INSIGHTS ARTICLE: Topolytics - The importance of waste in ESG reporting.

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The importance of waste in ESG reporting

Over the past five years, ESG factors have become a critical focus for many businesses. The associated reports, aimed at investors and stakeholders, describe how businesses are engaging with communities and suppliers, employment practices, including diversity and inclusion, environmental management, and associated economic planning. While the ‘environmental’ strand is dominated by carbon emission, energy, and NetZero considerations, there has been less attention paid to materials and waste.

For companies that produce waste through their operations, the management of this material has been normalized as a cost of doing business. They buy raw materials (some of which are wasted), convert them to products (creating operational by-products), distribute and sell the products (creating waste in transit) and products and packaging are then disposed of after use.

But this ‘linear’ economic model is now being challenged by government policy, increased supply chain pressures, and rising costs - shifting the focus to a ‘circular’ economy. However, in order to realise the economic benefits of transitioning to a new economic model, there remains a need to gain greater visibility and understanding of what happens to waste and by-products.

This challenge will only increase, as waste generation nearly doubled between 1970 and 2000 and continues to grow exponentially. The World Bank estimates that waste generated in urban areas will total 3.4 billion tonnes per annum by 2040.

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Currently, more than 60% of this material is sent to landfill and more than 61% of the world’s population do not have access to recycling infrastructure – creating significant economic loss, social problems and environmental danger.

These rising levels of waste are also linked to the rapid growth in consumption. The 2022 Circularity Gap report notes that over 90% of the materials extracted and used to make products and packaging, ultimately become waste. In essence, only 8.6% of these materials are fed back into the production system. This is very poor from a resource efficiency perspective, but even more damaging to the environment, given that 70% of global greenhouse gas (GHG) emissions are linked to the way material is handled and used.

Globally, publicly available waste management data is usually drawn from national regulators and agencies. They, in turn, require commercial waste companies and municipal authorities to disclose data on a regular basis. However, the quality of this data is by its nature varied because the $1.6 trillion industry that manages and processes this material ranges from multinational corporations to informal community enterprises.

A key global challenge is the ability to monitor the billions of material movements annually, from hazardous sludges, through construction waste, inert recyclables (such as plastics and card), metals, organics, food and ‘special’ waste materials from industrial processes.

This scale and complexity also creates a challenge for corporate ESG reporters, as the data they are generating and sharing on waste and recycling is generally aggregated and is not validated.

So, what is the solution?

As corporate waste producers and the waste industry adapt to a policy environment dominated by waste reduction and circularity, there is a need for new infrastructure and business models.

Waste producers need to exert more control over measurement and management as they seek to understand what happens to their waste and drive elimination and re-use of products and materials.

While technology is being applied to the measurement of certain waste materials, for example, bin sensors or robotic sorting, it is not universal and is not standardised. Most waste data is captured in standard databases and spreadsheets, therefore a data science approach that can work across multiple sources of input and accommodate poor information or gaps, is the only way to build a picture of all materials moving through the system. Such an approach can use machine learning to account for the variation in the data quality and, in turn provide a view into where material is moving and the associated impact, to a defined degree of confidence.

This offers waste producers and recyclers (that have their own complex supply chains) significant benefits in terms of cost savings, impact measurement and reporting. However, it can also assist with scenario planning to identify optimal commercial and environmental pathways for materials.

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