AI-based Climate Modelling Market Forecasts to 2032 – Global Analysis By Component (Software and Services), Technology, Deployment Mode, Application, End User and By Geography
Description
According to Stratistics MRC, the Global AI-based Climate Modelling Market is accounted for $425.2 million in 2025 and is expected to reach $1906.0 million by 2032 growing at a CAGR of 23.9% during the forecast period. AI-based climate modelling refers to the use of artificial intelligence and machine learning algorithms to simulate, predict, and analyze climate systems and their future changes. Unlike traditional models that rely solely on physics-based equations, AI-driven models learn patterns from large datasets, including satellite observations, weather records, and oceanic data, to enhance prediction accuracy and computational efficiency. These models can capture complex, nonlinear relationships within the climate system, enabling faster forecasting of extreme weather events, temperature variations, and carbon emissions. By integrating AI, scientists can improve climate resilience planning, policy development, and global efforts to mitigate and adapt to climate change.
Market Dynamics:
Driver:
Increasing frequency and severity of climate-extreme events
Governments and enterprises require predictive tools to assess risks from floods droughts wildfires and cyclones with greater accuracy and lead time. Platforms use satellite data historical records and real-time feeds to simulate weather patterns and environmental stressors. Integration with early warning systems and infrastructure planning enhances disaster preparedness and resource allocation. Demand for scalable and adaptive modelling is rising across agriculture insurance energy and urban planning. These dynamics are propelling platform innovation across climate risk intelligence and mitigation ecosystems.
Restraint:
Shortage of specialised domain expertise and integration challenges
AI deployment requires cross-disciplinary skills in climatology data science and geospatial analytics which remain scarce across many regions. Enterprises face challenges in aligning legacy systems with AI engines and ensuring interoperability across data formats and modelling frameworks. Lack of standardized protocols and training programs hampers workforce readiness and model reliability. Integration with policy tools and stakeholder workflows remains fragmented and resource-intensive. These constraints continue to hinder adoption across decentralized and infrastructure-limited climate modelling environments.
Opportunity:
Cross-sector demand in agriculture, energy & insurance
Farmers use predictive models to optimize irrigation crop selection and pest control under shifting climate conditions. Energy providers deploy simulations to manage grid resilience renewable integration and extreme weather risks. Insurers leverage climate analytics to assess exposure price risk and design parametric products across vulnerable geographies. Platforms support scenario planning carbon tracking and adaptation strategies tailored to industry-specific needs. Demand for modular and interoperable modelling tools is rising across public agencies and commercial enterprises. These trends are fostering growth across multi-sector climate intelligence platforms.
Threat:
Unequal access & scalability issues
High-performance computing data infrastructure and skilled personnel are concentrated in high-income economies limiting global reach and equity. Smaller nations and local agencies face challenges in accessing real-time data cloud platforms and technical support for AI deployment. Lack of inclusive datasets and regional calibration degrades model accuracy and relevance across diverse geographies. Funding gaps and policy fragmentation further constrain platform diffusion and stakeholder engagement. These limitations continue to restrict platform maturity and climate resilience planning across underserved regions.
Covid-19 Impact:
The pandemic disrupted climate research field data collection and infrastructure investment across modelling programs. Lockdowns delayed satellite calibration sensor deployment and international collaboration on climate datasets. However post-pandemic recovery emphasized resilience planning environmental monitoring and digital transformation across climate-sensitive sectors. Investment in remote sensing cloud computing and AI-driven analytics surged across public health and disaster response initiatives. Public awareness of systemic risk and environmental interdependencies increased across consumer and policy circles. These shifts are reinforcing long-term investment in AI-based climate modelling infrastructure and cross-sector integration.
The machine learning segment is expected to be the largest during the forecast period
The machine learning segment is expected to account for the largest market share during the forecast period due to its versatility scalability and performance across climate modelling workflows. Platforms use supervised and unsupervised models to detect anomalies simulate weather patterns and optimize resource allocation. Integration with satellite feeds IoT sensors and historical datasets enhances prediction accuracy and spatial resolution. Demand for adaptive and explainable AI is rising across agriculture energy insurance and urban planning. Vendors offer modular engines APIs and visualization tools to support cross-functional adoption and policy alignment. These capabilities are boosting segment dominance across AI-driven climate modelling platforms.
The disaster risk prediction & resilience planning segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the disaster risk prediction & resilience planning segment is predicted to witness the highest growth rate as climate modelling platforms expand across emergency response infrastructure design and policy frameworks. Platforms simulate hazard scenarios assess vulnerability and guide investment in resilient systems across flood zones drought-prone areas and wildfire corridors. Integration with geospatial data early warning systems and community engagement tools enhances preparedness and recovery. Demand for scalable and locally adapted modelling is rising across municipalities insurers and development agencies. These dynamics are accelerating growth across resilience-focused climate modelling platforms and services.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share due to its advanced research infrastructure institutional investment and regulatory engagement across climate modelling technologies. Enterprises and agencies deploy AI platforms across agriculture energy insurance and urban planning to manage climate risk and inform policy. Investment in satellite networks cloud platforms and geospatial analytics supports scalability and precision. Presence of leading vendors academic institutions and climate research centers drives innovation and standardization. Firms align modelling strategies with federal mandates ESG reporting and resilience planning frameworks.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as climate exposure urbanization and digital infrastructure converge across regional economies. Countries like India China Japan and Indonesia scale climate modelling platforms across agriculture disaster response and energy planning. Government-backed programs support AI adoption data infrastructure and startup incubation across climate-sensitive sectors. Local providers offer multilingual mobile-first and regionally adapted solutions tailored to hazard profiles and regulatory needs. Demand for scalable and proactive modelling infrastructure is rising across public agencies insurers and energy providers. These trends are accelerating regional growth across AI-based climate modelling innovation and deployment.
Key players in the market
Some of the key players in AI-based Climate Modelling Market include International Business Machines Corporation (IBM), Microsoft Corporation, Google LLC, Amazon.com Inc., The Climate Corporation, Tomorrow.io Inc., Descartes Labs Inc., ClimateAi Inc., Spire Global Inc., OpenClimate Network, ClimaCell Inc., DeepMind Technologies Limited, Planet Labs PBC, Sust Global Inc. and One Concern Inc.
Key Developments:
In March 2025, Amazon expanded its AI-based sustainability tools built on AWS, enabling real-time modeling of energy usage, emissions, and water consumption across its global operations. These tools supported Amazon’s Climate Pledge by optimizing logistics, packaging, and data center efficiency, helping the company reduce its carbon footprint and improve resource allocation.
In February 2025, Microsoft published its report Accelerating Sustainability with AI, introducing new tools for climate risk modeling, carbon accounting, and energy optimization. These platforms integrated with Azure and Microsoft Cloud for Sustainability, enabling enterprises to simulate climate scenarios and improve ESG performance. The launch reinforced Microsoft’s role in AI-native climate intelligence.
Components Covered:
• Software
• Services
Technologies Covered:
• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing (NLP)
• Other Technologies
Deployment Modes Covered:
• Cloud-Based
• On-Premise
Applications Covered:
• Climate Forecasting & Modeling
• Carbon Emissions Monitoring & Reporting
• Disaster Risk Prediction & Resilience Planning
• Agricultural Yield & Crop Health Prediction
• Urban Heat Island & Air Quality Analysis
• Water Resource & Flood Management
• Other Applications
End Users Covered:
• Government & Public Sector Agencies
• Research & Academic Institutions
• Insurance & Reinsurance Companies
• Energy & Utilities
• Agriculture & Forestry
• Transportation & Logistics
• Other End Users
Regions Covered:
• North AmericaUSCanadaMexico
• EuropeGermanyUKItalyFranceSpainRest of Europe
• Asia PacificJapan China India Australia New ZealandSouth KoreaRest of Asia Pacific
• South AmericaArgentinaBrazilChileRest of South America
• Middle East & Africa Saudi ArabiaUAEQatarSouth AfricaRest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Market Dynamics:
Driver:
Increasing frequency and severity of climate-extreme events
Governments and enterprises require predictive tools to assess risks from floods droughts wildfires and cyclones with greater accuracy and lead time. Platforms use satellite data historical records and real-time feeds to simulate weather patterns and environmental stressors. Integration with early warning systems and infrastructure planning enhances disaster preparedness and resource allocation. Demand for scalable and adaptive modelling is rising across agriculture insurance energy and urban planning. These dynamics are propelling platform innovation across climate risk intelligence and mitigation ecosystems.
Restraint:
Shortage of specialised domain expertise and integration challenges
AI deployment requires cross-disciplinary skills in climatology data science and geospatial analytics which remain scarce across many regions. Enterprises face challenges in aligning legacy systems with AI engines and ensuring interoperability across data formats and modelling frameworks. Lack of standardized protocols and training programs hampers workforce readiness and model reliability. Integration with policy tools and stakeholder workflows remains fragmented and resource-intensive. These constraints continue to hinder adoption across decentralized and infrastructure-limited climate modelling environments.
Opportunity:
Cross-sector demand in agriculture, energy & insurance
Farmers use predictive models to optimize irrigation crop selection and pest control under shifting climate conditions. Energy providers deploy simulations to manage grid resilience renewable integration and extreme weather risks. Insurers leverage climate analytics to assess exposure price risk and design parametric products across vulnerable geographies. Platforms support scenario planning carbon tracking and adaptation strategies tailored to industry-specific needs. Demand for modular and interoperable modelling tools is rising across public agencies and commercial enterprises. These trends are fostering growth across multi-sector climate intelligence platforms.
Threat:
Unequal access & scalability issues
High-performance computing data infrastructure and skilled personnel are concentrated in high-income economies limiting global reach and equity. Smaller nations and local agencies face challenges in accessing real-time data cloud platforms and technical support for AI deployment. Lack of inclusive datasets and regional calibration degrades model accuracy and relevance across diverse geographies. Funding gaps and policy fragmentation further constrain platform diffusion and stakeholder engagement. These limitations continue to restrict platform maturity and climate resilience planning across underserved regions.
Covid-19 Impact:
The pandemic disrupted climate research field data collection and infrastructure investment across modelling programs. Lockdowns delayed satellite calibration sensor deployment and international collaboration on climate datasets. However post-pandemic recovery emphasized resilience planning environmental monitoring and digital transformation across climate-sensitive sectors. Investment in remote sensing cloud computing and AI-driven analytics surged across public health and disaster response initiatives. Public awareness of systemic risk and environmental interdependencies increased across consumer and policy circles. These shifts are reinforcing long-term investment in AI-based climate modelling infrastructure and cross-sector integration.
The machine learning segment is expected to be the largest during the forecast period
The machine learning segment is expected to account for the largest market share during the forecast period due to its versatility scalability and performance across climate modelling workflows. Platforms use supervised and unsupervised models to detect anomalies simulate weather patterns and optimize resource allocation. Integration with satellite feeds IoT sensors and historical datasets enhances prediction accuracy and spatial resolution. Demand for adaptive and explainable AI is rising across agriculture energy insurance and urban planning. Vendors offer modular engines APIs and visualization tools to support cross-functional adoption and policy alignment. These capabilities are boosting segment dominance across AI-driven climate modelling platforms.
The disaster risk prediction & resilience planning segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the disaster risk prediction & resilience planning segment is predicted to witness the highest growth rate as climate modelling platforms expand across emergency response infrastructure design and policy frameworks. Platforms simulate hazard scenarios assess vulnerability and guide investment in resilient systems across flood zones drought-prone areas and wildfire corridors. Integration with geospatial data early warning systems and community engagement tools enhances preparedness and recovery. Demand for scalable and locally adapted modelling is rising across municipalities insurers and development agencies. These dynamics are accelerating growth across resilience-focused climate modelling platforms and services.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share due to its advanced research infrastructure institutional investment and regulatory engagement across climate modelling technologies. Enterprises and agencies deploy AI platforms across agriculture energy insurance and urban planning to manage climate risk and inform policy. Investment in satellite networks cloud platforms and geospatial analytics supports scalability and precision. Presence of leading vendors academic institutions and climate research centers drives innovation and standardization. Firms align modelling strategies with federal mandates ESG reporting and resilience planning frameworks.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as climate exposure urbanization and digital infrastructure converge across regional economies. Countries like India China Japan and Indonesia scale climate modelling platforms across agriculture disaster response and energy planning. Government-backed programs support AI adoption data infrastructure and startup incubation across climate-sensitive sectors. Local providers offer multilingual mobile-first and regionally adapted solutions tailored to hazard profiles and regulatory needs. Demand for scalable and proactive modelling infrastructure is rising across public agencies insurers and energy providers. These trends are accelerating regional growth across AI-based climate modelling innovation and deployment.
Key players in the market
Some of the key players in AI-based Climate Modelling Market include International Business Machines Corporation (IBM), Microsoft Corporation, Google LLC, Amazon.com Inc., The Climate Corporation, Tomorrow.io Inc., Descartes Labs Inc., ClimateAi Inc., Spire Global Inc., OpenClimate Network, ClimaCell Inc., DeepMind Technologies Limited, Planet Labs PBC, Sust Global Inc. and One Concern Inc.
Key Developments:
In March 2025, Amazon expanded its AI-based sustainability tools built on AWS, enabling real-time modeling of energy usage, emissions, and water consumption across its global operations. These tools supported Amazon’s Climate Pledge by optimizing logistics, packaging, and data center efficiency, helping the company reduce its carbon footprint and improve resource allocation.
In February 2025, Microsoft published its report Accelerating Sustainability with AI, introducing new tools for climate risk modeling, carbon accounting, and energy optimization. These platforms integrated with Azure and Microsoft Cloud for Sustainability, enabling enterprises to simulate climate scenarios and improve ESG performance. The launch reinforced Microsoft’s role in AI-native climate intelligence.
Components Covered:
• Software
• Services
Technologies Covered:
• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing (NLP)
• Other Technologies
Deployment Modes Covered:
• Cloud-Based
• On-Premise
Applications Covered:
• Climate Forecasting & Modeling
• Carbon Emissions Monitoring & Reporting
• Disaster Risk Prediction & Resilience Planning
• Agricultural Yield & Crop Health Prediction
• Urban Heat Island & Air Quality Analysis
• Water Resource & Flood Management
• Other Applications
End Users Covered:
• Government & Public Sector Agencies
• Research & Academic Institutions
• Insurance & Reinsurance Companies
• Energy & Utilities
• Agriculture & Forestry
• Transportation & Logistics
• Other End Users
Regions Covered:
• North AmericaUSCanadaMexico
• EuropeGermanyUKItalyFranceSpainRest of Europe
• Asia PacificJapan China India Australia New ZealandSouth KoreaRest of Asia Pacific
• South AmericaArgentinaBrazilChileRest of South America
• Middle East & Africa Saudi ArabiaUAEQatarSouth AfricaRest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Table of Contents
200 Pages
- 1 Executive Summary
- 2 Preface
- 2.1 Abstract
- 2.2 Stake Holders
- 2.3 Research Scope
- 2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
- 2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions
- 3 Market Trend Analysis
- 3.1 Introduction
- 3.2 Drivers
- 3.3 Restraints
- 3.4 Opportunities
- 3.5 Threats
- 3.6 Technology Analysis
- 3.7 Application Analysis
- 3.8 End User Analysis
- 3.9 Emerging Markets
- 3.10 Impact of Covid-19
- 4 Porters Five Force Analysis
- 4.1 Bargaining power of suppliers
- 4.2 Bargaining power of buyers
- 4.3 Threat of substitutes
- 4.4 Threat of new entrants
- 4.5 Competitive rivalry
- 5 Global AI-based Climate Modelling Market, By Component
- 5.1 Introduction
- 5.2 Software
- 5.2.1 AI Modeling Platforms
- 5.2.2 Data Management & Integration Tools
- 5.2.3 Visualization & Reporting Dashboards
- 5.2.4 APIs & Simulation Engines
- 5.3 Services
- 5.3.1 Consulting & Strategy
- 5.3.2 Model Development & Training
- 5.3.3 Deployment & Integration
- 5.3.4 Managed Services
- 6 Global AI-based Climate Modelling Market, By Technology
- 6.1 Introduction
- 6.2 Machine Learning
- 6.3 Deep Learning
- 6.4 Computer Vision
- 6.5 Natural Language Processing (NLP)
- 6.6 Other Technologies
- 7 Global AI-based Climate Modelling Market, By Deployment Mode
- 7.1 Introduction
- 7.2 Cloud-Based
- 7.3 On-Premise
- 8 Global AI-based Climate Modelling Market, By Application
- 8.1 Introduction
- 8.2 Climate Forecasting & Modeling
- 8.3 Carbon Emissions Monitoring & Reporting
- 8.4 Disaster Risk Prediction & Resilience Planning
- 8.5 Agricultural Yield & Crop Health Prediction
- 8.6 Urban Heat Island & Air Quality Analysis
- 8.7 Water Resource & Flood Management
- 8.8 Other Applications
- 9 Global AI-based Climate Modelling Market, By End User
- 9.1 Introduction
- 9.2 Government & Public Sector Agencies
- 9.3 Research & Academic Institutions
- 9.4 Insurance & Reinsurance Companies
- 9.5 Energy & Utilities
- 9.6 Agriculture & Forestry
- 9.7 Transportation & Logistics
- 9.8 Other End Users
- 10 Global AI-based Climate Modelling Market, By Geography
- 10.1 Introduction
- 10.2 North America
- 10.2.1 US
- 10.2.2 Canada
- 10.2.3 Mexico
- 10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 Italy
- 10.3.4 France
- 10.3.5 Spain
- 10.3.6 Rest of Europe
- 10.4 Asia Pacific
- 10.4.1 Japan
- 10.4.2 China
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 New Zealand
- 10.4.6 South Korea
- 10.4.7 Rest of Asia Pacific
- 10.5 South America
- 10.5.1 Argentina
- 10.5.2 Brazil
- 10.5.3 Chile
- 10.5.4 Rest of South America
- 10.6 Middle East & Africa
- 10.6.1 Saudi Arabia
- 10.6.2 UAE
- 10.6.3 Qatar
- 10.6.4 South Africa
- 10.6.5 Rest of Middle East & Africa
- 11 Key Developments
- 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
- 11.2 Acquisitions & Mergers
- 11.3 New Product Launch
- 11.4 Expansions
- 11.5 Other Key Strategies
- 12 Company Profiling
- 12.1 International Business Machines Corporation (IBM)
- 12.2 Microsoft Corporation
- 12.3 Google LLC
- 12.4 Amazon.com Inc.
- 12.5 The Climate Corporation
- 12.6 Tomorrow.io Inc.
- 12.7 Descartes Labs Inc.
- 12.8 ClimateAi Inc.
- 12.9 Spire Global Inc.
- 12.10 OpenClimate Network
- 12.11 ClimaCell Inc.
- 12.12 DeepMind Technologies Limited
- 12.13 Planet Labs PBC
- 12.14 Sust Global Inc.
- 12.15 One Concern Inc.
- List of Tables
- Table 1 Global AI-based Climate Modelling Market Outlook, By Region (2024-2032) ($MN)
- Table 2 Global AI-based Climate Modelling Market Outlook, By Component (2024-2032) ($MN)
- Table 3 Global AI-based Climate Modelling Market Outlook, By Software (2024-2032) ($MN)
- Table 4 Global AI-based Climate Modelling Market Outlook, By AI Modeling Platforms (2024-2032) ($MN)
- Table 5 Global AI-based Climate Modelling Market Outlook, By Data Management & Integration Tools (2024-2032) ($MN)
- Table 6 Global AI-based Climate Modelling Market Outlook, By Visualization & Reporting Dashboards (2024-2032) ($MN)
- Table 7 Global AI-based Climate Modelling Market Outlook, By APIs & Simulation Engines (2024-2032) ($MN)
- Table 8 Global AI-based Climate Modelling Market Outlook, By Services (2024-2032) ($MN)
- Table 9 Global AI-based Climate Modelling Market Outlook, By Consulting & Strategy (2024-2032) ($MN)
- Table 10 Global AI-based Climate Modelling Market Outlook, By Model Development & Training (2024-2032) ($MN)
- Table 11 Global AI-based Climate Modelling Market Outlook, By Deployment & Integration (2024-2032) ($MN)
- Table 12 Global AI-based Climate Modelling Market Outlook, By Managed Services (2024-2032) ($MN)
- Table 13 Global AI-based Climate Modelling Market Outlook, By Technology (2024-2032) ($MN)
- Table 14 Global AI-based Climate Modelling Market Outlook, By Machine Learning (2024-2032) ($MN)
- Table 15 Global AI-based Climate Modelling Market Outlook, By Deep Learning (2024-2032) ($MN)
- Table 16 Global AI-based Climate Modelling Market Outlook, By Computer Vision (2024-2032) ($MN)
- Table 17 Global AI-based Climate Modelling Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
- Table 18 Global AI-based Climate Modelling Market Outlook, By Other Technologies (2024-2032) ($MN)
- Table 19 Global AI-based Climate Modelling Market Outlook, By Deployment Mode (2024-2032) ($MN)
- Table 20 Global AI-based Climate Modelling Market Outlook, By Cloud-Based (2024-2032) ($MN)
- Table 21 Global AI-based Climate Modelling Market Outlook, By On-Premise (2024-2032) ($MN)
- Table 22 Global AI-based Climate Modelling Market Outlook, By Application (2024-2032) ($MN)
- Table 23 Global AI-based Climate Modelling Market Outlook, By Climate Forecasting & Modeling (2024-2032) ($MN)
- Table 24 Global AI-based Climate Modelling Market Outlook, By Carbon Emissions Monitoring & Reporting (2024-2032) ($MN)
- Table 25 Global AI-based Climate Modelling Market Outlook, By Disaster Risk Prediction & Resilience Planning (2024-2032) ($MN)
- Table 26 Global AI-based Climate Modelling Market Outlook, By Agricultural Yield & Crop Health Prediction (2024-2032) ($MN)
- Table 27 Global AI-based Climate Modelling Market Outlook, By Urban Heat Island & Air Quality Analysis (2024-2032) ($MN)
- Table 28 Global AI-based Climate Modelling Market Outlook, By Water Resource & Flood Management (2024-2032) ($MN)
- Table 29 Global AI-based Climate Modelling Market Outlook, By Other Applications (2024-2032) ($MN)
- Table 30 Global AI-based Climate Modelling Market Outlook, By End User (2024-2032) ($MN)
- Table 31 Global AI-based Climate Modelling Market Outlook, By Government & Public Sector Agencies (2024-2032) ($MN)
- Table 32 Global AI-based Climate Modelling Market Outlook, By Research & Academic Institutions (2024-2032) ($MN)
- Table 33 Global AI-based Climate Modelling Market Outlook, By Insurance & Reinsurance Companies (2024-2032) ($MN)
- Table 34 Global AI-based Climate Modelling Market Outlook, By Energy & Utilities (2024-2032) ($MN)
- Table 35 Global AI-based Climate Modelling Market Outlook, By Agriculture & Forestry (2024-2032) ($MN)
- Table 36 Global AI-based Climate Modelling Market Outlook, By Transportation & Logistics (2024-2032) ($MN)
- Table 37 Global AI-based Climate Modelling Market Outlook, By Other End Users (2024-2032) ($MN)
- Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
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