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Artificial intelligence and the circular economy: AI as a tool to accelerate the transition

April 11, 2019
Sustainability

Over the past 200 years, humans have developed an impressive industrial economy that has provided unprecedented prosperity. The result of our collective intelligence, this economy has been built by years of gradual improvement and is powered by new technologies. However, this system is in need of change to sustain rapid growth in the global middle class 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 shows that a circular economy in Europe can create a net benefit of €1.8 trillion by 2030, while addressing mounting resource-related challenges, creating jobs, spurring innovation, and generating 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.1 It is clear that we need new approaches and solutions to put us on an accelerated transition to a better model. New technologies, including faster and more agile learning processes with iterative cycles of designing, prototyping, and gathering feedback, are needed for the complex task of redesigning key aspects of our economy.

Artificial intelligence (AI) can play an important role in enabling this systemic shift. AI is a subset of the technologies enabling the emergent “Fourth Industrial Revolution” era,2 and deals with models and systems which perform functions generally associated with human intelligence, such as reasoning and learning. AI can complement people’s skills and expand their capabilities. 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.3
This paper offers a first look into the cross-section of two emerging megatrends: how AI can accelerate the transition to a circular economy. It provides an initial examination of how AI can enhance and enable circular economy innovation across industries in three main ways:

  1. Design circular products, components, and materials. AI can enhance and accelerate the development of new products, components, and materials fit for a circular economy through iterative machine-learning-assisted design processes that allow for rapid prototyping and testing.
  2. Operate circular business models. AI can magnify the competitive strength of circular economy business models, such as product-as-a-service and leasing. By combining real-time and historical data from products and users, AI can help increase product circulation and asset utilization through pricing and demand prediction, predictive maintenance, and smart inventory management.
  3. Optimize circular infrastructure. AI can help build and improve the reverse logistics infrastructure required to “close the loop” on products and materials, by improving the processes to sort and disassemble products, remanufacture components, and recycle materials.

To illustrate the range of applications across sectors, this paper looks at two value chains: food and agriculture; and consumer electronics. These examples, one centered on biological materials and the other on technical materials, highlight the potential of AI to increase the circularity of a broad range of economic activity.

The potential value unlocked by AI in helping design out waste in a circular economy for food is up to $127 billion a year in 2030. This is realized through opportunities at the farming, processing, logistics, and consumption stages. Specific applications include: using image recognition to determine when fruit is ready to pick; matching food supply and demand more effectively; and enhancing the valorization of food by-products.

The equivalent AI opportunity in accelerating the transition towards a circular economy for consumer electronics is up to $90 billion a year in 2030. Applications here include: selecting and designing specialist materials; extending the lifetime of electronics through predictive maintenance; and automating and improving e-waste recycling infrastructure through the combination of image recognition and robotics.

The essential similarities between the opportunities in these two industries suggest that the opportunities for AI to unlock value in a circular economy are not industry specific. Combining the power of AI with a vision for a circular economy represents a significant, and as yet largely untapped, opportunity to harness one of the great technological developments of our time to support efforts to fundamentally reshape the economy into one that is regenerative, resilient, and fit for the long term.

Creating a broader awareness and understanding of how AI can be used to support a circular economy will be essential to encourage applications which span, and go beyond, the areas of circular design, operating circular business models, and optimizing circular infrastructure. Ultimately, AI could be applied to the complex task of redesigning whole networks and systems, such as rewiring supply chains and optimizing global reverse logistics infrastructure, in any sector.

Both collaboration between relevant stakeholders and a degree of oversight will be needed to support these systemic applications of AI, ensuring that data can be shared in an open and secure manner, and that AI is developed and deployed in ways that are inclusive and fair to all.

Built on insights from over 40 interviews with experts, the work is a collaboration between the Ellen MacArthur Foundation and Google, with research and analytical support provided by McKinsey & Company.

Download the report (PDF-2MB)

Source: McKinsey

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