AI-Based Climate Modelling Market Report

Market Overview and Sustainable Development Goals (SDGs) Alignment

The AI-Based Climate Modelling Market was valued at USD 242.0 million in 2023 and is projected to reach USD 1715.2 million by 2032, growing at a compound annual growth rate (CAGR) of 34.32% from 2024 to 2032. This growth supports several United Nations Sustainable Development Goals (SDGs), including:

• SDG 13: Climate Action – by enhancing climate prediction and disaster risk reduction capabilities.
• SDG 9: Industry, Innovation, and Infrastructure – through technological advancements in AI and cloud computing.
• SDG 11: Sustainable Cities and Communities – by improving weather forecasting and disaster preparedness.

Regional Market Insights

1. North America: The largest market due to strong federal investments in climate science, advanced AI platforms, and integration with national forecasting and disaster preparedness systems.
2. Asia-Pacific: The fastest-growing region driven by escalating climate vulnerability and increased investments in AI-based modelling in countries such as China, India, Japan, and Southeast Asia. This growth aids SDG 13 by addressing climate risks like monsoons, typhoons, floods, and urban heat.

Market Segmentation and Growth Drivers

By Component

• Software: Dominated the market in 2023 with 82% revenue share, providing modelling platforms, algorithm toolkits, and simulation environments essential for climate research and operational work.
• Services: Fastest-growing segment, including system integration, consulting, cloud deployment, customization, and model validation. This growth supports SDG 17 (Partnerships for the Goals) by fostering collaboration among stakeholders.

By Deployment

• Cloud: Held 65% revenue share in 2023 due to scalable, high-performance computing capabilities that facilitate complex simulations with cost-effectiveness.
• On-Premises: Rapid growth driven by government and research institutions requiring data control, low-latency processing, and regulatory compliance, enhancing data security and privacy.

By Technology

• Machine Learning: Market leader in 2023 due to maturity and effectiveness in time-series prediction and anomaly detection.
• Deep Learning: Fastest-growing technology segment, leveraging neural networks to analyze complex climate data, contributing to improved forecast accuracy and scenario analysis.

By Application

• Weather Forecasting: Leading application segment supporting daily meteorological operations and public safety.
• Disaster Risk Reduction: Rapidly expanding application that utilizes AI-driven models to simulate natural disasters, improving community preparedness and resilience, directly supporting SDG 13 and SDG 11.

Key Market Players

• IBM – IBM Environmental Intelligence Suite
• Microsoft – Microsoft Planetary Computer
• Google – Google Earth Engine
• The Climate Corporation (Bayer) – Climate FieldView
• Accenture – Climate Analytics Platform
• AWS (Amazon Web Services) – Amazon Sustainability Data Initiative (ASDI)
• Tomorrow.io – Tomorrow.io Weather Engine
• Oracle – Oracle Climate Change Analytics
• Climavision – Climavision Climate Data Services
• Planet Labs – PlanetScope
• Descartes Labs – Descartes Labs Platform
• Cervest – EarthScan
• Jupiter Intelligence – ClimateScore Global
• One Concern – Domino Climate Platform
• ClimateAi – ClimateAi Analytics

Market Scope and Forecast

Conclusion

The AI-Based Climate Modelling Market is poised for significant growth, driven by technological innovation and increasing demand for climate resilience solutions. The market’s development aligns closely with multiple Sustainable Development Goals, particularly SDG 13 (Climate Action), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 11 (Sustainable Cities and Communities). The integration of AI technologies in climate modelling enhances predictive capabilities, disaster risk reduction, and environmental monitoring, contributing to global efforts to combat climate change and build sustainable communities.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 13: Climate Action
   • The article focuses on AI-based climate modelling to improve weather forecasting, climate prediction, and disaster risk reduction, directly supporting climate action.
2. SDG 9: Industry, Innovation and Infrastructure
   • The integration of AI technologies such as machine learning and deep learning in climate modelling reflects innovation in infrastructure and industry.
3. SDG 11: Sustainable Cities and Communities
   • Disaster risk reduction applications help cities and communities prepare for and mitigate the impact of extreme weather events.
4. SDG 7: Affordable and Clean Energy
   • Energy sector forecasting needs mentioned in the article relate to ensuring sustainable energy systems.

2. Specific Targets Under Those SDGs Identified

1. SDG 13: Climate Action
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters.
   • Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including AI and data analytics for climate modelling.
3. SDG 11: Sustainable Cities and Communities
   • Target 11.5: Significantly reduce the number of deaths and the number of people affected by disasters, including water-related disasters.
4. SDG 7: Affordable and Clean Energy
   • Target 7.a: Enhance international cooperation to facilitate access to clean energy research and technology.

3. Indicators Mentioned or Implied to Measure Progress

1. SDG 13 Indicators
   • Number of countries with national and local disaster risk reduction strategies (implied through disaster risk reduction applications).
   • Proportion of local governments adopting climate adaptation plans (implied by government use of AI-driven climate models).
2. SDG 9 Indicators
   • Research and development expenditure as a proportion of GDP (implied by federal investments and technological advancements in AI climate modelling).
   • Number of scientific and technological publications related to climate modelling (implied by growing AI-based climate modelling market and innovation).
3. SDG 11 Indicators
   • Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population (implied through disaster risk reduction improvements).
   • Proportion of urban population living in areas with disaster risk reduction strategies (implied by adoption of AI climate models in urban planning).
4. SDG 7 Indicators
   • International financial flows to clean energy research and development (implied by federal investments and energy sector forecasting needs).

4. Table: SDGs, Targets and Indicators

Source: globenewswire.com