Can AI Make Circular Economy Policies More Effective?

Circular economy policy is usually explained as a very straightforward concept: use fewer raw materials, get products to stay in use longer, make less waste, and recover value at the end of their useful lives. In theory, it seems easy enough. In reality, the problem is much harder.

Materials move through complex supply chains, and at every stage, things can be designed and manufactured, sold and used, repaired and discarded, collected and sorted, recycled and eventually wasted away to nothing. Waste data may often be partial, repair networks may be poorly developed, recycled materials may struggle to compete with cheap virgin materials and businesses may be unaware of where they lose value.

Governments may be implementing circular economy policies but not have the data they need to be effective.

This is where AI could be useful.

AI may be able to analyse material flows, help improve waste sorting, identify potential for repair or reuse, support better product design, help monitor resource risks and help governments know if their circular economy policies are effective.

However, AI itself will not make an economy circular. Circularity is not just a data issue, it is an issue for policy, design and governance.

AI can help make circular economy policies more effective when used to promote the right objectives: lower material demand, longer product lifetimes, safer recovery, better standards, and reducing waste at source.

Circular economy policy needs better information

A circular economy requires an understanding of where materials are, what is being done with them and how they are being lost.

Many economies currently do not yet have a good understanding of their material flows. Governments may know how much waste they have collected, but not how long products last, how much gets repaired, the state of markets for recycled products, levels of recovery through the informal sector, the material quality of resources or why valuable materials are lost prior to recovery.

By analysing large datasets from waste systems, supply chains, products, trade records, sensors, recycling plants and public reports, AI will be able to help governments find patterns.

Important questions include:

What materials are being wasted?
Which products have short lifetimes?
Where is repair economically viable?
Which sectors are dependent on imported resources?
Which wastes contain useful recoverable resources?

Better data and insights can support better resource use and waste policy when governments are trying to reduce imports of raw materials.

However, data collection should be about designing policy to ensure materials are kept in the economy for as long as possible, not simply about collecting more data.

AI can improve waste sorting and material recovery

One of the most obvious use cases for AI is in sorting waste.

AI cameras, sensors and robotics could be useful for identifying materials in sorting streams, distinguishing between different types of plastics, metals, paper, textiles, electronics, packaging and more.

This, in turn, can help improve the quality of recovered materials and reduce contamination.

Higher-quality sorting is important because secondary materials often struggle in cases of poor quality. If recycled materials are too blended, contaminated or inconsistent in quality, then manufacturers may have no choice but to continue sourcing virgin inputs instead.

So, better sorting has the potential to strengthen secondary material markets.

This can help, but it should not be our only focus for circularity. Recycling matters, but it is usually lower-value compared to reuse, repair and remanufacturing.

If we only use AI to improve the efficiency of waste-sorting, we would be helping to improve the end of a linear system without fundamentally shifting how the system functions.

A stronger circular economy could also apply AI to prevent useful products from becoming waste in the first place, not just to recover waste materials.

Better design begins before waste is created

Many of the circular economy challenges start at the design stage.

Designs might make products difficult to repair, hard to disassemble, reliant on proprietary spare parts, constructed from mixed materials, or engineered for premature replacement. Once design is established, waste-management systems can only do so much to remedy the consequences.

AI could support better design by enabling manufacturers to test product durability, materials, repairability, modularity and end-of-life recovery.

AI could help to identify where components fail, which material streams are difficult to recycle, and how design changes might enable reuse or recovery.

For example, AI tools could help compare alternative product designs on the basis of lifespan, repairability, material content, carbon footprint and recycling value, while identifying potentially safer alternatives to dangerous substances.

Circular economy policy should not start at the bin. It should start at the design and product rules stage.

Designs that make products last longer make repair and recovery easier. Designs that make products fail sooner mean that even good recycling systems will struggle.

Repair and reuse need more visibility

A circular economy needs more visibility.

Repair and reuse are often less visible processes compared to recycling. It is harder to account for repairs to a refrigerator, reused office chairs and refurbished computers, and repair networks, both informal and formal, can be difficult to assess accurately.

Reuse markets may not necessarily be clearly mapped. Public authorities may struggle to know which investments in repair and reuse support would make the biggest difference to people’s lives.

AI can help, however, to map these repair and reuse opportunities.

AI could be useful in analysing where products have failed before, what spare parts are available, the cost of repair, local repair networks and consumer behaviour.

Determining which products are worth repairing, locating areas lacking repair services, and designing policies to extend product life are all areas where this can play a crucial role.

This is crucial because the circular economy is not limited to materials. It is about maintaining value.

Maintaining a product’s operational capacity usually retains greater value than reducing it to raw materials. AI may help illuminate that value, yet policy must act on it.

Policies could include:

Repair rights laws;
Incentives for repair;
Public procurement criteria;
Spare part accessibility;
Demands for product information;
Local repair business assistance.

Public procurement can leverage AI in a more systematic manner

Governments procure substantial quantities of products and services, such as infrastructure, furniture, vehicles, clothes, electronics, equipment, buildings, packaging, and more. As a result, public procurement can influence markets.

Procurement officers can use AI to assess life cycle costs, durability, repairability, recycled content, recycling possibilities, and end-of-life disposal decisions. It can highlight cases in which circular procurement lowers lifetime cost or demand for resources.

This is crucial since the lowest up-front cost is not always the cheapest in the long term.

Products that are built to last, are easy to repair, and have reusable materials can offer better public value than cheaper products which rapidly turn into waste. AI tools can assist public purchasing agencies in evaluating such trade-offs with greater consistency.

However, the procurement process should not be turned into a solely automated evaluation procedure. Public procurers still require clear regulations, transparent selection criteria, and oversight.

In the end, circular procurement is a matter of governance; while AI can support analysis, it is up to public bodies to select the kind of market they wish to sustain.

Mapping resource vulnerability along global value chains

Circular economy policy is increasingly linked to resource security.

A significant amount of metal, minerals, materials, timber, biomass, and components that industrial and agricultural systems use is imported. Geopolitical tension, export bans, climate disruptions, price fluctuations, or environmental regulations can interfere with these supply chains.

AI can support businesses and public administration in mapping resource dependencies more precisely. AI can assess trade routes, provider network structure, raw material composition, geopolitical instability, recycling opportunities, and opportunities for recycling or replacement.

This could assist in determining where circular approaches can be employed to lower vulnerability to market shocks.

For instance, if a country is highly dependent on imported critical raw materials, AI could support estimating what portion of those materials is already incorporated in electrical equipment, mobile phones, cars, and industrial assets and aid preparation for recovery, reuse, recycling, and remanufacturing.

This not only contributes to economic security but also relieves strains on environmentally sensitive ecosystems which are impacted by resource extraction, land-use change, and contamination.

Strengthening material security and environmental protection can go hand-in-hand with making the circular economy work.

AI can support circular industrial policy

A circular economy needs industrial capacity.

This means countries need infrastructure and workforce for repair, refurbishment, remanufacturing, recycling, material processing, safe recovery, standards, investment, logistics, data systems, and secure markets for secondary materials.

AI can support all of that by helping to optimise logistics, predict material availability, improve quality control and link secondary material sources with demand.

A manufacturer might want to buy recycled metal on reliable terms. A construction company might need reused components that comply with safety standards. A textile recycler might want to know more about fibre content. AI can help to make those connections.

But industrial circularity also must be safe.

Activities like waste handling, recycling and material recovery carry risks if chemicals, emissions or hazardous residues are not properly managed. The circular economy should not shift pollution from one part of a supply chain to another.

Strong oversight of industrial pollution and chemical risks remains essential as circular industries develop.

Better policy evaluation

Circular economy strategies often include goals relating to recycling, waste reduction, resource productivity, product durability, product reuse, and the use of secondary materials.

Those goals need evaluation.

Governments need to ask:

Are policies reducing the demand for primary resources?
Are products designed to last longer?
Are markets for repair expanding?
Is improving the recycling rate a result of better systems or simply increased amounts of waste?
Are circular policies reducing environmental harms, or just shifting them somewhere else?

AI could help governments assess those questions through analyses of sectoral, regional and material-flow trends.

That would make policy more adaptive. If recycling targets do not improve resource quality, they may need revision. If fees to extended producer responsibility schemes do not stimulate better product design, they may need adjustment.

If governments fund circular economy projects that fail to achieve scale up, they need to know why.

A broader lesson for environmental policy applies here: ambitious action needs monitoring, accountability and the capacity to reform policies as evidence changes.

AI can assist policy-makers to make circular economy policy more evidence-led, but it should not reduce complicated policy issues to slick dashboards.

Circularity should relieve pressure on nature

Circular economy policy is sometimes discussed as being just about waste and industry. It matters for nature too.

Resource extraction impacts forests, rivers, soils, biodiversity, and people on the ground. Mining causes water pollution. Timber extraction contributes to habitat loss. The need for biomass can drive up the price of farmland. Waste exports just move the problem elsewhere, effectively shifting the impact of waste generation to other countries.

A more circular economy may alleviate some of that pressure by reducing demand for primary extraction and extending the lifespan of materials in use, thereby placing circularity directly within the sphere of the economic case for biodiversity.

Ecosystems deliver public goods which could be compromised by unnecessary resource extraction, but circular policies have the potential to help address these risks.

But circularity must be planned carefully. Recycling plants, waste management facilities, secondary material processing and bio-based manufacturing can cause environmental harm themselves.

Circularity needs to be assessed by whether it reduces total impact on nature, not just by whether it keeps materials circulating.

AI’s own environmental footprint

AI-driven circular policy has its own footprint.

Data centres require energy and some also rely on water consumption. Hardware needs resources. Sensors, tracking systems and digital infrastructure need to be fixed and maintained.

If AI-driven circular economy policy relies on a perpetual hardware replacement cycle, then it could exacerbate material pressure, undermining what it is supposed to reduce.

But this does not mean we should not use AI.

A solution that supports better material recovery, decreases waste or improves the lifespan of products could easily warrant its impact. But a complex digital system which adds a new layer of infrastructure, only to avoid any resource savings, is unlikely to be sustainable.

So AI-driven circular economy policy needs to pass the same test as other green technology.

It should ask:

Is it necessary?
Is it efficient and transparent?
Is it proportionate?

Digital technology should contribute to lessening the impact of materials, not adding another layer of hidden consumption.

From smarter data to a waste-free economy

AI is a key to better circular policy. It can make material flows more transparent. It can help identify lost opportunities. It can be used to improve product design. It can support better sorting. It can help develop better reuse systems and inform better public procurement. It can be used to assess the impact of policy on the ground.

But circular policy cannot rely on data alone.

A circular economy needs good standards, the right to repair, producer responsibility, public procurement, industrial investment, safe recycling, functioning markets and public accountability.

AI can improve the evidence. It can help us make the right decisions. And it can help reveal the linear economy’s hidden waste. But it takes governments and businesses to act on those insights.

It is not about building a more intelligent waste management system which supports an ongoing throwaway economy. Rather, it is about a circular economy with less reliance on virgin resources, longer product use and respect for ecological limits.

AI can help support this, but only if circular policy drives the way forward.