AI in Climate Change Mitigation Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services), By Deployment Mode (Cloud-Based, On-Premises), By End-Use Industry (Energy & Utilities, Transportation, Agriculture, Construction, Government and Public Sectors), By Application (Carbon Footprint Management, Energy Optimization, Climate Risk Assessment, Sustainable Agriculture), By Region, And By Segment Forecasts, 2025-2034

Report Id: 3124 Pages: 180 Last Updated: 30 June 2025 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Climate Change Mitigation Market-

AI in Climate Change Mitigation Market By Component-

  • Services
  • Hardware
  • Software

AI in Climate Change Mitigation Market

AI in Climate Change Mitigation Market By Deployment Mode-

  • Cloud-Based
  • On-Premises

AI in Climate Change Mitigation Market By Application-

  • Energy Optimization
  • Carbon Footprint Management
  • Climate Risk Assessment
  • Sustainable Agriculture

AI in Climate Change Mitigation Market By End-User-

  • Construction
  • Energy & Utilities
  • Agriculture
  • Transportation
  • Government and Public Sectors

AI in Climate Change Mitigation Market By Region-

North America-

  • The US
  • Canada

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of the Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Climate Change Mitigation Market Snapshot

Chapter 4. Global AI in Climate Change Mitigation Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2025-2034
4.8. Global AI in Climate Change Mitigation Market Penetration & Growth Prospect Mapping (US$ Mn), 2024-2034
4.9. Competitive Landscape & Market Share Analysis, By Key Player (2024)
4.10. Use/impact of AI on AI IN CLIMATE CHANGE MITIGATION MARKET Industry Trends

Chapter 5. AI in Climate Change Mitigation Market Segmentation 1: By Component, Estimates & Trend Analysis
5.1. Market Share by Component, 2024 & 2034
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Component:

5.2.1. Hardware
5.2.2. Software
5.2.3. Services

Chapter 6. AI in Climate Change Mitigation Market Segmentation 2: By Deployment Mode, Estimates & Trend Analysis
6.1. Market Share by Deployment Mode, 2024 & 2034
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Deployment Mode:

6.2.1. Cloud-Based
6.2.2. On-Premises

Chapter 7. AI in Climate Change Mitigation Market Segmentation 3: By Application, Estimates & Trend Analysis
7.1. Market Share by Application, 2024 & 2034
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Application:

7.2.1. Carbon Footprint Management
7.2.2. Energy Optimization
7.2.3. Climate Risk Assessment
7.2.4. Sustainable Agriculture

Chapter 8. AI in Climate Change Mitigation Market Segmentation 4: By End-User Industry, Estimates & Trend Analysis
8.1. Market Share by End-User Industry, 2024 & 2034
8.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following End-User Industry:

8.2.1. Energy and Utilities
8.2.2. Transportation
8.2.3. Agriculture
8.2.4. Construction

Chapter 9. AI in Climate Change Mitigation Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. Global AI in Climate Change Mitigation Market, Regional Snapshot 2024 & 2034
9.2. North America

9.2.1. North America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034

9.2.1.1. US
9.2.1.2. Canada

9.2.2. North America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
9.2.3. North America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
9.2.4. North America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.2.5. North America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034

9.3. Europe

9.3.1. Europe AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034

9.3.1.1. Germany
9.3.1.2. U.K.
9.3.1.3. France
9.3.1.4. Italy
9.3.1.5. Spain
9.3.1.6. Rest of Europe

9.3.2. Europe AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
9.3.3. Europe AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
9.3.4. Europe AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.3.5. Europe AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034

9.4. Asia Pacific

9.4.1. Asia Pacific AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034

9.4.1.1. India
9.4.1.2. China
9.4.1.3. Japan
9.4.1.4. Australia
9.4.1.5. South Korea
9.4.1.6. Hong Kong
9.4.1.7. Southeast Asia
9.4.1.8. Rest of Asia Pacific

9.4.2. Asia Pacific AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
9.4.3. Asia Pacific AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
9.4.4. Asia Pacific AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts By Application, 2021-2034
9.4.5. Asia Pacific AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034

9.5. Latin America

9.5.1. Latin America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034

9.5.1.1. Brazil
9.5.1.2. Mexico
9.5.1.3. Rest of Latin America

9.5.2. Latin America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
9.5.3. Latin America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
9.5.4. Latin America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.5.5. Latin America AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034

9.6. Middle East & Africa

9.6.1. Middle East & Africa AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

9.6.1.1. GCC Countries
9.6.1.2. Israel
9.6.1.3. South Africa
9.6.1.4. Rest of Middle East and Africa

9.6.2. Middle East & Africa AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
9.6.3. Middle East & Africa AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
9.6.4. Middle East & Africa AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.6.5. Middle East & Africa AI in Climate Change Mitigation Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034

Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles

10.2.1. Google

10.2.1.1. Business Overview
10.2.1.2. Key Product/Service
10.2.1.3. Financial Performance
10.2.1.4. Geographical Presence
10.2.1.5. Recent Developments with Business Strategy

10.2.2. IBM Corporation
10.2.3. Microsoft
10.2.4. Tesla
10.2.5. Siemens
10.2.6. Carbon Clean Solutions
10.2.7. DeepMind
10.2.8. C3 Ai
10.2.9. Xpansiv
10.2.10. Predikto
10.2.11. Elemental Excelerator
10.2.12. Enel
10.2.13. Schneider Electric
10.2.14. Amazon Web Service
10.2.15. Autogrid

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

AI in Climate Change Mitigation Market size is valued at USD 16.7 Bn in 2024 and is predicted to reach USD 80.6 Bn by the year 2034

AI in Climate Change Mitigation Market is expected to grow at a 17.3% CAGR during the forecast period for 2025-2034.

Amazon Web Service, Google, IBM Corporation, Siemens, DeepMind, C3.Ai, Predikto, Elemental Excelerator, Microsoft, Tesla, Carbon Clean Solutions, Enel

Component, Deployment Mode, Application, and End-User are the key segments of the AI in Climate Change Mitigation Market.

North America region is leading the AI in Climate Change Mitigation Market.

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