AI in Waste Management and Recycling Market Size was valued at USD 3.5 Bn in 2023 and is predicted to reach USD 15.9 Bn by 2031 at a 20.8% CAGR during the forecast period for 2024-2031.
The application of artificial intelligence (AI) to recycling and waste management has recently emerged as a key engine of development and innovation. The generation of waste is increasing along with the world's population, which presents severe obstacles to sustainable environmental practices and waste management. As a result, the industry's adoption of AI technologies has demonstrated encouraging outcomes in streamlining operations, improving recycling procedures, and lessening environmental impact overall. The optimization of waste collection routes is one of the significant uses of AI in waste management. Artificial intelligence (AI) algorithms can analyse real-time data from multiple sources, such as waste bin sensors and GPS trackers, to determine the most effective collection routes. IoT-enabled smart waste bins with AI capabilities have become a workable option for optimizing waste management. These smart containers can communicate real-time data to waste collection centres and track fill levels.
The market is driven by the growing need for environmentally friendly waste management solutions, the acceptance of AI in waste sorting and recycling, and the rising demand for waste management tools with AI capabilities. Government regulations that promote environmentally friendly waste management techniques and technical developments that make it possible to integrate artificial intelligence (AI) into sophisticated waste management systems are driving the market's growth. Strategic AI integration improves operational effectiveness and advances the more general objectives of environmental sustainability.
The AI in waste management and recycling market is segmented on the basis of type, application, and end-user. Based on type, the market is segmented as machine learning-based AI solutions, computer vision-based AI solutions, natural language processing-based AI solutions, and robotics and automation in waste management. The market is segmented by application into waste sorting and segregation, recycling process optimization, waste collection route planning, predictive maintenance of recycling machinery, waste monitoring and analysis, smart bin technology, energy recovery from waste, landfill management, and monitoring. Based on end-users, the market is segmented into municipalities and local governments, waste management companies, recycling facilities and plants, the industrial and commercial sector, and the residential sector.
AI solutions based on machine learning make it possible to analyze large amounts of data and identify patterns, which increases the efficiency and accuracy of garbage sorting. Through image processing technology, computer vision-based AI systems enable the identification and sorting of recyclable elements from mixed waste streams. Artificial Intelligence systems based on natural language processing help with garbage monitoring and collection route optimization by enabling improved textual data analysis and communication. Additionally, automated waste sorting is provided by robotics and automation in waste management, which lowers the need for human intervention and increases recycling productivity.
AI's sophisticated algorithms and computer vision skills are beneficial to waste sorting and segregation since they allow for accurate waste type identification and separation for effective recycling. Another important application of AI is recycling process optimization, which uses data analysis to increase resource efficiency, decrease waste production, and improve recycling efficiency. Waste collection route planning optimizes collection routes using AI-driven analytics to ensure timely and economical waste pickup while lowering fuel and carbon emissions. Reducing downtime and increasing operational efficiency are two major benefits of predictive maintenance for recycling technology. It also helps prevent problems and optimize machinery performance.
Regarding AI in the waste management and recycling industry, North America dominated the market. Government regulations that promote environmentally friendly waste management techniques and technical developments that make it possible to integrate artificial intelligence (AI) into sophisticated waste management systems are driving the market's growth. Strategic AI integration improves operational effectiveness and advances the more general objectives of environmental sustainability. Due to the European Union's implementation of various policies to promote recycling and reduce waste, including the waste framework directive and the circular economy package, the market is expanding rapidly in the European region.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 3.5 Bn |
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Revenue Forecast In 2031 |
USD 15.9 Bn |
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Growth Rate CAGR |
CAGR of 20.8% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, By Application, By End-user |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Waste Management, Inc., Veolia, SUEZ, Covanta Holding Corporation, IBM Corporation, Google LLC, Microsoft Corporation, Amazon Web Services, Inc., RecycleSmart, ZenRobotics Ltd. (Terex Corporation), Tomra Systems ASA, Enevo Oy, Ecube Labs Co., Ltd., RoadRunner Recycling, Inc., Rubicon Technology, AMP Robotics, Waste Robotics, Machinex Industries Inc., CleanRobotics Inc., Sortera Alloys, Brain Corporation, Bioelektra Group, RecyGlo Limited, Rezatec Limited, and Others. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Waste Management and Recycling Market Snapshot
Chapter 4. Global AI in Waste Management and Recycling Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
5.2.1. Machine Learning-based AI Solutions
5.2.2. Computer Vision-based AI Solutions
5.2.3. Natural Language Processing-based AI Solutions
5.2.4. Robotics and Automation in Waste Management
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:
6.2.1. Waste Sorting and Segregation
6.2.2. Recycling Process Optimization
6.2.3. Waste Collection Route Planning
6.2.4. Predictive Maintenance of Recycling Machinery
6.2.5. Waste Monitoring and Analysis
6.2.6. Smart Bin Technology
6.2.7. Energy Recovery from Waste
6.2.8. Landfill Management and Monitoring
Chapter 7. Market Segmentation 3: by End-User Estimates & Trend Analysis
7.1. by End-User & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End-User:
7.2.1. Municipalities and Local Governments
7.2.2. Waste Management Companies
7.2.3. Recycling Facilities and Plants
7.2.4. Industrial and Commercial Sector
7.2.5. Residential Sector
Chapter 8. AI in Waste Management and Recycling Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.1.2. North America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.1.3. North America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2023-2031
8.1.4. North America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.2. Europe
8.2.1. Europe AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.2.2. Europe AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.2.3. Europe AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2023-2031
8.2.4. Europe AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.3. Asia Pacific
8.3.1. Asia Pacific AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.3.2. Asia Pacific AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.3.3. Asia-Pacific AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2023-2031
8.3.4. Asia Pacific AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.4. Latin America
8.4.1. Latin America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.4.2. Latin America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.4.3. Latin America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2023-2031
8.4.4. Latin America AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.5.2. Middle East & Africa AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.5.3. Middle East & Africa AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2023-2031
8.5.4. Middle East & Africa AI in Waste Management and Recycling Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IBM Corporation
9.2.2. Microsoft Corporation
9.2.3. Google LLC
9.2.4. Amazon Web Services (AWS)
9.2.5. Waste Management, Inc.
9.2.6. Rubicon Technology
9.2.7. RecycleSmart Solutions
9.2.8. Enevo Oy
9.2.9. ZenRobotics Ltd.
9.2.10. Bigbelly, Inc.
9.2.11. AMP Robotics Corporation
9.2.12. Veolia Environnement S.A.
9.2.13. Other Prominent Players
AI in Waste Management and Recycling Market- By Type
AI in Waste Management and Recycling Market- By Application
AI in Waste Management and Recycling Market- By End-user
AI in Waste Management and Recycling Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
