AI as a tool to advance the circular economy
Written by Frida Holzhausen
With a growing world population and escalating consumption levels, production and consumption patterns need to shift towards sustainability. To combat climate change effectively, business models must separate resource use from economic gain. This could be the circular business model, which revolves around reusing and recycling resources within an infinite system. Through this approach, known as decoupling, the link between resource consumption and environmental degradation can be broken. Transitioning to a circular economy is essential to meet climate targets and pave the way for a more sustainable future.
As AI continues to advance, the potential to support a circular economy becomes stronger. AI as part of circular practices is still largely unexplored, but holds significant promise: AI can facilitate a fundamental restructuring of the economy into one that is resilient and future proof.
AI as a tool for innovation in a circular economy
AI can be used to accelerate the transition towards circularity through optimization, automatization and development. The potential applications are many, including:
Supply chain optimization: AI-powered supply chain management systems can optimize material flows, reduce waste, and identify opportunities for resource recovery and reuse, enabling companies to minimize their environmental footprint and maximize resource efficiency.
Product design and lifecycle management: AI algorithms can analyze product lifecycle data and user feedback to inform sustainable design decisions, such as designing products for durability, reparability, and recyclability. AI can also facilitate product tracking and traceability throughout their lifecycle, enabling companies to better manage end-of-life products and materials.
Waste sorting and recycling: AI-driven robotics and computer vision systems can automate waste sorting processes in recycling facilities, improving sorting accuracy and efficiency while reducing contamination levels in recycled materials. AI can also optimize recycling processes and identify new recycling opportunities for challenging materials.
Circular business models: AI technologies can enable the development of innovative circular business models, such as product-as-a-service and sharing economy platforms, which promote resource sharing, product reuse, and extended product lifespans.
Resource recovery and upcycling: AI-driven processes can identify valuable materials in waste streams and facilitate their recovery and upcycling into new products or materials, reducing the need for primary resources and minimizing waste.
The RESOLVE framework
The major business opportunities following a shift to a circular economy are often described using the RESOLVE framework, which includes six different categories: Regenerate, Share, Optimize, Loop, and Exchange (Ellen MacArthur foundation, 2015).
Based on the explored possibilities above, AI holds the potential to contribute to all business models, but it may be particularly promising in the areas of Optimize, Virtualize, and Exchange. These categories are often identified as having high profit potential and offer new opportunities. Combining them with AI technologies provides significant business opportunities with the potential for substantial profit.
In the optimize category, AI can enhance product design and manufacturing processes to improve performance and efficiency while reducing resource consumption. Predictive analytics and machine learning algorithms can optimize supply chain operations by forecasting demand, minimizing waste, and optimizing inventory levels.
For the virtualize category, AI-driven virtualization technologies enable digital simulations and prototyping, thereby reducing the reliance on physical materials and resources in product development. Additionally, virtual assistants and chatbots streamline communication and collaboration, reducing the need for physical meetings and travel.
For exchange, AI can optimize material substitution by analyzing the properties of alternative materials and simulating their performance in various applications.
Examples of AI applications that are already promoting circularity
At Algorithma, we've developed a digital twin for ships in order to apply AI to optimize the speed distribution and route. This advanced model calculates the most efficient speed and pathway for each vessel by analyzing resistance from wind, water, weather etc., minimizing emissions and environmental impact. By reducing fuel consumption and emissions, this solution can contribute to the circular transition by providing a way to minimize the use of fossil fuels in the global transportation and deep sea shipping sector.
Greyparrot is another example of how AI is actively driving circularity in the digital technology sector already. By aiding in recycling businesses, Greyparrot contributes to making businesses more circular. Its waste recognition software, deployed directly within facilities handling massive volumes of waste daily, revolutionizes waste management. Utilizing computer vision and artificial intelligence, the software seamlessly monitors and sorts waste at scale, providing real-time insights via a live dashboard.
"AI can transform marine systems, enhancing sustainability and resilience by optimizing resource use and minimizing waste. Our MGS digital twin solution is a testament to this"
– Jens Ekberg
However, an increasing challenge for companies using AI to transform their business models into circular ones is the substantial energy demand required for data storage and AI operation. This issue is highlighted in Microsoft's recent sustainability report, which reveals a significant increase in energy consumption, rising by nearly 29% from 2022 to 2023. Despite Microsoft's investment in nearly 20 GW of renewable electricity last year, the company saw a decline in the proportion of its energy sourced directly from renewables. This shift was attributed to the extended time required to develop renewable energy projects compared to the faster expansion of data centers. This underscores a critical challenge: the infrastructural and systemic readiness of energy grids to support the sustainable growth of AI technologies is lagging, potentially impeding progress toward a circular economy.
AI in the journey towards circularity
How to best start the implementation of AI in order to contribute to the circular transition varies for each company. It could be to establish a completely new circular business model enabled by AI or simply integrating AI to make a specific process within an existing business more circular. Regardless, implementing AI to foster sustainability and circularity requires a strategic, multi-faceted approach that integrates technology with core business operations.
Companies might begin by assessing their current practices to identify key areas where AI can enhance efficiency, reduce waste, and optimize resource use. Investing in employee training and collaborating with AI and sustainability experts can bridge knowledge gaps and drive innovation. Initiating pilot projects allows for the testing of AI solutions on a smaller scale, providing insights that can be scaled up across the organization. Combining AI with IoT for real-time monitoring, promoting open innovation, and maintaining a data-driven approach ensures continuous improvement and adaptability. Regularly evaluating and adjusting business models to align with circular principles, while engaging stakeholders and advocating for supportive policies, can position companies as leaders in the transition towards a sustainable and resilient economy.
AI presents a remarkable opportunity to accelerate and secure a sustainable way of life for current and future generations. By optimizing processes, promoting resource efficiency, and fostering innovation in circular business models, AI can drive transformative change towards a more sustainable future. Embracing AI-enabled solutions and implementing circular practices without delay will not only benefit the environment but also position companies for long-term success in a rapidly evolving landscape. At Algorithma, we fully embrace this challenge and accept the call to action to leverage AI in our pursuit for a more circular and sustainable world.
Sources and additional readings
Ellen MacArthur Foundation, Towards a circular economy: Business rationale for an accelerated transition (2015).
Greyparrot, https://www.greyparrot.ai/
Microsoft, https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RW1lhhu
