Global AI in Environmental Sustainability Market Size was valued at USD 17.2 Bn in 2024 and is predicted to reach USD 100.3 Bn by 2034 at a 19.4% CAGR during the forecast period for 2025 to 2034.
AI in Environmental Sustainability Market Size, Share & Trends Analysis Report, By Type (Machine Learning, Natural Language Processing (NLP), Computer Vision, Deep Learning, Expert Systems and Robotics and Automation); By Application, By End-User Industry, By Region, Forecasts, 2025 to 2034
The field of environmental sustainability is rapidly transforming due to artificial intelligence (AI). This potent technology is used to combat several issues, such as resource conservation and climate change. The incorporation of AI in environmental activities has started to show encouraging effects. Climate modelling and prediction are two main areas where AI has a big impact. Scientists may now create more accurate climate forecasts due to the analysis of enormous datasets by sophisticated AI algorithms. Thus, catastrophic weather occurrences like storms, droughts, and wildfires are better anticipated and governments and groups lessen their consequences. AI is not only improving prediction but also resource management. With AI, for example, optimizing energy use in buildings is becoming more effective.
AI in environmental sustainability is being driven by several factors, including technological advancements, rising investment in AI, growing strategic collaboration among the market players, and many others. However, the market's growth is restricted by variables like a shortage of skilled workforce, data privacy and security concerns, technical limitations, and others. Furthermore, advancements in NLP and consumer demand for green products are major potential opportunities for market growth during the projected period.
The AI in environmental sustainability market is segmented based on type, application and end-use industry. Based on type, the market is segmented as machine learning, natural language processing (NLP), computer vision, deep learning, expert systems and robotics and automation. By application, the market is segmented into climate change mitigation, renewable energy optimization, environmental monitoring and assessment, waste management and recycling, emission reduction and control, conservation and biodiversity, smart agriculture and precision farming, water management and conservation, sustainable urban planning and green building and energy efficiency. Based on end-user industry, the industry is bifurcated into energy and utilities, agriculture, transportation and logistics, manufacturing, healthcare and life sciences, government and public sector, retail and consumer goods, education and research and others.
The computer vision category is expected to hold a significant share of the global AI market in environmental sustainability. The industry is expanding due to ongoing advancements in computer vision technology, including stronger machine-learning models, more sophisticated image-processing algorithms, and higher-resolution cameras. These developments improve the precision and effectiveness of environmental monitoring applications. Furthermore, computer vision technologies for environmental sustainability are growing in several industries, including forestry, urban planning, and agriculture. A more comprehensive range of applications leads to an increase in revenue.
The energy and utilities segment is projected to grow rapidly in the global AI in environmental sustainability market. The rising worldwide energy demand fuels the need for more sustainable and effective energy management solutions. AI reduces environmental effects while assisting in meeting this demand. AI applications in the industry are also expanding due to encouraging government regulations and incentives for using renewable energy sources and energy efficiency. Subsidies and regulations promote investment in AI-powered solutions. Additionally, a sizable amount of public, private, and venture capital financing supports the advancement and application of AI technology in the energy and utility sectors. This investment fuels innovation and market growth.
The North American AI in the environmental sustainability market is expected to register the highest market share in revenue in the near future. AI is being heavily funded by corporate funds, government subsidies, and venture capital for environmental sustainability. The industry is developing and innovating thanks to this financial backing. Additionally, several initiatives and regulations at the federal and state levels encourage the creation and application of AI technology for environmental sustainability. Proposals such as Canada's Climate Plan and the United States' Green New Deal promote the application of cutting-edge technologies to environmental problems. Additionally, top research centres and colleges in North America are advancing the area by conducting state-of-the-art studies on AI applications for environmental sustainability. In addition, Asia Pacific is projected to grow rapidly in the global AI in environmental sustainability market due to rising investment by the market players.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 14.6 Bn |
| Revenue Forecast In 2031 | USD 56.9 Bn |
| Growth Rate CAGR | CAGR of 19.1% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, By Application, By End-Use Industry and By Region |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | Microsoft Corporation, IBM Corporation, Google LLC, Amazon Web Services (AWS), Intel Corporation, NVIDIA Corporation, Siemens AG, General Electric (GE), Schneider Electric SE, Accenture plc, Oracle Corporation, Enablon (Wolters Kluwer), SAP SE, C3.ai Inc., SAS Institute Inc., ABB Ltd., Wipro Limited, Hitachi, Ltd., Cisco Systems, Inc., Envision Energy, CleanCloud Technologies, Huawei Technologies Co., Ltd., Ecobot, ClimateAI, Green Energy Hub, and Others. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Environmental Sustainability Market- By Type
AI in Environmental Sustainability Market- By Application
AI in Environmental Sustainability Market- By End-Use Industry
AI in Environmental Sustainability Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.