Artificial Intelligence to aid a Circular Economy?

The latest landmark report from the Intergovernmental Panel on Climate Change (IPCC), has forecast that our planet may warm above the set 1.5°C climate threshold by as early as next decade, much earlier than previously estimated. While its findings paint a bleak picture for our planet’s future, it comes as little surprise to environmental scientists and climate activists.

It leaves many of us feeling despondent and full of despair for the future of our planet. The problem for most people is finding what action to take. Recycling or riding to work can feel futile when at the same time countries announce new coalmines and where national government and big finance continue to subsidise oil exploration. Individuals are powerless to alter the prevalence of fossil fuel use in economies, where political lobbying of democratic institutions takes place, facilitating support for the oil companies and their shareholders. Meanwhile, governments have been too slow and even reluctant to implement progressive green initiatives, such as; decommissioning and infrastructure projects.

People are left with grass root movements and fickle market options to encourage actual sustainable methods of consumerism. Although the Green Revolution has been slowly cranking into action, too much attention has been aimed at offsetting the impact of our actions rather than tackling the root cause. The transition to sustainable energy solutions are already available today, where Net Zero targets could be reached even within a decade.

It is simply a question of shifting the plutocratic political landscape. As a contemporary example, demonstrable Keynesian economics and MMT support from most governments, including the IMF have alleviated much of the socio-economic impacts of the recent Covid-19 pandemic. Investing in a green and circular economy now, will lessen the cost later on.

Finite resources for a growing population?

Often there are legitimate concerns of how it will be possible to support and sustain a growing global population, set to peak around 9.7 billion circa 2064. However, this would actually leave the globe within the boundaries of most academic carrying capacity projections. The climate crisis is therefore not a question of population size concern but one of consumption. The two are both inextricably linked, however it is important not to conflate anthropogenic vectors.

Over the past two centuries, humans have developed industrial economies that have provided extraordinary monetary prosperity - the result of collective intelligence and mixed-economics, all powered by institutional research and advancements in technology. However, the system is in need of change to sustain rapid growth, without being overwhelmed by negative environmental and social impacts.

A circular economy, in which growth is gradually decoupled from the consumption of finite resources, offers a response. Its principles are to design out waste and pollution, keep products and materials in use, and regenerate natural systems. The advantages of such an approach are substantial. For example, research has shown that a circular economy in Europe can create a net benefit of EUR 1.8 trillion by 2030, while addressing mounting resource-related challenges, creating jobs, spurring innovation and generating substantial environmental benefits.

The challenges and negative impacts of the current economic model are massive, cumulative, and set to grow in line with the global economy, which could almost double over the next 20 years. It is clear that we need new approaches and solutions to put us on an accelerated transition to a sustainable model. New technologies, including faster and more agile learning processes, with iterative cycles of designing, prototyping and gathering feedback, are required to aid the complex task of redesigning key aspects of our consumption models.

Seeking efficiencies by analysing data

Machine Learning (ML) and Artificial intelligence (AI) can play an important role in enabling this systemic shift. It allows humans to learn faster from feedback, deal more effectively with complexity, and make better sense of abundant data.

A growing number of initiatives are exploring how AI can create new opportunities to address some of the world’s most important challenges. Across all industries, the scope of AI application to curb waste is endless, and the principle is relatively similar for all fields. To grasp the extent of these AI applications, here are just some of the many examples:

New material design

Funded by the European Space Agency, the project ‘Accelerated Metallurgy’ conducted research on the rapid and systematic development, production, and testing of novel alloy combinations. The project aimed to develop new metals with the same performance in a more efficient way. Alloys designed with circular economy principles in mind: are non-toxic; are designed to be used and reused; have longer use periods; and could be made using additive manufacturing and processing methods that minimise waste. Additionally, improved material properties can implicitly reduce resource use through enhanced product performance.

Accelerated Metallurgy uses AI algorithms to systematically analyse huge amounts of data on existing materials and their properties to design and test new alloy formulations. By capturing details of the chemical, physical, and mechanical properties of these unexplored alloys, the algorithms can map key trends in structure, process, and properties to improve alloy design using rapid feedback loops.

Waste management

Recycling is crucial for a circular economy, however there are varying levels that take place. Approximately 75% of waste produced by consumers is recyclable, but only 30% gets recycled (with much of it actually sent to poor nations, only to be then burnt).

Nine out of ten people claim they would recycle if the process was easier, where part of the issue is that consumers aren’t always sure what can and can’t be recycled, meaning items are disposed of incorrectly. Not only does this take time to separate at a recycling facility, but it can also contaminate other items in the same system.

ZenRobotics based in Finland was the first company to apply AI and robotics in a demanding waste processing environment. The company combines AI and robotics to recover recyclables from waste. Their technology solution allows greater flexibility in waste sorting, enabling operators to react quickly to changes in a waste stream and increasing the rate of recovery and purity of secondary materials.

A startup named SamurAI has innovated a similar solution also, whilst a similar waste disposal solution has been developed by a Polish tech startup called Bin-e. The team there have created a robotic system designed to recognise and sort waste according to its type.

Smart farming

Two mutually opposing trends are currently putting more pressure on agriculture, calling for immediate action. Already severely depleted soils needed to provide food for an ever-growing global population, and yet at the same time, roughly a third of food remains never eaten. AI offers multiple opportunities to make farming smarter by using image recognition to determine crop health, fruit ripeness, yield, more effective weed control and increasing circular food-to-product valorisation.

Water management

Our planet will experience a 40% global water deficit by 2030 under projected climate scenarios. AI can contribute to more effective water treatment processes and better water infrastructure management by detecting potential defects ahead of time.

With AI, new insights can be created, enabling better water reuse and its flow rates. AI can also play a crucial role in measuring water supply and sanitation, ensuring water quality and proper treatment standards. For water utility companies, AI can create an overall monitoring system leading to optimised water supply for planning and predicting future investment requirements.

AI as an enabler and accelerator

AI can be an enabler and accelerator of the transition to a circular economy, where AI technologies can be applied to three key aspects of a circular economy: design circular products, components and materials, operate circular business models and optimise infrastructure, to ensure circular flows of products and materials.

Creating greater awareness and understanding of how AI can support a circular economy is essential to encourage applications in design, business models, and infrastructure.

At BSI, we supply AI technology solutions and offer options for hosting systems in sustainable 100% renewable powered data centres.