Artificial Intelligence (AI), commonly associated with self-driving vehicles, robots, and ChatGPT, is transforming waste management practices worldwide. Various countries have embraced AI to enhance waste processing, reflecting their governance priorities and innovative approaches.
In Barcelona, Spain, for instance, smart bins with sensors monitor fill levels in real-time, enabling waste collection trucks to optimize routes and collect bins when needed. This system has led to cost savings and reduced CO2 emissions by minimizing unnecessary garbage truck trips. Seoul, the capital of South Korea, has implemented a “pay-as-you-throw” system supported by AI-generated data, where households are charged based on the weight of their waste. This initiative has significantly decreased waste generation and increased recycling rates. In India’s Chennai, the Airbin pilot project utilizes sensors and Internet of Things (IoT) technology to alert municipal authorities in real-time when bins are full. Ireland has over 3,000 solar-powered smart compactors that can hold five times more waste than traditional bins, reducing trips and maintaining cleaner streets. These examples highlight that AI complements human efforts rather than replacing them.
The integration of AI in waste management typically starts with basic sensors in bins to monitor fill levels, followed by predictive systems that forecast waste production patterns. The most advanced stage involves sorting, where robots and machines identify materials, transforming waste into valuable resources. This gradual progression is crucial, as few countries immediately adopt robotic solutions. Most begin by leveraging data to understand waste production quantities and patterns, enabling better planning for waste collection routes and enhancing recycling and resource recovery processes.
While AI-based waste management systems are versatile, customization is key. In regions with unreliable electricity or internet access, solar panels can power bins, and SMS alerts can function in areas with weak internet connectivity. The success of replication also hinges on the effectiveness of local government institutions (LGIs), which play a central role in waste management. In developing nations, LGIs often face budget constraints, staff shortages, and inadequate infrastructure. AI can help overcome these challenges by improving efficiency and reducing costs. Smart bins equipped with sensors can monitor waste levels in real-time, optimizing collection routes and saving resources. Prediction systems can anticipate waste surges, enabling LGIs to plan resource allocation efficiently. Additionally, AI-assisted sorting, coupled with manual labor, enhances recycling accuracy even in low-resource settings.
Globally, the principles of “reduce, reuse, recycle” (3R) are gaining traction. Extended Producer Responsibility (EPR) laws hold companies responsible for their packaging waste, while circular economy strategies aim to sustain resource usage. AI aids in enforcing these laws by providing insightful data on pollution sources. Furthermore, AI integration aligns with the sustainability objectives of the private sector. For instance, Microsoft in the Asia Pacific region collaborates with organizations like Sustainable Coastlines to employ AI in categorizing and tracking litter on beaches. In Hong Kong, Microsoft supports “Clearbot Neo,” an AI-enabled robotic boat that identifies and logs types of trash from waterways using cameras and AI systems. By partnering with companies, local governments in developing countries can scale up AI-driven waste management solutions efficiently while reducing costs.
Bangladesh possesses the necessary institutional framework to experiment with AI-based waste management, including the National 3R Strategy for Waste Management, the Solid Waste Management Rules of 2021, e-governance initiatives, and the forthcoming Extended Producer Responsibility (EPR) guideline. However, effective enforcement requires a concerted effort. Many LGIs lack essential equipment, trained personnel, and reliable power and internet connectivity. Cost remains a significant challenge, but under EPR, companies responsible for packaging waste can be mandated to fund smart collection initiatives. By utilizing data-driven routes, LGIs can save on fuel costs and allocate resources to technology investments. Public-private partnerships with recycling industries and development partners can help bridge any remaining gaps.
Linking existing data collection and processing laws with waste management regulations in Bangladesh will ensure responsible handling of waste data and empower LGIs with digital tools to enforce 3R and EPR obligations. To materialize these initiatives, pilot projects could be initiated in city markets overwhelmed with organic waste, rural areas using solar bins with SMS alerts, and producer-funded programs targeting plastic packaging waste to showcase the financing potential of EPR for AI tools.
By leveraging AI for waste management, Bangladesh can not only enhance waste processing but also streamline the implementation of its legal frameworks effectively. As the world progresses, aligning technology, legislation, and governance is imperative for staying abreast of evolving waste management practices.