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1654698

ESG 與永續發展領域的全球人工智慧市場 - 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個工作天內

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簡介目錄

2024 年，ESG 和永續發展市場中的全球人工智慧規模達到 1,823.4 億美元，預計到 2032 年將達到 8,467.5 億美元，在 2025-2032 年預測期內的複合年成長率為 21.16%。

人工智慧 (AI) 融入環境、社會和治理 (ESG) 策略正在徹底改變企業的永續發展和道德實踐方法。生成式人工智慧使 ESG 團隊能夠透過分析巨量資料集、識別績效風險和提供實現目標的客製化建議來利用廣泛的機會。該系統簡化了ESG戰略制定、目標設立、實施和報告的複雜、以數據為中心的程序。

AI 對多個 ESG 維度而言都至關重要。在環境管理方面，人工智慧改善了消費和廢棄物管理舉措，同時促進了碳減排和綜合報告。人工智慧提供有關多元化、公平性和包容性措施以及供應鏈採購和社會和治理因素的見解。這些應用提高了透明度，增強了利害關係人的信心。一項民意調查顯示，95% 的知識工作者認為更透明的 ESG 報告可以增強人們對公司永續發展計畫的信任。

人工智慧透過制定策略來減少消耗和浪費，從而降低成本和生態影響，提供財務和環境優勢。 Net Zero Cloud 等 ESG 管理工具已整合人工智慧，以提高企業對其環境影響的運算和報告的準確性。此外，人工智慧也使企業能夠在 ESG 框架內進行創新，創造新的機會並提高品牌聲譽。 AI在ESG的應用，不僅加速了進步，也增強了市場差異化。

動力學

促進因素 1：利用人工智慧減少碳排放並實現永續的商業實踐

人工智慧（AI）融入環境、社會和治理（ESG）計畫正在推動永續發展事業取得顯著進展。人工智慧透過分析大量資料集來實現永續評估自動化的能力是這場革命的重要驅動力。大型語言模型（LLM）（包括 GPT）評估全球暖化的影響並提出永續策略，使公司能夠成功地確定需要改進的領域。

人工智慧能夠評估來自運輸和能源使用等多種來源的資料，使組織能夠確定準確的碳足跡，從而提高精度和效率，同時降低營運費用。人工智慧透過最佳化能源消耗和物流，為減少碳足跡做出了巨大貢獻。包括預測分析在內的人工智慧驅動技術可協助組織確定最永續的運輸路線，從而大幅減少溫室氣體排放。

即時監控能源消耗使企業能夠實施動態修改，從而顯著節約能源並減少碳排放。人工智慧透過提高可視性、最佳化路線和減少浪費來增強永續供應鏈管理。機器學習演算法根據環境標準評估供應商，促進道德採購和透明度。它可以提高公司的聲譽並確保遵守日益嚴格的 ESG 法律，同時降低法律風險。透過利用人工智慧，組織可以促進創新並實現持久的永續發展目標，同時遵守環境法規。

促進因素 2 - 監管環境推動 ESG 中 AI 的採用

全球政府和監管機構正在製定更嚴格的 ESG 揭露要求，要求企業提高其報告能力。人工智慧驅動的解決方案對於組織有效評估大量 ESG 資訊、保證合規性和提高透明度變得越來越重要。

歐盟的企業永續發展報告指令（CSRD）要求更多企業進行全面的永續發展揭露，從而建立全球基準。國際永續發展標準委員會 (ISSB) 正在製定與永續發展相關的揭露的統一框架，為投資者提供有關 ESG 風險和可能性的一致資訊。國際財務報告準則基金會的司法管轄區採用指南促進了全球監管的一致性，並保證了跨司法管轄區的統一永續性報告。

國家層級的監管架構多元。英國將在2025年前強制要求披露氣候相關的財務資訊，而美國則在州一級出現了支持和反對ESG立法相結合的局面，導致全球公司的合規環境變得複雜。隨著法規日益嚴格，人工智慧驅動的 ESG 解決方案對於實現合規自動化、減輕報告義務和加強企業永續發展計畫至關重要。利用人工智慧實現 ESG 合規的組織將透過提高透明度、降低監管風險和增強投資者信任來獲得競爭優勢。

限制：網路安全和資料隱私風險

處理重大敏感 ESG資料（包括環境、社會和治理指標）的人工智慧系統更容易受到網路攻擊。人工智慧被納入全球報告計劃組織 (GRI) 和永續發展會計準則委員會 (SASB) 等 ESG 報告框架，凸顯了網路安全是一個重要議題。

網路攻擊引發了與 ESG 相關的重大擔憂。例如，2021 年，駭客入侵了佛羅裡達州的一個水處理設施，遠端操縱化學濃度；近年來，對一家德國鋼鐵公司的網路攻擊導致其高爐關閉，危及工人的安全。一年前，美國 FDA 因安全漏洞撤回了 50 萬個心臟起搏器，而 2020 年德國的勒索軟體攻擊導致一家醫院的急診室關閉，並導致一名患者死亡。

網路安全人員的短缺加劇了這種情況，阻礙了企業建立有效保護措施的能力。隨著網路攻擊越來越集中在發電廠和水處理設施等重要基礎設施上，監管監督預計將加強，從而對將人工智慧融入 ESG 計畫提出挑戰。這些危險阻礙了市場成長，需要更強大的網路安全標準。

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