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1856962

全球環境永續發展人工智慧市場：預測至2032年－按解決方案、部署方式、技術、應用和區域分類的分析

AI in Environmental Sustainability Market Forecasts to 2032 - Global Analysis By Solution, Deployment Mode, Technology, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球環境永續性人工智慧市場預計到 2025 年將達到 208 億美元，到 2032 年將達到 811 億美元，預測期內複合年成長率為 21.4%。

人工智慧（AI）在環境永續性方面的應用是指利用先進的演算法、機器學習和數據驅動技術來監測、管理和最佳化自然資源和生態系統。它能夠進行預測分析，用於氣候建模、高效能源管理、污染防治和廢棄物減量。透過分析來自環境感測器、衛星影像和物聯網設備的大型資料集，人工智慧為永續實踐提供科學的決策支援。其應用範圍涵蓋智慧農業、可再生能源最佳化以及生態系統保護，最終目標是提高資源利用效率、減少環境影響並促進長期的生態平衡。

企業永續性舉措

企業正在利用人工智慧來模擬碳足跡、預測能源消耗並最佳化供應鏈排放。與ESG報告平台的整合提高了透明度並加強了監管合規性。人工智慧正在助力製造業和物流業實現預測性維護和循環經濟策略。各行各業對氣候友善技術和綠色人工智慧的投資都在不斷增加。這些能力正在推動企業實現環境智慧化。

資料隱私和安全問題

企業在匯總環境、營運和地理空間資料集時，必須確保遵守當地資料保護法律。雲端基礎的人工智慧模型需要安全的基礎設施和存取控制來防止資料外洩。缺乏標準化的環境資料共用通訊協定，使得相關人員之間的協作變得複雜。這些風險持續限制平台的擴充性和跨部門整合。

社會意識與消費者需求

消費者正在支持那些展現出可衡量的氣候行動和透明度的品牌。人工智慧能夠即時追蹤產品生命週期內的排放、用水量和廢棄物。零售商和製造商正在利用人工智慧來最佳化包裝、物流和能源消耗。與數位孿生和物聯網感測器的整合正在提升可視性和響應速度。這些趨勢正在推動可擴展的、以消費者為中心的永續性策略。

高品質數據的獲取有限

許多地區缺乏標準化的高解析度排放、生物多樣性和氣候風險資料集。政府、學術界和產業界的資料孤島阻礙了模型的訓練和檢驗。不一致的標籤和元資料降低了互通性和可重複使用性。基於不完整或偏差資料訓練的人工智慧模型可能會產生誤導性的結論。這些挑戰持續阻礙人們對永續性分析的信任和提升其效能。

新冠疫情的影響：

疫情一度擾亂環境監測，並減緩了各領域的永續性進程。然而，疫情後的復甦策略強調綠色基礎設施、清潔能源和數位轉型。人工智慧已被用於模擬污染趨勢、最佳化偏遠地區的能源利用以及支援氣候適應性規劃。作為經濟獎勵策略和復甦計畫的一部分，公共和私人對氣候技術的投資都在加速成長。這種轉變正在加速人工智慧與環境永續發展框架的長期融合。

在預測期內，機器學習（ML）將成為最大的細分市場。

由於機器學習 (ML) 在環境領域的模式識別、預測和最佳化方面具有廣泛的適用性，預計在預測期內，ML 細分市場將佔據最大的市場佔有率。 ML 模型已被用於預測能源需求、檢測森林砍伐和模擬氣候情境。與衛星影像、物聯網感測器和氣象資料的整合正在提高準確性和響應速度。供應商正在提供預訓練模型和可自訂的流程，以滿足永續性情境的需求。這些功能正在增強 ML 在環境人工智慧平台中的主導地位。

預計在預測期內，能源效率和最佳化解決方案領域將實現最高的複合年成長率。

預計在預測期內，能源效率與最佳化解決方案領域將實現最高成長率，因為企業都在尋求降低排放和營運成本。人工智慧正在協助建築、工廠和電網實現動態能源管理。預測分析正在幫助公用事業公司平衡負載並整合可再生能源。智慧暖通空調、照明和工業系統正在利用人工智慧來最大限度地減少浪費和停機時間。商業、工業和市政部門對即時最佳化的需求正在不斷成長。這些趨勢正在加速能源領域人工智慧的應用。

比最大的地區

由於北美擁有先進的人工智慧基礎設施、積極的監管參與和對氣候領域的投資，預計在預測期內，北美將佔據最大的市場佔有率。美國和加拿大的公司正在能源、農業和交通運輸領域部署人工智慧，以實現淨零排放目標。聯邦和州政府計畫正在資助人工智慧主導的氣候變遷創新和排放追蹤。領先的人工智慧供應商和研究機構正在推動平台開發。諸如美國證券交易委員會（SEC）的氣候資訊揭露規則等法規結構正在推動排放的普及應用。

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，這主要得益於都市化、能源需求和氣候風險的共同作用。中國、印度、日本和澳洲等國家正在智慧城市、可再生能源和災害應變等領域大力發展人工智慧。政府支持的項目正在推動人工智慧在環境監測和資源管理中的應用。區域新興企業正在推出符合本地區基礎設施和政策需求的客製化人工智慧平台。都市區和鄉村生態系統對可擴展、低成本解決方案的需求日益成長。這些趨勢正在推動人工智慧驅動的永續性市場在亞太地區的成長。

免費客製化服務

訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

  • 公司簡介
    • 對最多三家其他公司進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭基準化分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球環境永續發展人工智慧市場（按解決方案分類）

  • 環境監測系統
  • 氣候建模和模擬工具
  • 排放和污染追蹤平台
  • 廢棄物管理和回收解決方案
  • 能源效率和最佳化解決方案
  • 水資源管理工具
  • 其他解決方案

6. 全球環境永續性人工智慧市場（以部署方式分類）

  • 雲端基礎的
  • 本地部署

7. 全球環境永續性人工智慧市場（按技術分類）

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

8. 全球環境永續性人工智慧市場（按應用領域分類）

  • 氣候變遷減緩
  • 碳足跡和排放監測
  • 可再生能源管理
  • 智慧電網和能源供應
  • 廢棄物和回收最佳化
  • 水質監測
  • 其他用途

9. 全球環境永續性人工智慧市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 公司簡介

  • Microsoft Corporation
  • Google LLC
  • IBM Corporation
  • Amazon Web Services, Inc.（AWS）
  • Apple Inc.
  • Salesforce, Inc.
  • Siemens AG
  • Schneider Electric SE
  • Envision Digital Ltd.
  • Climavision LLC
  • Planet Labs PBC
  • Watershed Technology Inc.
  • Carbon Re Ltd.
  • Cervest Ltd.
  • Tomorrow.io Inc.
Product Code: SMRC31826

According to Stratistics MRC, the Global AI in Environmental Sustainability Market is accounted for $20.8 billion in 2025 and is expected to reach $81.1 billion by 2032 growing at a CAGR of 21.4% during the forecast period. Artificial Intelligence (AI) in Environmental Sustainability refers to the use of advanced algorithms, machine learning, and data-driven technologies to monitor, manage, and optimize natural resources and ecological systems. It enables predictive analytics for climate modeling, efficient energy management, pollution control, and waste reduction. By analyzing large datasets from environmental sensors, satellite imagery, and IoT devices, AI supports informed decision-making for sustainable practices. Its applications range from smart agriculture and renewable energy optimization to ecosystem conservation, ultimately promoting resource efficiency, reducing environmental impact, and fostering long-term ecological balance.

Market Dynamics:

Driver:

Corporate sustainability initiatives

Enterprises are using AI to model carbon footprints, predict energy consumption, and optimize supply chain emissions. Integration with ESG reporting platforms is improving transparency and regulatory alignment. AI is enabling predictive maintenance and circular economy strategies across manufacturing and logistics. Investment in climate tech and green AI is rising across sectors. These capabilities are propelling enterprise-wide environmental intelligence.

Restraint:

Data privacy and security concerns

Organizations must ensure compliance with regional data protection laws when aggregating environmental, operational, and geospatial datasets. Cloud-based AI models require secure infrastructure and access controls to prevent breaches. Lack of standardized protocols for environmental data sharing complicates collaboration across stakeholders. These risks continue to constrain platform scalability and cross-sector integration.

Opportunity:

Public awareness and consumer demand

Consumers are favoring brands that demonstrate measurable climate action and transparency. AI is enabling real-time tracking of emissions, water usage, and waste across product lifecycles. Retailers and manufacturers are using AI to optimize packaging, logistics, and energy consumption. Integration with digital twins and IoT sensors is improving visibility and responsiveness. These trends are fostering scalable and consumer-aligned sustainability strategies.

Threat:

Limited access to quality data

Many regions lack standardized, high-resolution datasets for emissions, biodiversity and climate risk. Data silos across government, academia, and industry hinder model training and validation. Inconsistent labeling and metadata reduce interoperability and reuse. AI models trained on incomplete or biased data may produce misleading insights. These challenges continue to hamper trust and performance in sustainability analytics.

Covid-19 Impact:

The pandemic temporarily disrupted environmental monitoring and delayed sustainability initiatives across sectors. However, post-pandemic recovery strategies have emphasized green infrastructure, clean energy, and digital transformation. AI was used to model pollution trends, optimize energy use in remote operations, and support climate resilience planning. Public and private investment in climate tech accelerated as part of stimulus and recovery packages. These shifts are accelerating long-term integration of AI into environmental sustainability frameworks.

The machine learning (ML) segment is expected to be the largest during the forecast period

The machine learning (ML) segment is expected to account for the largest market share during the forecast period due to its versatility in pattern recognition, forecasting, and optimization across environmental domains. ML models are being used to predict energy demand, detect deforestation, and model climate scenarios. Integration with satellite imagery, IoT sensors, and weather data is improving accuracy and responsiveness. Vendors are offering pre-trained models and customizable pipelines for sustainability use cases. These capabilities are boosting ML's dominance across environmental AI platforms.

The energy efficiency & optimization solutions segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the energy efficiency & optimization solutions segment is predicted to witness the highest growth rate as organizations seek to reduce emissions and operational costs. AI is enabling dynamic energy management across buildings, factories, and grids. Predictive analytics is helping utilities balance load and integrate renewables. Smart HVAC, lighting, and industrial systems are using AI to minimize waste and downtime. Demand for real-time optimization is rising across commercial, industrial, and municipal sectors. These dynamics are accelerating growth across energy-focused AI deployments.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its advanced AI infrastructure, regulatory engagement, and climate investment. U.S. and Canadian firms are deploying AI across energy, agriculture, and transportation to meet net-zero targets. Federal and state programs are funding AI-driven climate innovation and emissions tracking. Presence of leading AI vendors and research institutions is driving platform development. Regulatory frameworks such as the SEC's climate disclosure rules are reinforcing adoption.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as urbanization, energy demand, and climate risk converge. Countries like China, India, Japan, and Australia are scaling AI across smart cities, renewable energy, and disaster resilience. Government-backed programs are supporting AI integration in environmental monitoring and resource management. Local startups are launching AI platforms tailored to regional infrastructure and policy needs. Demand for scalable, low-cost solutions is rising across urban and rural ecosystems. These trends are accelerating regional growth across AI-enabled sustainability markets.

Key players in the market

Some of the key players in AI in Environmental Sustainability Market include Microsoft Corporation, Google LLC, IBM Corporation, Amazon Web Services, Inc. (AWS), Apple Inc., Salesforce, Inc., Siemens AG, Schneider Electric SE, Envision Digital Ltd., Climavision LLC, Planet Labs PBC, Watershed Technology Inc., Carbon Re Ltd., Cervest Ltd. and Tomorrow.io Inc.

Key Developments:

In June 2025, Google partnered with Climate TRACE and WattTime to expand its AI-powered emissions mapping across industrial sectors. The collaboration integrates satellite imagery, sensor data, and machine learning to track real-time CO2 emissions from power plants, transportation hubs, and supply chains. This supports ESG disclosures and climate risk modeling for enterprise clients.

In February 2025, Microsoft released "Accelerating Sustainability with AI", a strategic framework and product suite that includes AI-powered carbon accounting, emissions forecasting, and energy optimization tools. These solutions are embedded in Microsoft Cloud for Sustainability, enabling real-time Scope 1-3 tracking and predictive analytics for climate action.

Solutions Covered:

  • Environmental Monitoring Systems
  • Climate Modeling & Simulation Tools
  • Emission & Pollution Tracking Platforms
  • Waste Management & Recycling Solutions
  • Energy Efficiency & Optimization Solutions
  • Water Resource Management Tools
  • Other Solutions

Deployment Modes Covered:

  • Cloud-Based
  • On-Premise

Technologies Covered:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Deep Learning
  • Computer Vision
  • Natural Language Processing (NLP)
  • Robotic Process Automation (RPA)
  • Other Technologies

Applications Covered:

  • Climate Change Mitigation
  • Carbon Footprint & Emissions Monitoring
  • Renewable Energy Management
  • Smart Grid & Energy Distribution
  • Waste & Recycling Optimization
  • Water Quality Monitoring
  • Other Applications

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 Emerging Markets
  • 3.9 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 in Environmental Sustainability Market, By Solution

  • 5.1 Introduction
  • 5.2 Environmental Monitoring Systems
  • 5.3 Climate Modeling & Simulation Tools
  • 5.4 Emission & Pollution Tracking Platforms
  • 5.5 Waste Management & Recycling Solutions
  • 5.6 Energy Efficiency & Optimization Solutions
  • 5.7 Water Resource Management Tools
  • 5.8 Other Solutions

6 Global AI in Environmental Sustainability Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premise

7 Global AI in Environmental Sustainability Market, By Technology

  • 7.1 Introduction
  • 7.2 Artificial Intelligence (AI)
  • 7.3 Machine Learning (ML)
  • 7.4 Deep Learning
  • 7.5 Computer Vision
  • 7.6 Natural Language Processing (NLP)
  • 7.7 Robotic Process Automation (RPA)
  • 7.8 Other Technologies

8 Global AI in Environmental Sustainability Market, By Application

  • 8.1 Introduction
  • 8.2 Climate Change Mitigation
  • 8.3 Carbon Footprint & Emissions Monitoring
  • 8.4 Renewable Energy Management
  • 8.5 Smart Grid & Energy Distribution
  • 8.6 Waste & Recycling Optimization
  • 8.7 Water Quality Monitoring
  • 8.8 Other Applications

9 Global AI in Environmental Sustainability Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Microsoft Corporation
  • 11.2 Google LLC
  • 11.3 IBM Corporation
  • 11.4 Amazon Web Services, Inc. (AWS)
  • 11.5 Apple Inc.
  • 11.6 Salesforce, Inc.
  • 11.7 Siemens AG
  • 11.8 Schneider Electric SE
  • 11.9 Envision Digital Ltd.
  • 11.10 Climavision LLC
  • 11.11 Planet Labs PBC
  • 11.12 Watershed Technology Inc.
  • 11.13 Carbon Re Ltd.
  • 11.14 Cervest Ltd.
  • 11.15 Tomorrow.io Inc.

List of Tables

  • Table 1 Global AI in Environmental Sustainability Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Environmental Sustainability Market Outlook, By Solution (2024-2032) ($MN)
  • Table 3 Global AI in Environmental Sustainability Market Outlook, By Environmental Monitoring Systems (2024-2032) ($MN)
  • Table 4 Global AI in Environmental Sustainability Market Outlook, By Climate Modeling & Simulation Tools (2024-2032) ($MN)
  • Table 5 Global AI in Environmental Sustainability Market Outlook, By Emission & Pollution Tracking Platforms (2024-2032) ($MN)
  • Table 6 Global AI in Environmental Sustainability Market Outlook, By Waste Management & Recycling Solutions (2024-2032) ($MN)
  • Table 7 Global AI in Environmental Sustainability Market Outlook, By Energy Efficiency & Optimization Solutions (2024-2032) ($MN)
  • Table 8 Global AI in Environmental Sustainability Market Outlook, By Water Resource Management Tools (2024-2032) ($MN)
  • Table 9 Global AI in Environmental Sustainability Market Outlook, By Other Solutions (2024-2032) ($MN)
  • Table 10 Global AI in Environmental Sustainability Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 11 Global AI in Environmental Sustainability Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 12 Global AI in Environmental Sustainability Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 13 Global AI in Environmental Sustainability Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global AI in Environmental Sustainability Market Outlook, By Artificial Intelligence (AI) (2024-2032) ($MN)
  • Table 15 Global AI in Environmental Sustainability Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 16 Global AI in Environmental Sustainability Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 17 Global AI in Environmental Sustainability Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 18 Global AI in Environmental Sustainability Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 19 Global AI in Environmental Sustainability Market Outlook, By Robotic Process Automation (RPA) (2024-2032) ($MN)
  • Table 20 Global AI in Environmental Sustainability Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 21 Global AI in Environmental Sustainability Market Outlook, By Application (2024-2032) ($MN)
  • Table 22 Global AI in Environmental Sustainability Market Outlook, By Climate Change Mitigation (2024-2032) ($MN)
  • Table 23 Global AI in Environmental Sustainability Market Outlook, By Carbon Footprint & Emissions Monitoring (2024-2032) ($MN)
  • Table 24 Global AI in Environmental Sustainability Market Outlook, By Renewable Energy Management (2024-2032) ($MN)
  • Table 25 Global AI in Environmental Sustainability Market Outlook, By Smart Grid & Energy Distribution (2024-2032) ($MN)
  • Table 26 Global AI in Environmental Sustainability Market Outlook, By Waste & Recycling Optimization (2024-2032) ($MN)
  • Table 27 Global AI in Environmental Sustainability Market Outlook, By Water Quality Monitoring (2024-2032) ($MN)
  • Table 28 Global AI in Environmental Sustainability Market Outlook, By Other Applications (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.