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市場調查報告書
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1865536

全球人工智慧氣候建模市場：預測至2032年－按組件、技術、部署方式、應用、最終用戶和地區進行分析

AI-based Climate Modelling Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Technology, Deployment Mode, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，全球基於人工智慧的氣候建模市場預計到 2025 年將達到 4.252 億美元，到 2032 年將達到 19.06 億美元，在預測期內的複合年成長率為 23.9%。

基於人工智慧的氣候建模是指利用人工智慧和機器學習演算法來模擬、預測和分析氣候系統及其未來變化。與僅依賴物理方程式的傳統模型不同，人工智慧驅動的模型能夠從包括衛星觀測、氣象記錄和海洋資料在內的大型資料集中學習模式，從而提高預測精度和計算效率。這些模型可以捕捉氣候系統中複雜的非線性關係，並加快極端天氣事件、溫度波動和碳排放的預測。透過整合人工智慧，科學家可以改善氣候適應力規劃、政策制定以及全球減緩和適應氣候變遷的努力。

極端氣候事件的頻率和強度日益增加

政府和企業需要預測工具來更準確、更早評估洪水、乾旱、野火和颶風等風險。平台利用衛星資料、歷史記錄和即時資訊來模擬天氣模式和環境壓力因素。與預警系統和基礎設施規劃的銜接可以增強災害防備和資源分配能力。農業、保險、能源和城市規劃等領域對擴充性和適應性強的建模需求日益成長。這些趨勢正在推動氣候風險情報和減緩生態系統中的平台創新。

缺乏專業領域知識和整合方面的挑戰

人工智慧的應用需要跨學科技能，包括氣候學、資料科學和地理空間分析，而這些技能在許多地區仍然短缺。企業在將舊有系統與人工智慧引擎整合以及確保資料格式和建模框架之間的互通性方面面臨許多挑戰。缺乏標準化通訊協定和培訓計劃阻礙了員工的技能提升和模型的可靠性。與政策工具和相關人員工作流程的整合仍然分散且資源彙整資源。這些限制因素持續阻礙著分散式、基礎設施有限的氣候建模環境中人工智慧的應用。

農業、能源和保險領域的跨產業需求

在農業領域，預測模型被用於在氣候變遷條件下最佳化灌溉、作物選擇和病蟲害防治。能源供應商利用類比技術來管理電網韌性、整合可再生能源並應對極端天氣風險。保險公司利用氣候分析來評估脆弱地區的風險敞口、管理價格風險並設計參數化產品。平台支援情境規劃、碳排放追蹤以及針對特定產業需求量身定做的調適策略。公共和私營機構對模組化、可互通的建模工具的需求日益成長。這些趨勢正在推動多學科氣候智慧平台的發展。

接入差距和擴充性挑戰

高效能運算、資料基礎設施和專業人才集中在高所得經濟體，限制了人工智慧在全球的應用和公平性。小規模的國家和地方機構在獲取即時數據、雲端平台和人工智慧應用所需的技術援助方面面臨挑戰。缺乏全面的資料集和區域協調降低了模型在不同地區的準確性和相關性。資金短缺和政策碎片化進一步限制了平台的應用和相關人員的參與。這些限制持續阻礙著服務不足地區平台的成熟度和氣候適應力規劃。

新冠疫情的影響：

疫情擾亂了氣候研究建模專案的實地資料收集和基礎設施投資。封鎖措施延緩了衛星校準感測器的部署和氣候資料集的國際合作。然而，疫情後的復甦強調了氣候敏感型產業的韌性規劃、環境監測和數位轉型。公共衛生和災害舉措中，遙感探測、雲端運算和人工智慧驅動的分析技術投資激增。消費者和政策制定者對系統性風險和環境相互依存性的認知也日益增強。這些變化強化了對基於人工智慧的氣候建模基礎設施的長期投資和跨部門整合。

預計在預測期內，機器學習領域將佔據最大的市場佔有率。

由於機器學習在氣候建模工作流程中展現出的多功能性、擴充性和卓越性能，預計在預測期內，機器學習領域將佔據最大的市場佔有率。相關平台利用監督式和非監督式模型進行異常檢測、天氣模式模擬和資源分配最佳化。與衛星資料、物聯網感測器和歷史資料集的整合，能夠提高預測精度和空間解析度。農業、能源、保險和城市規劃等領域對適應性強且可解釋的人工智慧的需求日益成長。供應商提供模組化引擎、應用程式介面 (API) 和視覺化工具，以支援跨行業應用和政策協調。這些優勢正在鞏固機器學習領域在人工智慧驅動的氣候建模平台中的主導地位。

預計在預測期內，災害風險預測和韌性規劃領域將實現最高的複合年成長率。

預計在預測期內，隨著氣候建模平台的應用範圍擴展到緊急應變、基礎設施設計和政策制定等領域，災害風險預測和韌性規劃領域將迎來最高的成長率。這些平台能夠模擬災害情境、評估脆弱性，並指導在易受洪水、乾旱和野火侵襲的地區對韌性系統進行投資。與地理空間資料、預警系統和社區參與工具的整合，能夠增強災害防備和復原能力。地方政府、保險公司和發展機構對擴充性且本地化的建模需求日益成長。這些趨勢正在推動專注於韌性的氣候建模平台和服務的發展。

佔比最大的地區：

由於北美擁有先進的研究基礎設施、機構投資以及監管機構對氣候建模技術的積極參與，預計該地區將在預測期內佔據最大的市場佔有率。企業和機構正在農業、能源、保險和城市規劃等領域部署人工智慧平台，用於氣候風險管理和政策制定。對衛星網路、雲端平台和地理空間分析的投資有助於擴充性和準確性。主要供應商、學術機構和氣候研究中心的存在正在推動創新和標準化。各公司正在調整其建模策略，使其與聯邦政府的要求、環境、社會和治理（ESG）報告以及韌性規劃框架保持一致。

年複合成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率，因為氣候風險、都市化和數位基礎設施在該地區各國經濟體中相互交融。印度、中國、日本和印尼等國正在農業、災害應變和能源規劃領域擴展氣候建模平台。政府支持計畫正在推動人工智慧在氣候敏感產業的應用、數據基礎設施建設和Start-Ups。本地供應商提供多語言、行動優先且本地化的解決方案，以滿足不同災害類型和監管需求。公共機構、保險公司和能源供應商對擴充性且具有前瞻性的建模基礎設施的需求日益成長。這些趨勢正在加速基於人工智慧的氣候建模技術的創新和應用，從而推動全部區域的成長。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球基於人工智慧的氣候建模市場（按組件分類）

  • 介紹
  • 軟體
    • 人工智慧建模平台
    • 資料管理和整合工具
    • 視覺化和報告儀表板
    • API/仿真引擎
  • 服務
    • 諮詢與策略
    • 模型開發與培訓
    • 部署和整合
    • 託管服務

6. 全球基於人工智慧的氣候建模市場（按技術分類）

  • 介紹
  • 機器學習
  • 深度學習
  • 電腦視覺
  • 自然語言處理（NLP）
  • 其他技術

7. 全球基於人工智慧的氣候建模市場（按部署方式分類）

  • 介紹
  • 雲端基礎的
  • 本地部署

8. 全球以人工智慧為基礎的氣候建模市場（按應用分類）

  • 介紹
  • 氣候預測與建模
  • 碳排放監測和報告
  • 災害風險預測與韌性規劃
  • 農業產量和作物健康預測
  • 城市熱島效應與空氣品質分析
  • 水資源/洪水管理
  • 其他用途

9. 全球基於人工智慧的氣候建模市場（按最終用戶分類）

  • 介紹
  • 政府和公共部門組織
  • 研究和學術機構
  • 保險和再保險公司
  • 能源與公用事業
  • 農業和林業
  • 運輸/物流
  • 其他最終用戶

第10章 全球以人工智慧為基礎的氣候建模市場（按地區分類）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：公司簡介

  • 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.
  • One Concern Inc.
Product Code: SMRC32179

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 America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest 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

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

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.