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全球水管理人工智慧市场 - 2025 至 2032 年

Global AI in Water Management Market - 2025-2032

出版日期: 2025年02月13日 | 出版商:

DataM Intelligence | 英文 190 Pages | 商品交期: 最快1-2个工作天内

价格

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简介目录

2024 年全球水管理人工智慧市场规模达到 75.4 亿美元，预计到 2032 年将达到 538.5 亿美元，2025-2032 年预测期内的复合年增长率为 27.85%。

全球水管理人工智慧市场正在经历快速成长，这得益于越来越多地采用智慧技术来解决水资源短缺问题并提高营运效率。城市化进程加快、基础设施老化和气候变迁加剧了水资源管理挑战，为人工智慧（AI）解决方案创造了机会。据西门子称，人工智慧技术可以识别 90% 的潜在问题，比传统方法的效率高出三倍。据联合国教科文组织称，这一事实支持了全球永续发展目标，并解决了预计到 2050 年将成长 55% 的用水需求成长问题。

人工智慧系统正在透过预测性维护和自动洩漏检测彻底改变水基础设施管理。根据美国环保署（EPA）的数据，因洩漏而损失的水约占美国每年总用水量的 14-18%。智慧感测器和机器学习演算法使公用事业能够及时检测和处理洩漏，节省数十亿加仑的水。人工智慧综合灌溉系统可减少高达 25% 的用水量，促进永续农业实践并减轻水资源短缺对粮食生产的影响。

亚太地区是水管理人工智慧成长最快的市场。印度、中国和印尼等国家的快速城市化、工业扩张和严重的水资源短缺正在推动人工智慧技术的应用。根据亚洲开发银行（ADB）统计，亚洲超过75%的地区面临水资源短缺，该地区90%以上人口的居住国已经面临迫在眉睫的水危机。该地区的各国政府正大力投资智慧水利基础设施，印度的「水力教育运动」正在推动以人工智慧为基础的水资源保护措施。

动力学

水资源短缺与高效率资源管理

水资源短缺是一场日益严重的全球危机。根据联合国报告，全球有23亿人生活在缺水国家，而且由于人口成长、城市化和气候变化，这一数字预计还会继续上升。人工智慧优化水资源使用和分配的能力提供了一种变革性的解决方案。人工智慧技术与物联网设备结合，可即时监测水质、压力和流速。据 IBM 公司称，即时资料分析可以主动应对供水系统的异常情况，从而减少高达 15% 的水浪费。

采用人工智慧漏水检测系统，结合云端资讯管理，对供水管网进行监测，漏水率达全球最低。同样，机器学习模型可以分析历史资料来预测用水需求并预防系统故障。例如，欧洲水务与能源署报告称，95% 的欧洲公民可以获得高品质的饮用水，其中 82% 的公民已接入废水处理系统，透过开发数位环境和资料处理能力来优化水处理、供水和废水收集与处理

提高企业永续发展目标

美国水利基础设施老化是一个严重的问题，美国土木工程师学会 (ASCE) 报告称，由于管道老化，每天约有 60 亿加仑的处理过的水流失。为了应对这项挑战，人们正在利用人工智慧技术，尤其是数位孪生技术来模拟水系统的行为。这使得公用事业公司能够更有效地优先安排维修和升级，从而提高营运效率和资源管理。此外，人工智慧在洩漏检测和预测性维护方面发挥关键作用，使公用事业公司能够主动管理基础设施并减少停机时间，最终延长水资产的使用寿命。

随着气候变迁加剧，人工智慧对于调整水管理系统以适应极端天气事件变得越来越重要。例如，日本国土交通省利用人工智慧预测洪水风险，成功减少了与灾害相关的水系统故障。透过利用即时资料和高级分析，人工智慧帮助公用事业优化资源配置并提高应对气候引发的挑战的能力。人工智慧的整合不仅提高了供水服务的可靠性和可持续性，而且还满足了为应对日益增长的需求和环境压力而迫切需要实现现代化的需要。

数据品质问题和高实施成本

人工智慧在水管理市场的整合存在一些重大限制，可能会阻碍其有效性和广泛应用。数据品质问题至关重要；人工智慧系统严重依赖高品质的资料输入，但由于基础设施过时和监控能力不足，许多水务公司面临资料集不一致或不完整的挑战。缺乏可靠资料会严重限制人工智慧模型的预测能力，从而降低其优化水资源管理的整体效能。

此外，高昂的实施成本对许多公用事业构成了障碍，特别是在预算限制普遍存在的发展中地区。人工智慧技术所需的初始投资（包括硬体、软体和培训）可能会阻止组织采用这些解决方案。此外，由于许多现有基础设施与现代技术不相容，与传统系统的整合使向人工智慧驱动流程的过渡变得复杂。最后，劳动力往往缺乏有效实施和利用人工智慧系统的技术技能和知识，这进一步加剧了水务公司在利用人工智慧改善管理方面所面临的挑战。

The global AI in water management market is experiencing rapid growth, driven by the increasing adoption of smart technologies to address water scarcity and improve operational efficiencies. Rising urbanization, aging infrastructure and climate change exacerbate water management challenges, creating opportunities for artificial intelligence (AI) solutions. According to Siemens AI-driven technologies identify 90% of potential issues, which makes it three times more effective than traditional methods. This fact supports global sustainability goals and addresses the growing water demand projected to rise by 55% by 2050, according to UNESCO.

AI-enabled systems are revolutionizing water infrastructure management through predictive maintenance and automated leak detection. According to the U.S. Environmental Protection Agency (EPA), water loss from leaks accounts for approximately 14-18% of total water usage annually in US. Smart sensors and machine learning algorithms enable utilities to detect and address leaks promptly, saving billions of gallons of water. AI-integrated irrigation systems can reduce water consumption by up to 25%, promoting sustainable agricultural practices and mitigating the impacts of water scarcity on food production.

Asia-Pacific is the fastest-growing market for AI in water management. Rapid urbanization, industrial expansion and severe water scarcity in countries like India, China and Indonesia are driving the adoption of AI technologies. According to the Asian Development Bank (ADB), over 75% of Asia is water insecure, with countries that are home to more than 90% of the region's population already facing an imminent water crisis. Governments in the region are investing heavily in smart water infrastructure, with India's "Jal Shakti Abhiyan" promoting AI-based water conservation initiatives.

Dynamics

Water Scarcity and Efficient Resource Management

Water scarcity is a growing global crisis. The United Nations reports that 2.3 billion people live in water-stressed countries, with the figure expected to rise due to population growth, urbanization and climate change. AI's ability to optimize water usage and distribution offers a transformative solution. AI technologies integrated with IoT devices allow for real-time monitoring of water quality, pressure and flow rates. According to the IBM Corporation, real-time data analytics can reduce water waste by up to 15% by enabling proactive responses to irregularities in water systems.

AI-based water leak detection system with cloud information management for monitoring its water distribution network, achieving a leakage rate, among the lowest globally. Similarly, machine learning models analyze historical data to predict water demand and preempt system failures. For instance, the EurEau reports that 95% of European citizens have access to high-quality drinking water while 82% of them are connected to wastewater treatment, by developing a digital environment and data treatment capabilities to optimize water treatment, water distribution and wastewater collection and treatment

Increasing Corporate Sustainability Goals

The aging water infrastructure in the U.S. is a significant concern, with the American Society of Civil Engineers (ASCE) reporting that approximately 6 billion gallons of treated water are lost daily due to deteriorating pipelines. To address this challenge, AI technologies, particularly digital twins, are being utilized to simulate the behavior of water systems. This allows utilities to prioritize repairs and upgrades more effectively, enhancing operational efficiency and resource management. Additionally, AI plays a critical role in leak detection and predictive maintenance, enabling utilities to proactively manage infrastructure and reduce downtime, ultimately extending the lifespan of water assets.

As climate change intensifies, AI is increasingly vital for adapting water management systems to extreme weather events. For instance, Japan's Ministry of Land, Infrastructure, Transport and Tourism employs AI to predict flood risks, successfully reducing disaster-related failures in water systems. By leveraging real-time data and advanced analytics, AI helps utilities optimize resource allocation and improve resilience against climate-induced challenges. This integration of AI not only enhances the reliability and sustainability of water services but also addresses the urgent need for modernization in response to growing demands and environmental pressures.

Data Quality Issues with High Implementation Costs

The integration of AI in the water management market presents several significant restraints that can hinder its effectiveness and widespread adoption. Data Quality Issues are paramount; AI systems rely heavily on high-quality data inputs, yet many water utilities face challenges with inconsistent or incomplete datasets due to outdated infrastructure and insufficient monitoring capabilities. This lack of reliable data can severely limit the predictive capabilities of AI models, reducing their overall effectiveness in optimizing water resource management.

Additionally, high implementation costs pose a barrier for many utilities, particularly in developing regions where budget constraints are prevalent. The initial investments required for AI technologies, including hardware, software and training, can deter organizations from adopting these solutions. Furthermore, integration with legacy systems complicates the transition to AI-driven processes, as many existing infrastructures are not compatible with modern technologies. Lastly, there is often a lack of technical skills and knowledge within the workforce to effectively implement and utilize AI systems, which further exacerbates the challenges faced by water utilities in leveraging AI for improved management.

Segment Analysis

The global AI in water management market is segmented based on offering, technology, capacity, end-user and region.

Rising Demand for AI-based Water Management in Power Generation Industry

The smart water utilities sector is rapidly evolving, driven by the need for enhanced operational efficiency and reduced water losses. Artificial Intelligence (AI) plays a crucial role in optimizing water treatment and distribution processes. Wastewater treatment plant uses AI to reduce aeration energy use by 30%. Furthermore, AI systems are instrumental in addressing the challenge of non-revenue water (NRW), which represents substantial losses in the water sector.

A notable example is a pilot project in South Africa's eThekwini Municipality, where AI helped decrease NRW in two years, demonstrating the technology's effectiveness in detecting unauthorized usage and leaks. Additionally, AI enhances customer engagement through smart billing systems that provide real-time insights into water consumption. AI-driven portals have improved customer satisfaction significantly while minimizing billing discrepancies.

The integration of AI into water management not only fosters operational improvements but also facilitates data-driven decision-making for long-term resource planning and allocation. By leveraging advanced analytics and predictive capabilities, utilities can optimize their operations, respond to demand fluctuations and ultimately contribute to sustainable water management practices..

Geographical Penetration

Availability of Advanced Technological Eco-Systemin North America

North America is leading the global AI-driven water management market, primarily due to its advanced technological ecosystem and supportive regulatory environment. The AI-integrated water systems have enhanced efficiency in urban utilities. Cities such as Las Vegas and Los Angeles are utilizing AI to optimize water distribution, resulting in significant reductions in per capita water consumption.

Furthermore, government initiatives like the EPA's Water Infrastructure Finance and Innovation Act (WIFIA) are providing essential funding for smart water projects, facilitating the adoption of AI technologies. In Canada, the "Smart Water Canada" initiative has contributed to an improvement in municipal water management efficiency since 2020.

The industrial sector is also witnessing robust AI adoption in water management. For example, GM's new system can recycle and reuse 85% of wastewater, cutting the complex's daily groundwater extraction in half while increasing operations across its North American facilities. This trend is indicative of a broader movement towards sustainable practices, driven by increasing concerns over water scarcity and aging infrastructure.

As North America continues to invest heavily in smart water solutions, it is expected to maintain its dominance in the market, with projected growth fueled by advancements in digital technologies and regulatory support aimed at enhancing efficiency and sustainability in water resource management.

Competitive Landscape

The major global players in the market include Honeywell International Inc, Schneider Electric, IBM Corp., ABB, TATA Consultancy Services Limited oracle, SUEZ, Landis+Gyr, Siemens and Larsen & Toubro.

Sustainable Analysis

The integration of artificial intelligence (AI) in water management is significantly contributing to the achievement of the United Nations Sustainable Development Goal 6, which focuses on clean water and sanitation. AI systems enhance water conservation by optimizing usage, leading to a reported 22% reduction in water wastage across various pilot projects globally, according to the International Institute for Sustainable Development (IISD).

Additionally, these systems improve energy efficiency in water treatment processes; the AI-enabled wastewater treatment solutions saved millions of kWh of water annually in pilot initiatives. By utilizing predictive analytics, AI also enhances pollution control by improving wastewater treatment efficiency, with the European Commission's Horizon 2020 initiative indicating a 15% improvement in water quality standards through AI-driven monitoring. Beyond efficiency gains, AI technologies are reshaping how utilities manage water resources. They enable real-time data analysis to predict water demand, optimize supply and reduce waste effectively.

Furthermore, AI can identify leaks and other infrastructure issues before they escalate into crises, allowing for timely repairs and maintenance. This proactive approach not only conserves water but also minimizes operational costs and environmental impacts. As these technologies continue to evolve, their role in ensuring sustainable water management becomes increasingly vital, particularly in addressing global challenges such as climate change and population growth that threaten water security.

Integration of IoT and AI in AI in Water Management

The integration of IoT (Internet of Things) and AI (Artificial Intelligence) in water management systems is transforming how utilities operate by providing real-time data and actionable insights. According to the World Economic Forum, IoT-connected sensors can reduce operational inefficiencies by up to 30% in water systems. These technologies enable automation in critical areas such as irrigation, leak detection and water quality monitoring. For instance, Israel's Mekorot National Water Company successfully reduced irrigation water usage, through the integration of AI and IoT.

Furthermore, AI enhances decision-making capabilities by analyzing large datasets, with a significant percent reduction in response times to water crises in pilot implementations. The benefits of these technologies extend beyond efficiency gains; they also facilitate better resource management and sustainability. IoT systems provide continuous monitoring that allows for early leak detection and optimized water use, which can significantly lower maintenance costs.

Additionally, AI-driven analytics help predict infrastructure failures, enabling proactive maintenance and minimizing downtime. As a result, water utilities can respond more effectively to challenges such as aging infrastructure and changing environmental conditions, ultimately leading to improved service delivery and conservation efforts across the sector.

Key Developments

In October 2024, FIDO AI Ltd, a software company dedicated to reducing non-revenue water (NRW) through AI-led solutions and curated water data insights, announced the successful closing of its Series B funding round. This investment will enable FIDO AI to scale its operations and continue its mission to support global water resilience.
In July 2023, SUEZ, a leader in sustainable water and waste services, joined forces with Schneider Electric to drive the adoption of digital solutions focused on energy efficiency, resource conservation and minimizing the carbon footprint of water cycle management systems. This collaboration emphasizes the importance of digital transformation in addressing water management challenges.
In June 2023, SUEZ also partnered with Northumbrian Water Ltd. (NWL) on the Hydro Powered Concentric Smart Meter Project, which was awarded the Ofwat Innovation Fund's Catalyst Stream prize. This groundbreaking project aims to revolutionize water usage monitoring and management through innovative smart metering technology.

By Offering

Water Meter
Solutions
- Smart Irrigation Management
- Advanced Pressure Management
- Mobile Workforce Management
- Network Management
- CIS and Billing
- Leak Detection
- Other Solutions
Services
- Integration & Deployment
- Support & Maintenance
- Consulting

By Capacity

Less than 25,000L,
25,001 TO 50,000 L
50,001 L TO 100,000 L
More than 100,001 L

By Technology

Internet of Things (IoT) Devices
Remote Sensing and Satellite Imagery
Data Analytics and Big Data
Cloud Computing Solutions
Others

By End-User

Municipal
- Urban Water Management
- Rural Water Supply Programs
Industrial
- Power Generation
- Food and Beverage
- Pharmaceuticals
- Textiles and Leather
- Pulp and Paper
- Oil and Gas
- Mining and Metals
- Others
Commercial
Others

By Region

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Why Purchase the Report?

To visualize the global AI in water management market segmentation based on offering, technology, capacity, end-user and region.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points at the AI in water management market level for all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global AI in water management market report would provide approximately 70 tables, 66 figures and 190 pages.

Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Offering
3.2. Snippet by Capacity
3.3. Snippet by Technology
3.4. Snippet by End-User
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Adoption of the Water Reuse and Recycle from Diverse Industries
  - 4.1.1.2. Increasing Corporate Sustainability Goals
- 4.1.2. Restraints
  - 4.1.2.1. High Initial Investment
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Sustainable Analysis
5.6. DMI Opinion

6. By Offering

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 6.1.2. Market Attractiveness Index, By Offering
6.2. Water Meter*
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Solutions
- 6.3.1. Smart Irrigation Management
- 6.3.2. Advanced Pressure Management
- 6.3.3. Mobile Workforce Management
- 6.3.4. Network Management
- 6.3.5. CIS and Billing
- 6.3.6. Leak Detection
- 6.3.7. Other Solutions
6.4. Services
- 6.4.1. Integration & Deployment
- 6.4.2. Support & Maintenance
- 6.4.3. Consulting

7. By Capacity

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 7.1.2. Market Attractiveness Index, By Capacity
7.2. Less than 25,000L*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. 25,001 TO 50,000 L
7.4. 50,001 L TO 100,000 L
7.5. More than 100,001 L

8. By Technology

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 8.1.2. Market Attractiveness Index, By Technology
8.2. Internet of Things (IoT) Devices*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Remote Sensing and Satellite Imagery
8.4. Data Analytics and Big Data
8.5. Cloud Computing Solutions
8.6. Others

9. By End-User

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 9.1.2. Market Attractiveness Index, By End-User
9.2. Municipal*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  - 9.2.2.1. Urban Water Management
  - 9.2.2.2. Rural Water Supply Programs
9.3. Industrial
- 9.3.1. Power Generation
- 9.3.2. Food and Beverage
- 9.3.3. Pharmaceuticals
- 9.3.4. Textiles and Leather
- 9.3.5. Pulp and Paper
- 9.3.6. Oil and Gas
- 9.3.7. Mining and Metals
- 9.3.8. Others
9.4. Commercial
9.5. Others

10. Sustainability Analysis

10.1. Environmental Analysis
10.2. Economic Analysis
10.3. Governance Analysis

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. US
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. UK
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Spain
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. Honeywell International Inc*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Schneider Electric
13.3. IBM Corp.
13.4. ABB
13.5. TATA Consultancy Services Limited
13.6. Oracle
13.7. SUEZ
13.8. Landis+Gyr
13.9. Siemens
13.10. Larsen & Toubro

LIST NOT EXHAUSTIVE

14. Appendix

14.1. About Us and Services
14.2. Contact Us

02-2729-4219

+886-2-2729-4219

License/价格

全球水管理人工智慧市场 - 2025 至 2032 年

Global AI in Water Management Market - 2025-2032

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

第五章：产业分析

第 6 章：奉献

第七章：按容量

第 8 章：按技术

第 9 章：按最终用户

第 10 章：可持续性分析

第 11 章：按地区

第 12 章：竞争格局

第 13 章：公司简介

第 14 章：附录