AI in Climate Change Mitigation Market Top Competitors Analysis Report 2025 – InsightAce Analytic

Global AI in Climate Change Mitigation Market Report

Chapter 1: Methodology and Scope

1. Research Methodology
2. Research Scope & Assumptions

Chapter 2: Executive Summary

Chapter 3: Global AI in Climate Change Mitigation Market Snapshot

Chapter 4: Market Variables, Trends & Scope

1. Market Segmentation & Scope
2. Drivers
3. Challenges
4. Trends
5. Investment and Funding Analysis
6. Porter’s Five Forces Analysis
7. Incremental Opportunity Analysis (US$ MN), 2025-2034
8. Market Penetration & Growth Prospect Mapping (US$ Mn), 2024-2034
9. Competitive Landscape & Market Share Analysis by Key Player (2024)
10. Use and Impact of AI on Climate Change Mitigation Industry Trends

Chapter 5: Market Segmentation 1 – By Component

1. Market Share by Component, 2024 & 2034
2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for:
   • Hardware
   • Software
   • Services

Chapter 6: Market Segmentation 2 – By Deployment Mode

1. Market Share by Deployment Mode, 2024 & 2034
2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for:
   • Cloud-Based
   • On-Premises

Chapter 7: Market Segmentation 3 – By Application

1. Market Share by Application, 2024 & 2034
2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for:
   • Carbon Footprint Management
   • Energy Optimization
   • Climate Risk Assessment
   • Sustainable Agriculture

Chapter 8: Market Segmentation 4 – By End-User Industry

1. Market Share by End-User Industry, 2024 & 2034
2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for:
   • Energy and Utilities
   • Transportation
   • Agriculture
   • Construction

Chapter 9: Market Segmentation 5 – Regional Estimates & Trend Analysis

1. Global Market Regional Snapshot 2024 & 2034
2. North America
   • Revenue Estimates and Forecasts by Country (US, Canada), 2021-2034
   • Revenue Estimates and Forecasts by Component, Deployment Mode, Application, End-User Industry, 2021-2034
3. Europe
   • Revenue Estimates and Forecasts by Country (Germany, U.K., France, Italy, Spain, Rest of Europe), 2021-2034
   • Revenue Estimates and Forecasts by Component, Deployment Mode, Application, End-User Industry, 2021-2034
4. Asia Pacific
   • Revenue Estimates and Forecasts by Country (India, China, Japan, Australia, South Korea, Hong Kong, Southeast Asia, Rest of Asia Pacific), 2021-2034
   • Revenue Estimates and Forecasts by Component, Deployment Mode, Application, End-User Industry, 2021-2034
5. Latin America
   • Revenue Estimates and Forecasts by Country (Brazil, Mexico, Rest of Latin America), 2021-2034
   • Revenue Estimates and Forecasts by Component, Deployment Mode, Application, End-User Industry, 2021-2034
6. Middle East & Africa
   • Revenue Estimates and Forecasts by Country (GCC Countries, Israel, South Africa, Rest of Middle East and Africa), 2021-2034
   • Revenue Estimates and Forecasts by Component, Deployment Mode, Application, End-User Industry, 2021-2034

Chapter 10: Competitive Landscape

1. Major Mergers and Acquisitions / Strategic Alliances
2. Company Profiles
   • Google
     • Business Overview
     • Key Products and Services
     • Financial Performance
     • Geographical Presence
     • Recent Developments and Business Strategy
   • IBM Corporation
   • Microsoft
   • Tesla
   • Siemens
   • Carbon Clean Solutions
   • DeepMind
   • C3 AI
   • Xpansiv
   • Predikto
   • Elemental Excelerator
   • Enel
   • Schneider Electric
   • Amazon Web Services
   • Autogrid

Emphasis on Sustainable Development Goals (SDGs)

The Global AI in Climate Change Mitigation Market plays a critical role in advancing multiple United Nations Sustainable Development Goals (SDGs), including:

• SDG 7: Affordable and Clean Energy – AI technologies optimize energy consumption and promote renewable energy integration.
• SDG 9: Industry, Innovation, and Infrastructure – AI fosters innovation in climate technologies and sustainable infrastructure development.
• SDG 11: Sustainable Cities and Communities – AI applications contribute to climate risk assessment and urban resilience.
• SDG 12: Responsible Consumption and Production – AI enhances carbon footprint management and resource efficiency.
• SDG 13: Climate Action – The core focus of the market is to mitigate climate change impacts through advanced AI solutions.
• SDG 15: Life on Land – AI supports sustainable agriculture practices that protect ecosystems and biodiversity.

By integrating AI in climate change mitigation efforts, stakeholders across industries and regions contribute to achieving these SDGs, promoting a sustainable and resilient future globally.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 7: Affordable and Clean Energy – The article discusses AI applications in energy optimization and energy and utilities industries, which relate to ensuring access to affordable, reliable, sustainable, and modern energy.
2. SDG 9: Industry, Innovation and Infrastructure – The focus on AI technology, hardware, software, services, and deployment modes indicates innovation and infrastructure development.
3. SDG 11: Sustainable Cities and Communities – AI applications in transportation and construction sectors contribute to sustainable urban development.
4. SDG 12: Responsible Consumption and Production – Carbon footprint management and sustainable agriculture applications relate to responsible consumption and production patterns.
5. SDG 13: Climate Action – The entire article centers on AI in climate change mitigation, directly addressing climate action.
6. SDG 2: Zero Hunger – Sustainable agriculture as an application supports food security and sustainable agriculture.

2. Specific Targets Under Those SDGs Identified

1. SDG 7
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
   • Target 7.3: Double the global rate of improvement in energy efficiency.
2. SDG 9
   • Target 9.5: Enhance scientific research, upgrade technological capabilities of industrial sectors.
3. SDG 11
   • Target 11.2: Provide access to safe, affordable, accessible and sustainable transport systems for all.
   • Target 11.6: Reduce the adverse per capita environmental impact of cities.
4. SDG 12
   • Target 12.2: Achieve the sustainable management and efficient use of natural resources.
   • Target 12.4: Achieve environmentally sound management of chemicals and wastes.
5. SDG 13
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters.
   • Target 13.2: Integrate climate change measures into national policies, strategies and planning.
6. SDG 2
   • Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices.

3. Indicators Mentioned or Implied to Measure Progress

1. Carbon Footprint Management – Implies indicators such as reduction in CO2 emissions, carbon intensity per unit of GDP or production.
2. Energy Optimization – Indicators like energy consumption per capita, energy efficiency improvements, share of renewable energy.
3. Climate Risk Assessment – Indicators related to climate vulnerability and resilience metrics.
4. Sustainable Agriculture – Indicators such as percentage of agricultural land under sustainable practices, crop yield improvements, resource use efficiency.
5. Market Size and Growth Forecasts – Financial indicators such as market revenue (US$ Million), investment and funding analysis, and market penetration rates can indirectly measure progress in AI deployment for climate mitigation.
6. Deployment Modes and Components – Indicators on adoption rates of cloud-based vs. on-premises AI solutions, hardware vs. software usage.

4. Table of SDGs, Targets and Indicators

Source: insightaceanalytic.com