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1654698

ESG 与永续发展领域的全球人工智慧市场 - 2025 至 2032 年

Global AI in ESG & Sustainability Market - 2025-2032

出版日期: 2025年02月13日 | 出版商:

DataM Intelligence | 英文 203 Pages | 商品交期: 最快1-2个工作天内

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简介目录

2024 年，ESG 和永续发展市场中的全球人工智慧规模达到 1,823.4 亿美元，预计到 2032 年将达到 8,467.5 亿美元，在 2025-2032 年预测期内的复合年增长率为 21.16%。

人工智慧 (AI) 融入环境、社会和治理 (ESG) 策略正在彻底改变企业的永续发展和道德实践方法。生成式人工智慧使 ESG 团队能够透过分析大数据集、识别绩效风险和提供实现目标的客製化建议来利用广泛的机会。该系统简化了ESG战略制定、目标设立、实施和报告的复杂、以数据为中心的程序。

AI 对多个 ESG 维度而言都至关重要。在环境管理方面，人工智慧改善了消费和废弃物管理倡议，同时促进了碳减排和综合报告。人工智慧提供有关多元化、公平性和包容性措施以及供应链采购和社会和治理因素的见解。这些应用提高了透明度，增强了利害关係人的信心。一项民意调查显示，95% 的知识工作者认为更透明的 ESG 报告可以增强人们对公司永续发展计画的信任。

人工智慧透过制定策略来减少消耗和浪费，从而降低成本和生态影响，提供财务和环境优势。 Net Zero Cloud 等 ESG 管理工具已整合人工智慧，以提高企业对其环境影响的运算和报告的准确性。此外，人工智慧也使企业能够在 ESG 框架内进行创新，创造新的机会并提高品牌声誉。 AI在ESG的应用，不仅加速了进步，也增强了市场差异化。

动力学

驱动因素 1：利用人工智慧减少碳排放并实现永续的商业实践

人工智慧（AI）融入环境、社会和治理（ESG）计画正在推动永续发展事业取得显着进展。人工智慧透过分析大量资料集来实现永续评估自动化的能力是这场革命的重要驱动力。大型语言模型（LLM）（包括 GPT）评估全球暖化的影响并提出永续策略，使公司能够成功地确定需要改进的领域。

人工智慧能够评估来自运输和能源使用等多种来源的资料，使组织能够确定准确的碳足迹，从而提高精度和效率，同时降低营运费用。人工智慧透过优化能源消耗和物流，为减少碳足迹做出了巨大贡献。包括预测分析在内的人工智慧驱动技术可协助组织确定最永续的运输路线，从而大幅减少温室气体排放。

即时监控能源消耗使企业能够实施动态修改，从而显着节约能源并减少碳排放。人工智慧透过提高可视性、优化路线和减少浪费来增强永续供应链管理。机器学习演算法根据环境标准评估供应商，促进道德采购和透明度。它可以提高公司的声誉并确保遵守日益严格的 ESG 法律，同时降低法律风险。透过利用人工智慧，组织可以促进创新并实现持久的永续发展目标，同时遵守环境法规。

驱动因素 2 - 监管环境推动 ESG 中 AI 的采用

全球政府和监管机构正在製定更严格的 ESG 揭露要求，要求企业提高其报告能力。人工智慧驱动的解决方案对于组织有效评估大量 ESG 资讯、保证合规性和提高透明度变得越来越重要。

欧盟的企业永续发展报告指令（CSRD）要求更多企业进行全面的永续发展揭露，从而建立全球基准。国际永续发展标准委员会 (ISSB) 正在製定与永续发展相关的揭露的统一框架，为投资者提供有关 ESG 风险和可能性的一致资讯。国际财务报告准则基金会的司法管辖区采用指南促进了全球监管的一致性，并保证了跨司法管辖区的统一永续性报告。

国家层级的监管架构多元。英国将在2025年前强制要求披露气候相关的财务信息，而美国则在州一级出现了支持和反对ESG立法相结合的局面，导致全球公司的合规环境变得复杂。随着法规日益严格，人工智慧驱动的 ESG 解决方案对于实现合规自动化、减轻报告义务和加强企业永续发展计画至关重要。利用人工智慧实现 ESG 合规的组织将透过提高透明度、降低监管风险和增强投资者信任来获得竞争优势。

限制：网路安全和资料隐私风险

处理重大敏感 ESG资料（包括环境、社会和治理指标）的人工智慧系统更容易受到网路攻击。人工智慧被纳入全球报告倡议组织 (GRI) 和永续发展会计准则委员会 (SASB) 等 ESG 报告框架，凸显了网路安全是一个重要议题。

网路攻击引发了与 ESG 相关的重大担忧。例如，2021 年，骇客入侵了佛罗里达州的一个水处理设施，远端操纵化学浓度；近年来，对一家德国钢铁公司的网路攻击导致其高炉关闭，危及工人的安全。一年前，美国 FDA 因安全漏洞撤回了 50 万个心臟起搏器，而 2020 年德国的勒索软体攻击导致一家医院的急诊室关闭，并导致一名患者死亡。

网路安全人员的短缺加剧了这种情况，阻碍了企业建立有效保护措施的能力。随着网路攻击越来越集中在发电厂和水处理设施等重要基础设施上，监管监督预计将加强，从而对将人工智慧融入 ESG 计画提出挑战。这些危险阻碍了市场成长，需要更强大的网路安全标准。

The use of Artificial Intelligence (AI) into Environmental, Social and Governance (ESG) strategies is revolutionizing corporate approaches to sustainability and ethical practices. Generative AI empowers ESG teams to capitalize on extensive opportunities through the analysis of big datasets, the identification of performance risks and the provision of customized suggestions for target attainment. This system streamlines the intricate, data-centric procedure of ESG strategy formulation, objective establishment, implementation and reporting.

AI is integral to multiple ESG dimensions. In environmental management, AI improves consumption and waste management initiatives while facilitating carbon reduction and comprehensive reporting. AI provides insights on diversity, equity and inclusion measures, along with supply chain sourcing and social and governance factors. The applications enhance transparency, fostering confidence among stakeholders. A poll indicated that 95% of knowledge workers assert that more transparent ESG reporting enhances trust in a company's sustainability initiatives.

AI offers financial and environmental advantages by pinpointing strategies to minimize consumption and waste, thereby reducing costs and ecological impacts. ESG management tools, such as Net Zero Cloud, have integrated AI to enhance the accuracy of firms' calculations and reporting of their environmental impact. Furthermore, AI empowers firms to innovate within ESG frameworks, creating new opportunities and improving brand reputation. The application of AI in ESG not only expedites advancement but also enhances market differentiation.

Dynamics

Driver 1 - Leveraging AI for carbon reduction and sustainable business practices

The incorporation of Artificial Intelligence (AI) into Environmental, Social and Governance (ESG) projects is propelling notable progress in sustainability endeavors. The capacity of AI to automate sustainability evaluations through the analysis of extensive datasets is a significant driver of this revolution. Large language models (LLMs), including GPTs, evaluate the effects of global warming and propose sustainable strategies, allowing companies to successfully identify areas for enhancement.

AI's ability to evaluate data from many sources, such as transportation and energy use, enables organizations to determine accurate carbon footprints, thereby improving both precision and efficiency while lowering operational expenses. Artificial intelligence significantly contributes to minimizing carbon footprints through the optimization of energy consumption and logistics. AI-driven technologies, including predictive analytics, assist organizations in determining the most sustainable delivery routes, thereby substantially reducing greenhouse gas emissions.

Real-time monitoring of energy consumption enables companies to implement dynamic modifications, resulting in significant energy savings and a decrease in carbon emissions. AI augments sustainable supply chain management by enhancing visibility, optimizing routing and reducing waste. Machine learning algorithms evaluate suppliers according to environmental standards, facilitating ethical sourcing and transparency. It enhances a company's reputation and assures adherence to growing ESG laws, while reducing legal risks. Through the utilization of AI organizations can foster innovation and attain enduring sustainability objectives while complying with environmental regulations.

Driver 2 - Regulatory landscape driving AI adoption in ESG

Global governments and regulatory agencies are enacting more stringent ESG disclosure mandates, necessitating firms to improve their reporting proficiency. AI-driven solutions are increasingly vital for organizations to effectively evaluate extensive ESG information, guarantee compliance and enhance transparency.

The European Union's Corporate Sustainability Reporting Directive (CSRD) requires comprehensive sustainability disclosures from a wider array of corporations, establishing a global benchmark. The International Sustainability Standards Board (ISSB) is developing a cohesive framework for sustainability-related disclosures, offering investors consistent information regarding ESG risks and possibilities. The IFRS Foundation's jurisdictional adoption guide facilitates global regulatory coherence, guaranteeing uniform sustainability reporting across jurisdictions.

The regulatory framework at the national level is varied. The UK will mandate climate-related financial disclosures by 2025, whereas the US is witnessing a combination of pro- and anti-ESG legislation at the state level, resulting in a convoluted compliance landscape for global firms. With the increasing stringency of regulations, AI-driven ESG solutions will be essential for automating compliance, alleviating reporting obligations and enhancing corporate sustainability plans. Organizations utilizing AI for ESG compliance will acquire a competitive advantage by improving transparency, reducing regulatory risks and bolstering investor trust.

Restraint: Cybersecurity and data privacy risks

AI systems handling significant sensitive ESG data, including environmental, social and governance indicators, are more susceptible to cyber attacks. The incorporation of AI in ESG reporting frameworks like the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) has underscored cybersecurity as a significant issue.

Cyberattacks pose substantial ESG-related concerns. For instance, In 2021, hackers breached a Florida water treatment facility, manipulating chemical concentrations remotely and in recent years, a cyberattack on a German steel company compelled the shutdown of a blast furnace, endangering worker safety. A year prior, the US FDA withdrew 500,000 pacemakers owing to security flaws, while a 2020 ransomware assault in Germany resulted in the closure of a hospital emergency department, leading to a patient's mortality.

The shortage of cybersecurity personnel intensifies the situation, hindering firms' ability to establish effective protection measures. As cyberattacks increasingly focus on vital infrastructure, including power plants and water treatment facilities, regulatory oversight is anticipated to intensify, hence challenging the integration of AI into ESG plans. These dangers impede market growth and require more robust cybersecurity standards.

Segment Analysis

The global AI in ESG & sustainability market is segmented based on technology, deployment, organization size, end-user and region.

AI-Driven Sustainability in Energy & Utility Sector

The energy and utility sector is a major consumer of AI in ESG and sustainability, utilizing AI-driven solutions for carbon footprint reduction, energy efficiency, water conservation and system modernization. Artificial Intelligence facilitates real-time surveillance, predictive analysis and automated reporting, assisting utilities in achieving ESG objectives while enhancing resource management efficiency. The incorporation of AI in renewable energy forecasts, smart grids and advanced metering infrastructure (AMI) improves operational efficiency and sustainability initiatives.

Regulatory frameworks, such the EU's Corporate Sustainability Reporting Directive (CSRD) and the US Securities and Exchange Commission (SEC) climate disclosure regulations, impose rigorous ESG reporting requirements on energy corporations. AI-driven technologies assist utilities in adhering to rules by automating data acquisition and guaranteeing precise sustainability reporting. AI is essential in enhancing ESG initiatives within the energy industry, driven by the emergence of microgrids, IoT, blockchain and carbon capture technologies. The advances promote efficiency, diminish environmental impact and improve regulatory compliance, cultivating a sustainable future.

Geographical Penetration

North America's AI Role in advancing ESG & sustainability goals

North America leads in AI adoption for ESG and sustainability, driven by major technology firms and rising regulatory focus on sustainable practices. ESG software platforms like as Enablon, Intelex and Sphera provide real-time tracking and reporting of sustainability parameters, consolidating data from multiple sources for an integrated assessment of performance. These platforms are essential for optimizing data collection, analysis and reporting through customisable templates, hence assisting firms in effectively achieving ESG objectives.

Cloud-based data management solutions from Microsoft Azure and Google Cloud have enhanced this industry by providing scalable and effective platforms for the storage and management of extensive ESG datasets. These technologies enable firms, particularly those with intricate supply chains, to automate data entry and swiftly discern trends, hence improving decision-making and transparency with stakeholders.

Artificial intelligence and machine learning tools are helpful in evaluating vast datasets to forecast and enhance variables such as carbon emissions and energy consumption. For example, Microsoft's AI-powered technologies monitor carbon emissions to assist in achieving its carbon-negative objective by 2030. Blockchain technology is increasingly being adopted, exemplified by Unilever's implementation to enhance supply chain transparency, foster trust among stakeholders and validate sustainability assertions.

Competitive Landscape

The major Global players in the market include Algotec Green Technology, Gross-Wen Technologies (GWT), Liqoflux, Agromorph, Xylem Inc., Valicor Environmental Services, Algenuity originClear Inc., Evodos B.V. and MicroBio Engineering Inc.

By Technology

Machine Learning (ML)
Natural Language Processing (NLP)
Deep Learning
Predictive Analytics
Generative AI
Others

By Deployment

Cloud-based Solutions
On-premises Solutions

By Organization Size

Small and Medium Enterprises (SMEs)
Large Enterprises

By End-User

Energy & Utilities
Manufacturing
Retail
Financial Services
Healthcare
Information Technology
Consumer Goods
Government & Public Sector
Others

By Region

North America
South America
Europe
Asia-Pacific
Middle East and Africa

Key Developments

In January 14, 2024, the Capgemini Research Institute's released their paper on the sustainability of generative AI, titled 'Developing Sustainable Gen AI', indicates that generative AI has a substantial and increasing adverse environmental impact. As enterprises evaluate the capacity of generative AI to enhance company growth in relation to the technology's environmental impact, the paper delineates strategies for formulating a responsible and sustainable generative AI approach.

Why Purchase the Report?

To visualize the global AI in ESG & sustainability market segmentation based on technology, deployment, organization size, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of the AI in ESG & Sustainability market with 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 ESG & sustainability market report would provide approximately 62 tables, 54 figures and 203 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 Technology
3.2. Snippet by Deployment
3.3. Snippet by Organization Size
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. Leveraging AI for carbon reduction and sustainable business practices
  - 4.1.1.2. Regulatory landscape driving AI adoption in ESG
- 4.1.2. Restraints
  - 4.1.2.1. Cybersecurity and data privacy risks
- 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. DMI Opinion

6. By Technology

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 6.1.2. Market Attractiveness Index, By Technology
6.2. Machine Learning (ML)*
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Natural Language Processing (NLP)
6.4. Deep Learning
6.5. Predictive Analytics
6.6. Generative AI
6.7. Others

7. By Deployment

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 7.1.2. Market Attractiveness Index, By Deployment
7.2. Cloud-based Solutions*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. On-premises Solutions

8. By Organization Size

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 8.1.2. Market Attractiveness Index, By Organization Size
8.2. Small and Medium Enterprises (SMEs)*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Large Enterprises

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. Energy & Utilities*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Manufacturing
9.4. Retail
9.5. Financial Services
9.6. Healthcare
9.7. Information Technology
9.8. Consumer Goods
9.9. Government & Public Sector
9.10. Others

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.7.1. US
  - 10.2.7.2. Canada
  - 10.2.7.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.7.1. Germany
  - 10.3.7.2. UK
  - 10.3.7.3. France
  - 10.3.7.4. Italy
  - 10.3.7.5. Spain
  - 10.3.7.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.7.1. Brazil
  - 10.4.7.2. Argentina
  - 10.4.7.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.7.1. China
  - 10.5.7.2. India
  - 10.5.7.3. Japan
  - 10.5.7.4. Australia
  - 10.5.7.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

11.1. Competitive Scenario
11.2. Market Positioning/Share Analysis
11.3. Mergers and Acquisitions Analysis

12. Company Profiles

12.1. Salesforce*
- 12.1.1. Company Overview
- 12.1.2. Product Portfolio and Description
- 12.1.3. Financial Overview
- 12.1.4. Key Developments
12.2. Microsoft
12.3. IBM
12.4. Google Cloud
12.5. SAP
12.6. Oracle
12.7. Accenture
12.8. PwC
12.9. C3.ai
12.10. Honeywell

LIST NOT EXHAUSTIVE