Using digital technologies to improve industry, innovation and infrastructure

United Nations Sustainable Development Goal 9 aims to “build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.”

The post-World War 2 era saw a significant increase in the volume of plastic being manufactured and used in product packaging. Even now that volume is increasing year on year. Over half a trillion plastic bottles were produced in 2021 (100 billion more than five years ago). Over five trillion plastic bags will be used in 2022 (Earth Day, 2022). Almost 75% of the more than 400 million tonnes of primary plastic produced end up as waste (Geyer, Jambeck & Law, 2017).

Waste plastics cause harm in two main ways. First, plastic is a hydrocarbon-based product made from oil extracted from the earth. This means not only that carbon is emitted in its production, but that plastics “generate heat-trapping gases at every stage of their life cycle (Bauman, 2019). That is, plastics produce greenhouse gas emissions in their production and during the time it takes for them to break down – which, in some cases, can take as long as 500 years (WWF Australia, 2021). Second, millions of tonnes of plastic waste end up in the world’s oceans every year. These ‘ocean plastics’ destroy habitats and kill sea creatures and have even ended up in the food humans eat. Vortices of ocean plastics in the Pacific Ocean have been collectively called the ’Great Pacific Garbage Patch’, the largest of which covers an area of 20 million square kilometres (7.7 million square miles) (National Geographic, 2022).

What is ‘artificial intelligence’?

Artificial intelligence (AI) is a field of study focused on work that can be done entirely by machines but which previously had needed human facilitation. Initially, this conceptualization focused on machines’ potential ability to use language and to solve problems (McCarthy et al., 1955). AI has subsequently been developed to solve problems and do human tasks, such as playing chess, better than humans can (Lowry, 2021). Using large datasets, algorithms are produced to both improve and accelerate tasks previously done by humans (Pournader et al., 2021).

Interest in AI has accelerated rapidly in the last decade as its potential for generating value has developed. In a waste management context, for example, AI can be trained to identify, much faster than humans, the different materials on a conveyor belt in front of the system’s ‘eyes’, and therefore to instruct the arms of the machine to sort those materials.

AI in waste management

Greyparrot is a London-headquartered provider of artificial intelligence (AI) services for responsible waste management. The company was founded in 2019 to digitize the $1.6tn waste management industry and equip waste managers, producers, and regulators with the insights they need to bolster recycling rates and introduce accountability to the waste value chain. Greyparrot now lists some of the world’s largest waste management companies as clients, helping them to manage their waste and save money through both efficiency and the recovery of valuable materials. In doing so, Greyparrot hopes to become a key player in the growing discourse on the circular economy.

Greyparrot’s system attempts to eliminate the risk of poor recycled materials quality, reduce inefficient sorting processes, and reduce over-reliance on manual sorting of waste through a system which monitors and analyses 100% of the waste that comes through sorting centres. The company’s AI Waste Analytics System identifies and characterises over 50 materials, including mixed material-objects, automatically in real time. The system is even able to identify the brand on packaging. The result is bales of recyclable material that meet quality standards to enable customers to be sure that the materials they buy can be recycled into new products and offers them a real-time dashboard to enable improvements in visibility of materials and therefore workflow.

Benefits of the technology

The use of artificial intelligence technology in this case offers three major benefits. First, Greyparrot’s AI waste monitoring scheme allows firms to analyze and deal with waste streams, saving its clients money by both being more efficient and by highlighting valuable waste which would otherwise go to landfill. Second, the accuracy of Greyparrot’s system means that waste management firms can become key players in the circular economy, guaranteeing a high quality of materials as outputs from the AI-managed waste management process which can be recycled into new products. Third, the system is replicable at any site that opts for Greyparrot as its technology provider.

Costs of adopting the technology

The cost of Greyparrot’s technology is hard to ascertain. AI pre-dates the company by over 50 years. This means that the technology is built on existing knowledge adapted to Greyparrot’s needs. The company has raised over £10m GBP in investment and offers data which enables customers to track trends in relevant materials values and invest in facilities which themselves have a return on investment. One plastics recycling facility recovered £1.6m of material which would otherwise have been lost to landfill.

Barriers, problems, and challenges

The main challenge to Greyparrot’s system is that the technology is not rapidly scalable at the same speed as other digital business solutions to SDG problems. Whereas other solutions rely on digital data being collected by software systems, Greyparrot’s AI solution creates digital data from physical systems. The result is an efficient and effective system, but one which relies on physical infrastructure, the creation and installation of which slows down its scaling at pace.