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市場調查報告書
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2021574

2034年氣候科技領域人工智慧市場預測：按組件、部署模式、技術、應用、最終用戶和地區分類的全球分析

AI in Climate Technology Market Forecasts to 2034- Global Analysis By Component (Software, Hardware and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球氣候技術領域的 AI 市場規模將達到 364.2 億美元，在預測期內將以 22.9% 的複合年成長率成長，到 2034 年將達到 1896 億美元。

氣候科技領域的人工智慧是指應用人工智慧工具和演算法來監測、分析和緩解氣候變遷的影響。這包括利用機器學習、預測分析和數據建模來最佳化能源利用、預測天氣模式、加強碳排放追蹤以及支援永續資源管理。這些系統處理龐大的環境資料集，並為政府、產業和組織提供可操作的見解。透過提高決策效率和營運效率，氣候科技領域的人工智慧在推動脫碳進程、增強應對氣候變遷的能力以及促進向更永續、更具環境意識的全球經濟轉型方面發揮著至關重要的作用。

氣候變遷和極端天氣事件的加劇

氣候相關災害（例如熱浪、洪水和颶風）的發生頻率和嚴重性不斷增加，正在加速人工智慧在氣候技術領域的應用。各國政府和企業都在優先考慮數據驅動型解決方案，以加強氣候預測、災害防備和減災工作。人工智慧能夠實現即時監測、預測分析和預警系統，從而最大限度地減少環境和經濟損失。這種日益成長的迫切性正在推動對先進技術的投資，以增強抵禦能力、支持永續性目標，並推動全球各行各業積極主動地進行氣候風險管理。

高昂的運算成本和基礎設施成本

將人工智慧應用於氣候技術領域需要對高效能運算基礎設施、資料儲存系統和進階分析平台進行大量投資。這些成本可能構成障礙，尤其對於發展中地區和小規模組織。此外，維護和升級人工智慧系統需要持續投入硬體、軟體和專業人員。與大規模人工智慧模型相關的能源消耗會進一步增加營運成本。這些財務和技術障礙會限制人工智慧主導的氣候解決方案在資源匱乏環境中的應用，並延緩其整合進程。

雲端運算、物聯網和遙感探測的進步

雲端運算、物聯網 (IoT) 和遙感探測技術的快速發展為人工智慧在氣候技術領域創造了巨大的機會。雲端平台能夠實現可擴展的數據處理和存儲，而物聯網設備和感測器則有助於即時環境監測。包括衛星影像在內的遙感探測技術提高了資料的準確性和覆蓋範圍。這些創新技術的結合，使人工智慧系統能夠提供更精準的氣候洞察，最佳化資源利用，並支援永續決策，從而推動市場成長，並拓展其在各個領域的應用。

與數據品質、可用性和整合相關的挑戰。

人工智慧系統高度依賴高品質、全面且標準化的資料集來產生準確的氣候洞察。然而，資料收集方法的不一致、存取受限以及資料來源的分散帶來了巨大的挑戰。整合來自衛星、感測器和歷史記錄等多個平台的多樣化資料集可能是一項複雜且耗時的任務。資料品質不佳或資訊缺失會導致預測不可靠和決策效率低下。這些挑戰會阻礙人工智慧驅動的氣候解決方案在不同地區和產業的擴充性和有效性。

新冠疫情的影響：

新冠疫情對氣候科技領域的人工智慧市場產生了複雜的影響。雖然疫情初期擾亂了專案進度和投資，但危機也凸顯了數據驅動決策和韌性規劃的重要性。各國政府和組織日益認知到人工智慧在應對包括氣候變遷在內的複雜全球挑戰方面的價值。疫情後的復甦策略強調永續和綠色舉措，促使人們重新投資於人工智慧驅動的氣候解決方案，加速數位轉型，並推動市場長期成長。

在預測期內，氣候風險評估部分預計將是規模最大的部分。

預計在預測期內，氣候風險評估領域將佔據最大的市場佔有率，因為它在識別、評估和緩解環境風險方面發揮著至關重要的作用。各組織機構越來越依賴人工智慧模型來分析氣候數據、評估脆弱性並預測其對基礎設施、供應鏈和生態系統的潛在影響。這些洞察有助於做出明智的決策並遵守監管規定。人們對氣候相關金融風險的認知不斷提高，以及對主動風險管理的需求日益成長，正在推動全球範圍內對先進氣候風險評估解決方案的採用。

在預測期內，醫療保健產業預計將呈現最高的複合年成長率。

在預測期內，由於氣候變遷對公眾健康的影響日益加劇，醫療保健領域預計將呈現最高的成長率。人工智慧技術正被用於分析空氣品質、溫度變化和疾病爆發模式等環境因素，以預測健康風險和感染疾病爆發。醫療保健系統正在利用這些洞察來增強應對能力、最佳化資源配置並改善患者照護。人們對氣候敏感型疾病的認知不斷提高，以及對適應性醫療保健基礎設施的需求，正在進一步加速人工智慧在該領域的應用。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其強大的技術基礎設施、人工智慧解決方案的高普及率以及對氣候創新的大量投資。領先的科技公司、政府的支持性政策和先進的研究舉措正在推動市場成長。此外，監管機構對碳減排和永續性的日益重視，也促使各組織採用人工智慧驅動的氣候技術，進一步鞏固了該地區在全球市場的主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的工業化進程、日益成長的環境問題關注以及政府主導的永續性舉措的不斷擴大。該地區各國正大力投資智慧技術、可再生能源以及增強應對氣候變遷韌性的策略。數位基礎設施的擴展和人工智慧解決方案在各領域的日益普及進一步推動了市場成長。此外，該地區易受氣候變遷影響，這也促使其對先進的氣候分析和減緩技術的需求不斷成長。

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  • 企業概況
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球氣候科技領域人工智慧市場：按組件分類

  • 軟體
  • 硬體
  • 服務

第6章：全球氣候科技領域人工智慧市場：依部署模式分類

  • 現場
  • 基於雲端的

第7章：全球氣候科技領域人工智慧市場：按技術分類

  • 機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 機器人流程自動化（RPA）
  • 深度學習
  • 其他技術

第8章：全球氣候科技領域人工智慧市場：按應用分類

  • 氣候建模和天氣預報
  • 災害預測與管理
  • 氣候風險評估
  • 追蹤並減少碳排放
  • 最佳化可再生能源
  • 環境監測與評估
  • 水資源管理

第9章：全球氣候技術領域人工智慧市場：按最終用戶分類

  • 衛生保健
  • 零售與電子商務
  • 製造業
  • 資訊科技/通訊
  • 車
  • 能源公用事業
  • 其他最終用戶

第10章：全球氣候科技領域人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services（AWS）
  • NVIDIA Corporation
  • AccuWeather, Inc.
  • ClimateAI
  • Descartes Labs
  • Spire Global Inc.
  • Planet Labs PBC
  • Schneider Electric SE
  • Siemens AG
  • C3.ai, Inc.
  • The Climate Corporation
  • Blue Sky Analytics
Product Code: SMRC34914

According to Stratistics MRC, the Global AI in Climate Technology Market is accounted for $36.42 billion in 2026 and is expected to reach $189.60 billion by 2034 growing at a CAGR of 22.9% during the forecast period. AI in Climate Technology refers to the application of artificial intelligence tools and algorithms to monitor, analyze, and mitigate climate change impacts. It involves leveraging machine learning, predictive analytics, and data modeling to optimize energy usage, forecast weather patterns, enhance carbon tracking, and support sustainable resource management. These systems process vast environmental datasets to deliver actionable insights for governments, industries, and organizations. By improving decision-making and operational efficiency, AI in climate technology plays a critical role in advancing decarbonization efforts, strengthening climate resilience, and enabling the transition toward a more sustainable and environmentally responsible global economy.

Market Dynamics:

Driver:

Rising urgency of climate change and extreme weather events

The increasing frequency and severity of climate-related disasters, including heatwaves, floods, and hurricanes, are accelerating the adoption of AI in climate technology. Governments and enterprises are prioritizing data driven solutions to enhance climate forecasting, disaster preparedness, and mitigation strategies. AI enables real-time monitoring, predictive analytics, and early warning systems, helping minimize environmental and economic losses. This growing urgency is fostering investments in advanced technologies to strengthen resilience, support sustainability goals, and drive proactive climate risk management across industries globally.

Restraint:

High computational and infrastructure costs

The deployment of AI in climate technology requires substantial investment in high performance computing infrastructure, data storage systems, and advanced analytics platforms. These costs can be prohibitive, particularly for developing regions and small organizations. Additionally, maintaining and upgrading AI systems involves continuous expenditure on hardware, software, and skilled personnel. Energy consumption associated with large-scale AI models further adds to operational costs. These financial and technical barriers may limit widespread adoption and slow the integration of AI driven climate solutions in resource constrained environments.

Opportunity:

Advancements in cloud computing, IoT, and remote sensing

Rapid advancements in cloud computing, Internet of Things (IoT), and remote sensing technologies are creating significant opportunities for AI in climate technology. Cloud platforms enable scalable data processing and storage, while IoT devices and sensors facilitate real-time environmental monitoring. Remote sensing technologies, including satellite imagery, enhance data accuracy and coverage. Together, these innovations empower AI systems to deliver more precise climate insights, optimize resource utilization, and support sustainable decision-making, thereby driving market growth and expanding application areas across sectors.

Threat:

Data quality, availability, and integration challenges

AI systems rely heavily on high quality, comprehensive, and standardized datasets to generate accurate climate insights. However, inconsistencies in data collection methods, limited accessibility, and fragmented data sources pose significant challenges. Integrating diverse datasets from multiple platforms, such as satellites, sensors, and historical records, can be complex and time-consuming. Poor data quality or gaps in information may lead to unreliable predictions and ineffective decision-making. These challenges can hinder the scalability and effectiveness of AI driven climate solutions across different regions and industries.

Covid-19 Impact:

The COVID-19 pandemic had a mixed impact on the AI in climate technology market. While initial disruptions affected project timelines and investments, the crisis also highlighted the importance of data-driven decision making and resilience planning. Governments and organizations increasingly recognized the value of AI in managing complex global challenges, including climate change. Post pandemic recovery strategies have emphasized sustainable development and green initiatives, leading to renewed investments in AI-enabled climate solutions, thereby accelerating digital transformation and long term market growth.

The climate risk assessment segment is expected to be the largest during the forecast period

The climate risk assessment segment is expected to account for the largest market share during the forecast period, due to its critical role in identifying, evaluating, and mitigating environmental risks. Organizations are increasingly relying on AI-driven models to analyze climate data, assess vulnerabilities, and predict potential impacts on infrastructure, supply chains, and ecosystems. These insights support informed decision making and regulatory compliance. Growing awareness of climate related financial risks and the need for proactive risk management are driving the adoption of advanced climate risk assessment solutions globally.

The healthcare segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare segment is predicted to witness the highest growth rate, due to increasing impact of climate change on public health. AI technologies are being used to analyze environmental factors such as air quality, temperature changes, and disease patterns to predict health risks and outbreaks. Healthcare systems are leveraging these insights to improve preparedness, resource allocation, and patient care. Rising awareness of climate sensitive diseases and the need for adaptive healthcare infrastructure are further accelerating the adoption of AI in this segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to strong technological infrastructure, high adoption of AI solutions, and significant investments in climate innovation. The presence of leading technology companies, supportive government policies, and advanced research initiatives are driving market growth. Additionally, increasing regulatory focus on carbon reduction and sustainability is encouraging organizations to adopt AI driven climate technologies, further strengthening the region's dominant position in the global market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid industrialization, increasing environmental concerns, and growing government initiatives toward sustainability. Countries in the region are investing in smart technologies, renewable energy, and climate resilience strategies. Expanding digital infrastructure and rising adoption of AI solutions across sectors are further fueling market growth. Additionally, the region's vulnerability to climate change impacts is driving demand for advanced climate analytics and mitigation technologies.

Key players in the market

Some of the key players in AI in Climate Technology Market include IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services (AWS), NVIDIA Corporation, AccuWeather, Inc., ClimateAI, Descartes Labs, Spire Global Inc., Planet Labs PBC, Schneider Electric SE, Siemens AG, C3.ai, Inc., The Climate Corporation and Blue Sky Analytics.

Key Developments:

In February 2026, IBM introduced the next-generation autonomous storage portfolio featuring IBM Flash System 5600, 7600, and 9600, powered by agentic AI. The systems automate storage management, improve cyber-resilience, and optimize enterprise data operations, helping organizations manage AI workloads more efficiently. This launch strengthens IBM's hybrid cloud and AI infrastructure ecosystem by reducing manual IT operations and enabling autonomous data storage environments.

In January 2026, IBM partnered with telecom group e& to deploy enterprise-grade agentic AI solutions for governance and regulatory compliance. The collaboration focuses on implementing advanced AI agents capable of automating compliance monitoring, operational decision-making, and enterprise analytics. Announced at the World Economic Forum in Davos, the initiative demonstrates IBM's growing focus on enterprise AI ecosystems.

Components Covered:

  • Software
  • Hardware
  • Services

Deployment Modes Covered:

  • On Premises
  • Cloud Based

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Robotics Process Automation (RPA)
  • Deep Learning
  • Other Technologies

Applications Covered:

  • Climate Modeling & Weather Forecasting
  • Disaster Prediction & Management
  • Climate Risk Assessment
  • Carbon Emission Tracking & Reduction
  • Renewable Energy Optimization
  • Environmental Monitoring & Assessment
  • Water Management

End Users Covered:

  • Healthcare
  • Retail & E-commerce
  • Manufacturing
  • IT & Telecom
  • Automotive
  • Energy & Utilities
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Climate Technology Market, By Component

  • 5.1 Software
  • 5.2 Hardware
  • 5.3 Services

6 Global AI in Climate Technology Market, By Deployment Mode

  • 6.1 On Premises
  • 6.2 Cloud Based

7 Global AI in Climate Technology Market, By Technology

  • 7.1 Machine Learning
  • 7.2 Natural Language Processing (NLP)
  • 7.3 Computer Vision
  • 7.4 Robotics Process Automation (RPA)
  • 7.5 Deep Learning
  • 7.6 Other Technologies

8 Global AI in Climate Technology Market, By Application

  • 8.1 Climate Modeling & Weather Forecasting
  • 8.2 Disaster Prediction & Management
  • 8.3 Climate Risk Assessment
  • 8.4 Carbon Emission Tracking & Reduction
  • 8.5 Renewable Energy Optimization
  • 8.6 Environmental Monitoring & Assessment
  • 8.7 Water Management

9 Global AI in Climate Technology Market, By End User

  • 9.1 Healthcare
  • 9.2 Retail & E-commerce
  • 9.3 Manufacturing
  • 9.4 IT & Telecom
  • 9.5 Automotive
  • 9.6 Energy & Utilities
  • 9.7 Other End Users

10 Global AI in Climate Technology Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 IBM Corporation
  • 13.2 Microsoft Corporation
  • 13.3 Google LLC
  • 13.4 Amazon Web Services (AWS)
  • 13.5 NVIDIA Corporation
  • 13.6 AccuWeather, Inc.
  • 13.7 ClimateAI
  • 13.8 Descartes Labs
  • 13.9 Spire Global Inc.
  • 13.10 Planet Labs PBC
  • 13.11 Schneider Electric SE
  • 13.12 Siemens AG
  • 13.13 C3.ai, Inc.
  • 13.14 The Climate Corporation
  • 13.15 Blue Sky Analytics

List of Tables

  • Table 1 Global AI in Climate Technology Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Climate Technology Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Climate Technology Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Climate Technology Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 5 Global AI in Climate Technology Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI in Climate Technology Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 7 Global AI in Climate Technology Market Outlook, By On Premises (2023-2034) ($MN)
  • Table 8 Global AI in Climate Technology Market Outlook, By Cloud Based (2023-2034) ($MN)
  • Table 9 Global AI in Climate Technology Market Outlook, By Technology (2023-2034) ($MN)
  • Table 10 Global AI in Climate Technology Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 11 Global AI in Climate Technology Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 12 Global AI in Climate Technology Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 13 Global AI in Climate Technology Market Outlook, By Robotics Process Automation (RPA) (2023-2034) ($MN)
  • Table 14 Global AI in Climate Technology Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 15 Global AI in Climate Technology Market Outlook, By Other Technologies (2023-2034) ($MN)
  • Table 16 Global AI in Climate Technology Market Outlook, By Application (2023-2034) ($MN)
  • Table 17 Global AI in Climate Technology Market Outlook, By Climate Modeling & Weather Forecasting (2023-2034) ($MN)
  • Table 18 Global AI in Climate Technology Market Outlook, By Disaster Prediction & Management (2023-2034) ($MN)
  • Table 19 Global AI in Climate Technology Market Outlook, By Climate Risk Assessment (2023-2034) ($MN)
  • Table 20 Global AI in Climate Technology Market Outlook, By Carbon Emission Tracking & Reduction (2023-2034) ($MN)
  • Table 21 Global AI in Climate Technology Market Outlook, By Renewable Energy Optimization (2023-2034) ($MN)
  • Table 22 Global AI in Climate Technology Market Outlook, By Environmental Monitoring & Assessment (2023-2034) ($MN)
  • Table 23 Global AI in Climate Technology Market Outlook, By Water Management (2023-2034) ($MN)
  • Table 24 Global AI in Climate Technology Market Outlook, By End User (2023-2034) ($MN)
  • Table 25 Global AI in Climate Technology Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 26 Global AI in Climate Technology Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 27 Global AI in Climate Technology Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 28 Global AI in Climate Technology Market Outlook, By IT & Telecom (2023-2034) ($MN)
  • Table 29 Global AI in Climate Technology Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 30 Global AI in Climate Technology Market Outlook, By Energy & Utilities (2023-2034) ($MN)
  • Table 31 Global AI in Climate Technology Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.