查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1865536

全球人工智慧气候建模市场：预测至2032年－按组件、技术、部署方式、应用、最终用户和地区进行分析

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个工作天内

价格

根据 Stratistics MRC 的一项研究，全球基于人工智慧的气候建模市场预计到 2025 年将达到 4.252 亿美元，到 2032 年将达到 19.06 亿美元，在预测期内的复合年增长率为 23.9%。

基于人工智慧的气候建模是指利用人工智慧和机器学习演算法来模拟、预测和分析气候系统及其未来变化。与仅依赖物理方程式的传统模型不同，人工智慧驱动的模型能够从包括卫星观测、气象记录和海洋资料在内的大型资料集中学习模式，从而提高预测精度和计算效率。这些模型可以捕捉气候系统中复杂的非线性关係，并加快极端天气事件、温度波动和碳排放的预测。透过整合人工智慧，科学家可以改善气候适应力规划、政策制定以及全球减缓和适应气候变迁的努力。

极端气候事件的频率和强度日益增加

政府和企业需要预测工具来更准确、更早评估洪水、干旱、野火和飓风等风险。平台利用卫星资料、历史记录和即时资讯来模拟天气模式和环境压力因素。与预警系统和基础设施规划的衔接可以增强灾害防备和资源分配能力。农业、保险、能源和城市规划等领域对扩充性和适应性强的建模需求日益增长。这些趋势正在推动气候风险情报和减缓生态系统中的平台创新。

缺乏专业领域知识和整合方面的挑战

人工智慧的应用需要跨学科技能，包括气候学、资料科学和地理空间分析，而这些技能在许多地区仍然短缺。企业在将旧有系统与人工智慧引擎整合以及确保资料格式和建模框架之间的互通性方面面临许多挑战。缺乏标准化通讯协定和培训计划阻碍了员工的技能提升和模型的可靠性。与政策工具和相关人员工作流程的整合仍然分散且资源彙整资源。这些限制因素持续阻碍着分散式、基础设施有限的气候建模环境中人工智慧的应用。

农业、能源和保险领域的跨产业需求

在农业领域，预测模型被用于在气候变迁条件下优化灌溉、作物选择和病虫害防治。能源供应商利用类比技术来管理电网韧性、整合可再生能源并应对极端天气风险。保险公司利用气候分析来评估脆弱地区的风险敞口、管理价格风险并设计参数化产品。平台支援情境规划、碳排放追踪以及针对特定产业需求量身定制的调适策略。公共和私营机构对模组化、可互通的建模工具的需求日益增长。这些趋势正在推动多学科气候智慧平台的发展。

接入差距和扩充性挑战

高效能运算、资料基础设施和专业人才集中在高所得经济体，限制了人工智慧在全球的应用和公平性。小规模的国家和地方机构在获取即时数据、云端平台和人工智慧应用所需的技术援助方面面临挑战。缺乏全面的数据集和区域协调降低了模型在不同地区的准确性和相关性。资金短缺和政策碎片化进一步限制了平台的应用和相关人员的参与。这些限制持续阻碍着服务不足地区平台的成熟度和气候适应力规划。

新冠疫情的影响：

疫情扰乱了气候研究建模专案的实地资料收集和基础设施投资。封锁措施延缓了卫星校准感测器的部署和气候资料集的国际合作。然而，疫情后的復苏强调了气候敏感型产业的韧性规划、环境监测和数位转型。公共卫生和灾害倡议中，遥感探测、云端运算和人工智慧驱动的分析技术投资激增。消费者和政策制定者对系统性风险和环境相互依存性的认知也日益增强。这些变化强化了对基于人工智慧的气候建模基础设施的长期投资和跨部门整合。

预计在预测期内，机器学习领域将占据最大的市场份额。

由于机器学习在气候建模工作流程中展现出的多功能性、扩充性和卓越性能，预计在预测期内，机器学习领域将占据最大的市场份额。相关平台利用监督式和非监督式模型进行异常检测、天气模式模拟和资源分配最佳化。与卫星资料、物联网感测器和历史资料集的集成，能够提高预测精度和空间解析度。农业、能源、保险和城市规划等领域对适应性强且可解释的人工智慧的需求日益增长。供应商提供模组化引擎、应用程式介面 (API) 和视觉化工具，以支援跨行业应用和政策协调。这些优势正在巩固机器学习领域在人工智慧驱动的气候建模平台中的主导地位。

预计在预测期内，灾害风险预测和韧性规划领域将实现最高的复合年增长率。

预计在预测期内，随着气候建模平台的应用范围扩展到紧急应变、基础设施设计和政策制定等领域，灾害风险预测和韧性规划领域将迎来最高的成长率。这些平台能够模拟灾害情境、评估脆弱性，并指导在易受洪水、干旱和野火侵袭的地区对韧性系统进行投资。与地理空间资料、预警系统和社区参与工具的整合，能够增强灾害防备和復原能力。地方政府、保险公司和发展机构对扩充性且本地化的建模需求日益增长。这些趋势正在推动专注于韧性的气候建模平台和服务的发展。

占比最大的地区：

由于北美拥有先进的研究基础设施、机构投资以及监管机构对气候建模技术的积极参与，预计该地区将在预测期内占据最大的市场份额。企业和机构正在农业、能源、保险和城市规划等领域部署人工智慧平台，用于气候风险管理和政策制定。对卫星网路、云端平台和地理空间分析的投资有助于扩充性和准确性。主要供应商、学术机构和气候研究中心的存在正在推动创新和标准化。各公司正在调整其建模策略，使其与联邦政府的要求、环境、社会和治理（ESG）报告以及韧性规划框架保持一致。

年复合成长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率，因为气候风险、都市化和数位基础设施在该地区各国经济体中相互交融。印度、中国、日本和印尼等国正在农业、灾害应变和能源规划领域扩展气候建模平台。政府支持计画正在推动人工智慧在气候敏感产业的应用、数据基础设施建设和Start-Ups。本地供应商提供多语言、行动优先且本地化的解决方案，以满足不同灾害类型和监管需求。公共机构、保险公司和能源供应商对扩充性且具有前瞻性的建模基础设施的需求日益增长。这些趋势正在加速基于人工智慧的气候建模技术的创新和应用，从而推动全部区域的成长。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球基于人工智慧的气候建模市场（按组件划分）

  • 介绍
  • 软体
    • 人工智慧建模平台
    • 资料管理和整合工具
    • 视觉化和报告仪表板
    • API/仿真引擎
  • 服务
    • 咨询与策略
    • 模型开发与培训
    • 部署和集成
    • 託管服务

6. 全球基于人工智慧的气候建模市场（按技术划分）

  • 介绍
  • 机器学习
  • 深度学习
  • 电脑视觉
  • 自然语言处理（NLP）
  • 其他技术

7. 全球基于人工智慧的气候建模市场（按部署方式划分）

  • 介绍
  • 云端基础的
  • 本地部署

8. 全球以人工智慧为基础的气候建模市场（按应用划分）

  • 介绍
  • 气候预测与建模
  • 碳排放监测和报告
  • 灾害风险预测与韧性规划
  • 农业产量和作物健康预测
  • 城市热岛效应与空气品质分析
  • 水资源/洪水管理
  • 其他用途

9. 全球基于人工智慧的气候建模市场（按最终用户划分）

  • 介绍
  • 政府和公共部门组织
  • 研究和学术机构
  • 保险和再保险公司
  • 能源与公用事业
  • 农业和林业
  • 运输/物流
  • 其他最终用户

第十章 全球以人工智慧为基础的气候建模市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • 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.