AI in Governance - Solid Waste Management for Sustainable Future
Indian School of Public Policy (ISPP)
Democratising public policy & building state capacities
GOI launched Smart City Mission in 2015 to enhance the quality of life by providing efficient, sustainable, and technology-driven solutions for cities while addressing resident’s needs and aspirations. Solid Waste Management (SWM) is one of the integral parts of the mission for creating a healthy and workable environment. India generates over 62 million tonnes of waste annually, which is expected to increase by 70% by 205,0 as mentioned by World Bank’s "What a Waste 2.0" report (2018). Hence, addressing this critical issue is the need of the hour.?
Recycling and segregation of the waste at source are important stages of the waste management cycle but significant challenges often lead to waste dumping and creating other problems of overflowing landfills. The root cause of the tumultuous segregation process is the poorly labelled and overflowing dustbins. Residents often can’t differentiate between the colour of the dustbins and are unaware of which type of waste belongs to which category. Simplifying and Indianizing Dustbin Categorization as follows: a) Green Bin: "Harit Kachra", b) Blue Bin: "Punarachit Kachra" for dry, recyclable waste, c) Red Bin: "Khatarnaak Kachra" for hazardous waste, such as medical or electronic waste and using illustrations with common waste items for clarity (e.g., banana peels, paper, plastic bottles) can be a solution but given that behavioural nudging is a long process, there’s need of an urgent solution.?
AI is a game-changing force at a time when the global waste crisis necessitates immediate action. Artificial intelligence (AI) elevates trash management beyond simple robot sorting to a complex, data-driven process. The use of cutting-edge technology like AI is crucial for improving waste management, including waste-to-energy conversion and real-time waste collection monitoring and promoting circular economy, according to Dr. Rajiv Kumar, vice-chairperson of NITI Aayog. Sensor-equipped smart bins that are linked to the Internet of Things (IoT) can solve the problem of overflowing dustbins. To ensure bins are emptied before overflow, these bins can track fill levels and coordinate pickups with garbage collection services. Manual sorting of recyclables often results in high contamination rates but the use of sophisticated computer vision and machine learning algorithms, IoT, and robotic systems can recognise and classify various materials with up to 90% accuracy. Municipalities often face the challenge of inefficient collection routes and unpredictable waste volumes. By analysing data on waste generation patterns, traffic conditions, and bin locations, AI can design collection routes. Real-time route adjustments can improve waste collection efficiency, lower fuel usage, and reduce emissions and operating expenses.?
Several countries like South Korea and Japan have already integrated AI successfully in managing their wastes to become efficiently sustainable. In South Korea, one will come across AI smart bins that are fitted with sensors, enabling them to monitor how much waste they are storing. Identification of when the bins are full is alerted by the sensor for the waste disposal to take place and thus prevent the accumulation of garbage. The IoT-connected bins lead to at least the monitoring of real-time in which management may still optimise the way of collection and truck routing, which would include optimising times and routes based on current traffic conditions as well as analysing historical waste patterns. Automation of waste classification and
even lift programs can be conducted using computer vision, as well as machine-learning algorithms to determine and categorise waste materials as something greater; it boosts recycling rates. Meanwhile, in Japan, AI has been recognised as being used to automate the sorting procedures of recycling facilities with robotic arms and artificial vision up to approximately 90% accuracy in material identification. This can enable the optimal processes for incinerating with very high energy recovery while applying AI in a waste-to-energy plant in the country. Raising the public's awareness and thorough implementation in the segregation of municipal solid waste is necessary for the full bloom of AI use in domestic waste management daily with such technologies.?
India’s approach must be practical, sustainable, and according to its socio-economic realities. India should adopt a decentralised approach by starting with pilot projects in metro cities and then moving to Tier-II and Tier-III cities. With effective AI integration, robust public engagement, and behavioural nudges, India would not only address its waste management issues but also serve as a model for sustainable urban planning.?
Written by: Pooja Yadav, PDM Scholar Class of 2025
Footnotes:-
● Government of India. (2015). Smart Cities Mission Guidelines. Ministry of Urban Development.?
https://pib.gov.in/PressNoteDetails.aspx?NoteId=151908&ModuleId=3®=3&lang=1 The objective of SCM is to promote cities that provide core infrastructure and give a decent quality of life to its citizens, a clean and sustainable environment through the application of 'Smart' solutions.?
● Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. The World Bank.?
The report also stresses sustainable solutions for waste minimization.?
● Kumar, R. (2022). Remarks on AI and Waste Management in Public Policy. NITI Aayog Publications?
https://www.niti.gov.in/sites/default/files/2023-03/National-Strategy-for-Artificial-Intelligence.pdf Dr. Rajiv Kumar emphasizes AI's transformative role in addressing urban issues like waste management.?
● Case Studies on AI in Waste Management: South Korea and Japan. (2021). Global Waste Management Review.?
https://www.forbes.com/sites/ganeskesari/2024/05/31/turning-trash-into-treasure-how-ai-is-revol utionizing-waste-sorting/?
These strategies provide valuable insights for India's urban planning.