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市场调查报告书
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1856962

全球环境永续发展人工智慧市场：预测至2032年－按解决方案、部署方式、技术、应用和区域分類的分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球环境永续性人工智慧市场预计到 2025 年将达到 208 亿美元，到 2032 年将达到 811 亿美元，预测期内复合年增长率为 21.4%。

人工智慧（AI）在环境永续性方面的应用是指利用先进的演算法、机器学习和数据驱动技术来监测、管理和优化自然资源和生态系统。它能够进行预测分析，用于气候建模、高效能源管理、污染防治和废弃物减量。透过分析来自环境感测器、卫星影像和物联网设备的大型资料集，人工智慧为永续实践提供科学的决策支援。其应用范围涵盖智慧农业、可再生能源优化以及生态系统保护，最终目标是提高资源利用效率、减少环境影响并促进长期的生态平衡。

企业永续性倡议

企业正在利用人工智慧来模拟碳足迹、预测能源消耗并优化供应链排放。与ESG报告平台的整合提高了透明度并加强了监管合规性。人工智慧正在助力製造业和物流业实现预测性维护和循环经济策略。各行各业对气候友善技术和绿色人工智慧的投资都在不断增加。这些能力正在推动企业实现环境智慧化。

资料隐私和安全问题

企业在汇总环境、营运和地理空间资料集时，必须确保遵守当地资料保护法律。云端基础的人工智慧模型需要安全的基础设施和存取控制来防止资料外洩。缺乏标准化的环境资料共用通讯协定，使得相关人员之间的协作变得复杂。这些风险持续限制平台的扩充性和跨部门整合。

社会意识与消费者需求

消费者正在支持那些展现出可衡量的气候行动和透明度的品牌。人工智慧能够即时追踪产品生命週期内的排放、用水量和废弃物。零售商和製造商正在利用人工智慧来优化包装、物流和能源消耗。与数位孪生和物联网感测器的整合正在提升可视性和响应速度。这些趋势正在推动可扩展的、以消费者为中心的永续性策略。

高品质数据的获取有限

许多地区缺乏标准化的高解析度排放、生物多样性和气候风险资料集。政府、学术界和产业界的资料孤岛阻碍了模型的训练和检验。不一致的标籤和元资料降低了互通性和可重复使用性。基于不完整或偏差资料训练的人工智慧模型可能会产生误导性的结论。这些挑战持续阻碍人们对永续性分析的信任和提升其效能。

新冠疫情的影响：

疫情一度扰乱环境监测，并减缓了各领域的永续性进程。然而，疫情后的復苏策略强调绿色基础设施、清洁能源和数位转型。人工智慧已被用于模拟污染趋势、优化偏远地区的能源利用以及支援气候适应性规划。作为经济奖励策略和復苏计画的一部分，公共和私人对气候技术的投资都在加速成长。这种转变正在加速人工智慧与环境永续发展框架的长期融合。

在预测期内，机器学习（ML）将成为最大的细分市场。

由于机器学习 (ML) 在环境领域的模式识别、预测和优化方面具有广泛的适用性，预计在预测期内，ML 细分市场将占据最大的市场份额。 ML 模型已被用于预测能源需求、检测森林砍伐和模拟气候情境。与卫星影像、物联网感测器和气象资料的整合正在提高准确性和响应速度。供应商正在提供预训练模型和可自订的流程，以满足永续性情境的需求。这些功能正在增强 ML 在环境人工智慧平台中的主导地位。

预计在预测期内，能源效率和最佳化解决方案领域将实现最高的复合年增长率。

预计在预测期内，能源效率与优化解决方案领域将实现最高成长率，因为企业都在寻求降低排放和营运成本。人工智慧正在协助楼宇、工厂和电网实现动态能源管理。预测分析正在帮助公用事业公司平衡负载并整合可再生能源。智慧暖通空调、照明和工业系统正在利用人工智慧来最大限度地减少浪费和停机时间。商业、工业和市政部门对即时优化的需求正在不断增长。这些趋势正在加速能源领域人工智慧的应用。

比最大的地区

由于北美拥有先进的人工智慧基础设施、积极的监管参与和对气候领域的投资，预计在预测期内，北美将占据最大的市场份额。美国和加拿大的公司正在能源、农业和交通运输领域部署人工智慧，以实现净零排放目标。联邦和州政府计画正在资助人工智慧主导的气候变迁创新和排放追踪。领先的人工智慧供应商和研究机构正在推动平台开发。诸如美国证券交易委员会（SEC）的气候资讯揭露规则等法规结构正在推动排放的普及应用。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于都市化、能源需求和气候风险的共同作用。中国、印度、日本和澳洲等国家正在智慧城市、可再生能源和灾害应变等领域大力发展人工智慧。政府支持的项目正在推动人工智慧在环境监测和资源管理中的应用。区域新兴企业正在推出符合本地区基础设施和政策需求的客製化人工智慧平台。都市区和乡村生态系统对可扩展、低成本解决方案的需求日益增长。这些趋势正在推动人工智慧驱动的永续性市场在亚太地区的成长。

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  • 公司简介
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    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球环境永续发展人工智慧市场（按解决方案划分）

  • 环境监测系统
  • 气候建模和模拟工具
  • 排放和污染追踪平台
  • 废弃物管理和回收解决方案
  • 能源效率和优化解决方案
  • 水资源管理工具
  • 其他解决方案

6. 全球环境永续性人工智慧市场（以部署方式划分）

  • 云端基础的
  • 本地部署

7. 全球环境永续性人工智慧市场（按技术划分）

  • 人工智慧（AI）
  • 机器学习（ML）
  • 深度学习
  • 电脑视觉
  • 自然语言处理（NLP）
  • 机器人流程自动化 (RPA)
  • 其他技术

8. 全球环境永续性人工智慧市场（按应用领域划分）

  • 气候变迁减缓
  • 碳足迹和排放监测
  • 可再生能源管理
  • 智慧电网和能源供应
  • 废弃物和回收优化
  • 水质监测
  • 其他用途

9. 全球环境永续性人工智慧市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 公司简介

  • 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.