Data, emerging technologies, and the circular economy: how Accenture and AWS are unlocking environmental and business impact | Amazon Web Services

Data, Technology, and the Circular Economy: How Accenture and AWS are Unlocking Environmental and Business Impact

This post was contributed by Ilan Gleiser, Principal Specialist, Emerging Technologies at AWS, Joshua Curtis, Circular Intelligence Global Lead and Patrick Ford, Circular Intelligence North America Lead, Accenture Sustainability Services

It is well documented that the circular economy is an opportunity for positive impact on business and society. Accenture’s analysis presents an economic opportunity of $4.5tn value is at stake for the global economy to 2030 by departing from our current ‘take-make-waste’ economic system [1]. Ellen MacArthur Foundation outlines the importance of circularity as a solution to climate change, with 45% of the required carbon emission reductions to achieve a 1.5-degree world coming from how we make and consume products [2].

It’s clear that resource use and circularity are critical to the creation of a sustainable, healthy economy. But how do we realize this value? How does a business identify where and how circular strategies can create financial and environmental impact?

Achieving data-driven circularity

The importance of transitioning to circular business models is why the European Commission is, for the first time, making measurement and disclosure of resource use and circular economy impacts mandatory for companies. The newly-launched European Sustainability Reporting Standards (ESRS) include a requirement [3] for companies – where material – to report on circular economy metrics like:

1. the percentage of material used for products and packaging that are renewable, recycled or re-used
2. the volume of waste by stream that is recovered by destination
3. the financial effects of material risks and opportunities arising from resource use

But how do we calculate these metrics? How do we collect, aggregate, and analyze the data in a way that doesn’t require significant time and resources year on year? How do we not only do this to understand where we are, but also to determine where we need to go?

In this post, we will explore the challenges to achieving accurate and actionable data on circular economy performance and impact, and the solutions that lie in emerging technologies. Join us as we explore opportunities for kicking off and accelerating the data transformation needed to drive authentic, impact-driven progress on circularity.

The challenges to circular data transformation

To help ensure digital solutions are effective in managing circular economy performance, it’s crucial to design them to address specific challenges faced by businesses. Let’s begin by exploring these challenges.

First, selecting the right metrics themselves is not straightforward. We mentioned the European Commission’s regulations ESRS E5 on resource use and the Circular Economy. They provide headline metrics for business disclosure. The Circular Target-Setting Guidance from the Circular Economy Indicators Coalition (CEIC), a partnership between The Platform for Accelerating the Circular Economy (PACE) and Circle Economy (supported by Accenture) provides an overview of leading measurement methodologies and approaches for business implementation.

These are important starting points for business across industries, but they don’t account for the specific value chains or functional priorities of businesses in different sectors. For example, the metrics to measure circular economy performance (and therefore the data required) vary significantly for a fashion retailer compared to an oil and gas major. Ultimately, selecting the right metrics must be led by each business, drawing on the wealth of supporting materials, best practices and market standards.

Next comes the hard part: identifying, collecting and transforming the data. Comprehensive circular measurement relies on data from across the value chain, often not tracked in existing enterprise systems. The foundational data itself is simple enough – what materials are being used and where do they come from; what waste is being produced and where is it going – are tangible examples. The challenge is collecting that data across product lines, business units, and geographies and then transforming the data to be usable. For example, when calculating your percentage of materials that are recycled, renewable or re-used (as ESRS E5 requires), materials data must be segmented in ways not currently built into enterprise data capture. Without technology, this requires line-by-line segmentation based on data that is available e.g. through supplier declarations. The bottom line is that collecting data to measure circular performance is an arduous process, requiring time and costs, and is hindered by data gaps. To do this at the business level, in a way that enables action, is not only helped by digital technologies, it depends upon them.

Finally, companies must transform this data into actionable insights to guide decision-making. Circularity is not an end, but a means to optimize planetary and business impact. To accelerate this impact, resource use data like the above example regarding materials that are recycled, renewable or re-used, must be connected with other internal and external data sets like sales data and emissions intensity factors. Companies must understand how different material choices impact carbon emissions, as well as procurement costs and business profitability. The true story of corporate circularity is of trade-offs and investment requirements to capture long-term value. Without a comprehensive approach to circular economy measurement and data transformation, understanding those trade-offs properly and making impact-driven decisions is impossible. This again adds complexity to data collection and analysis, with the only solution for ongoing insight generation being an automated, centralized approach, like a circular and/or sustainability data lake, which combines data sets and applies analytics solutions for calculation and visualization.

The role of emerging technologies

Accenture and AWS are collaborating to bring the best of their combined data, technology and sustainability expertise to transform circular economy data management. AWS offers the broadest set of capabilities in artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), big data analytics, and high-performance computing (HPC) in the market. Accenture is the world’s leading integrator of AWS solutions and technologies – they’ve completed over 1,100 projects with us over 15 years of partnership.SDGs, Targets, and Indicators

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 12: Responsible Consumption and Production
• SDG 13: Climate Action
• SDG 15: Life on Land
• SDG 17: Partnerships for the Goals

The article discusses the importance of transitioning to a circular economy to achieve sustainable and responsible consumption and production (SDG 12). It also highlights the role of circularity in addressing climate change and reducing carbon emissions (SDG 13). Additionally, the article mentions the need to consider environmental impacts and resource use in order to protect life on land (SDG 15). Finally, the collaboration between Accenture and AWS demonstrates the importance of partnerships for achieving the Sustainable Development Goals (SDG 17).

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources.
• Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling, and reuse.
• Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
• Target 15.2: By 2020, promote the implementation of sustainable management of all types of forests.
• Target 17.16: Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology, and financial resources.

The article emphasizes the need for sustainable management and efficient use of resources (Target 12.2) and the reduction of waste generation through circular strategies (Target 12.5). It also highlights the importance of integrating climate change measures into policies and planning (Target 13.2) and promoting sustainable forest management (Target 15.2). Lastly, the collaboration between Accenture and AWS exemplifies the importance of multi-stakeholder partnerships for achieving sustainable development (Target 17.16).

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Percentage of material used for products and packaging that are renewable, recycled, or re-used.
• Volume of waste by stream that is recovered by destination.
• Financial effects of material risks and opportunities arising from resource use.

The article mentions these indicators as part of the European Sustainability Reporting Standards (ESRS) requirement for companies to report on circular economy metrics. These indicators can be used to measure progress towards achieving sustainable resource management, waste reduction, and circularity.

Table: SDGs, Targets, and Indicators

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Fuente: aws.amazon.com

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