The market for climate risk assessment tools is experiencing rapid growth, driven by increasing awareness of environmental-related risks and regulatory pressures. According to research, the global climate risk assessment market is projected to reach $31.2 billion by 2030, growing at a compound annual growth rate (CAGR) of 17.5% between 2024 to 2030.1 This growth is fuelled by the rising adoption of AI and machine learning technologies in climate risk modelling and assessment.
The demand for these tools spans various sectors, including finance, insurance, agriculture, energy, and government. Investors are increasingly incorporating climate risk into their decision-making processes, while insurers are refining their risk models to account for changing climate patterns. Businesses across industries are seeking to understand and mitigate their exposure to climate-related risks, driving further market expansion.
AI Technologies Revolutionising Climate Risk Assessment
AI is transforming climate risk assessment through various technologies and approaches. Machine learning models analyse vast amounts of historical and real-time climate data to identify patterns and predict future trends. These models can process diverse data types, including satellite imagery, weather station readings, and socioeconomic indicators. Natural Language Processing (NLP) techniques are used to analyse climate-related news, research papers, and reports, extracting relevant information and sentiment to inform risk assessments.
Computer vision technology is applied to satellite and drone imagery to assess physical risks such as flood vulnerability, deforestation, and urban heat islands. Deep learning, through neural networks, is employed to model complex climate systems and their interactions with human activities, enabling more accurate long-term predictions. Reinforcement learning is used to optimise climate adaptation strategies by simulating various scenarios and their outcomes.
These technologies are often combined to create comprehensive climate risk assessment platforms that offer a range of functionalities, from asset-level risk scoring to portfolio-wide climate scenario analysis.
Key Players in the AI Climate Risk Assessment Space
This market features a mix of innovative start-ups and established companies. Among the start-ups, Jupiter Intelligence stands out. Founded in 2016, Jupiter provides climate risk analytics for resilience planning and risk management. The company raised $54 million in Series C funding in 2021, bringing its total funding to $84 million.2 Jupiter’s AI-powered platform offers high-resolution climate risk assessments for various sectors, including real estate, infrastructure, and agriculture. Their forward-looking analytics also drive comprehensive compliance for TCFD/CSRD and regulatory disclosures, making Jupiter a crucial player in the climate resilience landscape.
Another notable start-up is ClimateAi, whose platform has pioneered the application of artificial intelligence to climate change mitigation. The company secured $22 million in Series B funding in 2023.3 By applying AI to climate risk modelling, ClimateAi provides long-term climate forecasts and risk assessments for crop yields and supply chain disruptions, helping businesses identify the actions needed today to adapt to the climate change disruptions of tomorrow.
Adding to this innovative landscape is Risilience, a UK-based company that brings a unique approach to climate risk assessment and management. The company raised £21 million in Series B funding led by Quantam Capital Group.4 Risilience’s climate-analytics technology enables business transformation to a low-carbon economy through its award-winning, climate-intelligence SaaS platform. What sets Risilience apart is its integration of fourfold expertise centres: science, risk, business, and technology.
Among established companies, IBM offers the Environmental Intelligence Suite, which uses AI to help organisations assess and respond to climate and weather risks. IBM’s Watson AI capabilities are leveraged to provide advanced climate risk analytics and forecasting. Moody’s, the financial services company, has integrated climate risk assessment into its offerings through its acquisition of Four Twenty Seven, a climate risk data firm5 and Risk Management Solutions (RMS)6, a longstanding player in catastrophe risk modelling. Moody’s ESG Solutions now provides AI-powered climate risk screening and scoring for various asset classes.
These companies represent a fraction of the growing ecosystem of AI-powered climate risk assessment tools, with new entrants and innovations continually emerging.
Practical Applications: AI Climate Tools Across Industries
AI-powered climate risk assessment tools are finding applications across various sectors. Investment firms are using these tools to evaluate the climate risks associated with their portfolios. For example, BlackRock, the world’s largest asset manager, has integrated climate risk assessment into its Aladdin platform, using AI to analyse climate-related risks and opportunities across its investments.
Insurance companies are leveraging AI to refine their underwriting processes and price policies more accurately based on climate risks, as climate events become more frequent and their impacts become more extreme. For instance, Swiss Re uses machine learning models to assess flood risks and improve its catastrophe models. Companies across industries are employing AI-powered climate risk assessments to inform their strategic planning and risk management.
In the public sector, government agencies and local authorities are using these tools for urban planning, infrastructure development, and disaster preparedness. The UK Environment Agency, for instance, employs AI-powered flood forecasting systems to improve its flood warning capabilities.
AI’s Influence on Investment and Risk Management
The adoption of AI-powered climate risk assessment tools is having a significant impact on investment strategies and risk management practices. Investors are incorporating climate risk assessments into their due diligence processes, leading to more informed investment decisions. Asset managers are using AI-driven insights to optimise their portfolios for climate resilience, potentially leading to shifts in asset allocation.
The availability of granular climate risk data is enabling the development of new financial products, such as climate-resilient bonds and parametric insurance policies. Companies are using AI-powered tools to enhance their climate-related financial disclosures, responding to growing regulatory pressures and investor demands. AI is enabling more sophisticated climate scenario analysis, allowing organisations to stress-test their strategies against various climate futures.
These developments are contributing to a more holistic approach to risk management that considers both short-term and long-term climate-related risks.
Navigating Challenges: Limitations in AI-Driven Climate Risk Assessment
Despite the significant potential of these assessment tools, several challenges remain. The accuracy of AI models depends on the quality and comprehensiveness of input data. Climate data can be inconsistent, sparse, or biased, particularly in developing regions and systems are complex, and long-term predictions involve inherent uncertainties. Communicating these uncertainties effectively to decision-makers remains a challenge.
Some AI models, particularly deep learning systems, can be “black boxes,” making it difficult to understand and explain their decision-making processes. As climate risk disclosure regulations evolve, ensuring that AI-powered assessments align with regulatory requirements can be challenging. Many organisations face difficulties integrating AI-powered climate risk tools with their existing risk management and decision-making processes.
Addressing these challenges will be crucial for the continued development and adoption of AI-powered climate risk assessment tools.
The Evolution of AI in Climate Risk Analysis
Looking ahead, AI models are expected to provide even more detailed, asset-level climate risk assessments, enabling more targeted mitigation strategies. In addition, they twill become more adept at simulating complex climate scenarios, including cascading effects and feedback loops. AI is likely to be increasingly combined with other technologies, such as blockchain and the Internet of Things, to create more comprehensive risk assessment ecosystems. As these tools become more widely available and user-friendly, smaller organisations and individual investors may gain access to sophisticated climate risk assessments.
In addition to assessing risks, AI tools are expected to play a growing role in developing and optimising climate adaptation strategies. As climate change continues to reshape the global risk landscape, AI-powered climate risk assessment tools will play an increasingly vital role in helping organisations navigate this complex terrain. By providing data-driven insights and enabling more informed decision-making, these tools have the potential to enhance resilience and drive more sustainable practices across the economy.
The rapid growth of this market, coupled with ongoing technological advancements, suggests that AI will remain at the forefront of climate risk management for years to come. As these tools continue to evolve, they will undoubtedly play a crucial role in shaping our collective response to one of the most pressing challenges of our time.
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