought-prone areas.
To assess vulnerability effectively, it’s crucial to map out key suppliers, evaluate their climate exposure, and understand the materiality of their role in the production process.
Climate Risk Modeling
As climate change accelerates, organizations can no longer rely solely on historical data to assess risk. Climate risk modeling, as categorized by Net Zero Insights, includes companies that develop forward-looking simulation models integrating physical climate data with financial, socio-economic, and infrastructure variables.
This product vertical includes modeling platforms that help users understand how long-term climate hazards will evolve under different emission trajectories. These companies are tracked in our taxonomy based on factors like modeling approach, spatial resolution, use of real-time inputs, and applicability across sectors.
The next generation of climate models increasingly leverages dynamic data from satellites, sensors, and IoT devices. By classifying companies in this way, our taxonomy helps identify those best equipped to provide granular, actionable insights for portfolio stress-testing, supply chain exposure analysis, and long-term resilience planning.
Explore the innovators driving climate risk intelligence
Discover some of the leading Climate Tech startups leveraging advanced tools like satellite data, machine learning, and climate modeling to deliver accurate climate risk insights. These innovators are equipping businesses with the data they need to embed physical climate risk into financial planning, real estate decisions, and global supply chain strategies.
Climate risk assessment evolves from disclosure to decision-making
The accelerating pace of climate change is pushing global systems beyond the limits of conventional risk management. Climate risk management which was once a niche concern of the sustainability team is now an enterprise-wide risk function.
Today, it is an accurate data-backed discipline powered by advances in AI, satellite sensing, machine learning, and probabilistic climate modeling. Stakeholders should leverage these tools to quantify asset-level risk, evaluate exposure across geographies, and guide capital allocation under multiple climate scenarios.
As regulators worldwide begin mandating risk disclosures, access to high-quality, location-specific climate data is foundational to sound financial and economic planning. It enables the preservation of asset value, the avoidance of stranded investments, and the integration of resilience into every capital or long-term planning decision.
Those who act now can convert valuable risk insights into their strategic advantage and help build a financial system that’s fit for a climate-constrained future.
Interested in learning more about the technologies and startups leading the change? Book a free trial of our platform to explore the innovations in climate risk assessment in detail, uncover emerging trends, and gain actionable insights to stay ahead in this rapidly evolving space.
