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市场调查报告书
商品编码
1664892

基于人工智慧的气候建模市场机会、成长动力、产业趋势分析和 2025 - 2034 年预测

AI-Based Climate Modelling Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 180 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2024 年全球基于人工智慧的气候建模市场规模达到 2.664 亿美元，预计 2025 年至 2034 年期间复合年增长率将达到惊人的 23.1%。这一增长是由人们对气候变迁及其严重影响的认识不断提高所推动的，推动了对先进工具的需求，以有效预测和减轻气候相关风险。人工智慧的创新，特别是机器学习和深度学习，使得气候预测更加精确、细緻和即时，改变了气候科学的模式。

基于人工智慧的气候建模市场 - IMG1

该市场的主要驱动力之一是灾害风险管理对预测工具的需求不断增加。由于气候变迁导致极端天气事件变得越来越频繁，政府和组织正在转向人工智慧驱动的模型来进行预警系统和战略准备。这些尖端工具增强了减少经济损失和保护弱势群体的能力，对于全球復原力建设工作至关重要。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 2.664 亿美元
预测值 20亿美元
复合年增长率 23.1％

基于人工智慧的气候建模市场分为软体和服务。 2024 年，软体领域占据 80% 的市场份额，预计将大幅成长，到 2034 年将达到 14 亿美元。先进的演算法和机器学习框架使这些工具能够处理大量气候资料，提供准确的预测和可操作的见解。农业、能源和灾害管理等领域严重依赖这些解决方案进行即时、数据驱动的决策。

根据应用，市场分为天气预报、气候预测、减少灾害风险、环境监测等。 2024 年，天气预报领域占最大份额，为 45%。人工智慧模型分析包括卫星影像和气象资料在内的大量资料集，以提供精确的预测，使企业能够更好地规划营运并有效降低风险。

2024 年，美国基于人工智慧的气候建模市场占据了 80% 的收入份额，预计到 2034 年将达到 5 亿美元。对人工智慧研发的大量投资，加上对气候适应力、灾害管理和环境永续性的关注，刺激了复杂气候建模工具的创新。此外，政府推动气候重点技术的措施进一步促进了市场成长。

报告内容

第 1 章：方法论与范围

  • 研究设计
    • 研究方法
    • 资料收集方法
  • 基础估计和计算
    • 基准年计算
    • 市场估计的主要趋势
  • 预测模型
  • 初步研究与验证
    • 主要来源
    • 资料探勘来源
  • 市场定义

第 2 章：执行摘要

第 3 章：产业洞察

  • 产业生态系统分析
  • 供应商概况
    • 基于人工智慧的气候建模软体供应商
    • 服务提供者
    • 经销商
    • 最终用户
  • 利润率分析
  • 定价分析
  • 专利格局
  • 成本明细
  • 技术与创新格局
  • 重要新闻及倡议
  • 监管格局
  • 衝击力
    • 成长动力
      • 提高应对气候变迁影响的意识和紧迫性
      • 人工智慧的技术进步，包括机器学习和深度学习模型
      • 政府和组织对气候调适计画的投资不断增加
      • 农业、能源和灾害风险管理等领域对预测工具的需求
    • 产业陷阱与挑战
      • 高品质、综合的气候资料有限，尤其是在发展中地区
      • 人工智慧模型的长期气候预测的复杂性和固有的不确定性
  • 成长潜力分析
  • 波特的分析
  • PESTEL 分析

第四章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第五章：市场估计与预测：按组件，2021 - 2034 年

  • 主要趋势
  • 软体
  • 服务

第六章：市场估计与预测：依部署模式，2021 - 2034 年

  • 主要趋势
  • 本地
  • 云

第 7 章：市场估计与预测：按技术，2021 - 2032 年

  • 主要趋势
  • 机器学习
  • 深度学习
  • 自然语言处理 (NLP)
  • 电脑视觉
  • 其他的

第 8 章：市场估计与预测：按应用，2021 - 2032 年

  • 主要趋势
  • 天气预报
  • 气候预测
  • 减少灾害风险
  • 环境监测
  • 其他的

第 9 章：市场估计与预测：按地区，2021 - 2034 年

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 俄罗斯
    • 北欧
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中东及非洲
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯

第十章：公司简介

  • Amazon Web Services (AWS)
  • Arundo Analytics
  • Atmos AI
  • Blue Sky Analytics
  • ClimateAi
  • Cloudera
  • DTN
  • Google
  • Hewlett Packard Enterprise (HPE)
  • IBM
  • Jupiter Intelligence
  • Microsoft
  • NVIDIA
  • Open Climate Fix
  • Oracle Corporation
  • SAS Institute
  • Spire Global
  • The Weather Company
  • Tomorrow.io
  • Weather Technologies
简介目录
Product Code: 12534

The Global AI-Based Climate Modelling Market reached USD 266.4 million in 2024 and is forecasted to grow at an impressive CAGR of 23.1% from 2025 to 2034. This growth is propelled by heightened awareness of climate change and its severe impacts, driving the need for advanced tools to predict and mitigate climate-related risks effectively. Innovations in artificial intelligence, particularly in machine learning and deep learning, have enabled more precise, granular, and real-time climate predictions, transforming the landscape of climate science.

AI-Based Climate Modelling Market - IMG1

One of the primary drivers of this market is the increasing demand for predictive tools in disaster risk management. With extreme weather events becoming more frequent due to climate change, governments and organizations are turning to AI-driven models for early warning systems and strategic preparedness. These cutting-edge tools enhance capabilities to minimize economic losses and safeguard vulnerable populations, proving critical in resilience-building efforts globally.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$266.4 Million
Forecast Value$2 Billion
CAGR23.1%

The AI-based climate modelling market is segmented into software and services. In 2024, the software segment dominated with an 80% market share and is projected to grow significantly, reaching USD 1.4 billion by 2034. The widespread adoption of software solutions stems from their versatility and scalability across industries. Advanced algorithms and machine learning frameworks empower these tools to process vast amounts of climate data, delivering accurate forecasts and actionable insights. Sectors such as agriculture, energy, and disaster management heavily rely on these solutions for real-time, data-driven decision-making.

By application, the market is categorized into weather forecasting, climate prediction, disaster risk reduction, environmental monitoring, and others. The weather forecasting segment held the largest share-45%-in 2024. This dominance is attributed to the critical need for accurate, real-time weather predictions across sectors like agriculture, energy, and transportation. AI models analyze extensive datasets, including satellite imagery and meteorological data, to deliver precise forecasts, enabling businesses to better plan operations and mitigate risks effectively.

The U.S. AI-based climate modelling market captured a substantial 80% revenue share in 2024 and is projected to reach USD 500 million by 2034. This leadership is fueled by the country's robust technological infrastructure and a strong presence of leading technology companies and startups. Significant investments in AI research and development, coupled with a focus on climate resilience, disaster management, and environmental sustainability, have spurred the innovation of sophisticated climate modeling tools. Additionally, governmental initiatives to advance climate-focused technologies further bolster market growth.

Report Content

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 AI-based climate modelling software providers
    • 3.2.2 Service providers
    • 3.2.3 Distributors
    • 3.2.4 End users
  • 3.3 Profit margin analysis
  • 3.4 Pricing analysis
  • 3.5 Patent Landscape
  • 3.6 Cost Breakdown
  • 3.7 Technology & innovation landscape
  • 3.8 Key news & initiatives
  • 3.9 Regulatory landscape
  • 3.10 Impact forces
    • 3.10.1 Growth drivers
      • 3.10.1.1 Increasing awareness and urgency to address climate change impacts
      • 3.10.1.2 Technological advancements in AI, including machine learning and deep learning models
      • 3.10.1.3 Rising investments by governments and organizations in climate resilience initiatives
      • 3.10.1.4 Demand for predictive tools in sectors like agriculture, energy, and disaster risk management
    • 3.10.2 Industry pitfalls & challenges
      • 3.10.2.1 Limited availability of high-quality and comprehensive climate data, especially in developing regions
      • 3.10.2.2 Complexity and inherent uncertainties in long-term climate predictions by AI models
  • 3.11 Growth potential analysis
  • 3.12 Porter’s analysis
  • 3.13 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn)

  • 5.1 Key trends
  • 5.2 Software
  • 5.3 Services

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Bn)

  • 6.1 Key trends
  • 6.2 On-premises
  • 6.3 Cloud

Chapter 7 Market Estimates & Forecast, By Technology, 2021 - 2032 ($Bn)

  • 7.1 Key trends
  • 7.2 Machine learning
  • 7.3 Deep learning
  • 7.4 Natural Language Processing (NLP)
  • 7.5 Computer vision
  • 7.6 Others

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

  • 8.1 Key trends
  • 8.2 Weather forecasting
  • 8.3 Climate prediction
  • 8.4 Disaster risk reduction
  • 8.5 Environmental monitoring
  • 8.6 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

  • 9.1 Key trends
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
  • 9.3 Europe
    • 9.3.1 UK
    • 9.3.2 Germany
    • 9.3.3 France
    • 9.3.4 Spain
    • 9.3.5 Italy
    • 9.3.6 Russia
    • 9.3.7 Nordics
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 India
    • 9.4.3 Japan
    • 9.4.4 South Korea
    • 9.4.5 ANZ
    • 9.4.6 Southeast Asia
  • 9.5 Latin America
    • 9.5.1 Brazil
    • 9.5.2 Mexico
    • 9.5.3 Argentina
  • 9.6 MEA
    • 9.6.1 UAE
    • 9.6.2 South Africa
    • 9.6.3 Saudi Arabia

Chapter 10 Company Profiles

  • 10.1 Amazon Web Services (AWS)
  • 10.2 Arundo Analytics
  • 10.3 Atmos AI
  • 10.4 Blue Sky Analytics
  • 10.5 ClimateAi
  • 10.6 Cloudera
  • 10.7 DTN
  • 10.8 Google
  • 10.9 Hewlett Packard Enterprise (HPE)
  • 10.10 IBM
  • 10.11 Jupiter Intelligence
  • 10.12 Microsoft
  • 10.13 NVIDIA
  • 10.14 Open Climate Fix
  • 10.15 Oracle Corporation
  • 10.16 SAS Institute
  • 10.17 Spire Global
  • 10.18 The Weather Company
  • 10.19 Tomorrow.io
  • 10.20 Weather Technologies