Achieving net-zero requires a shift in the way we make and use materials: a circular economy. National circular economy action plans are cropping up around the world: of all the countries party to the Paris Agreement, 72 have mentioned the term 'circular' in their climate pledges. While pledges and plans are an important first step, taking action will require an understanding of how transitions are taking place elsewhere—and what works. The circular economy is key to helping us reach our end goal of an ecologically safe and socially just space—but for this reason, it's crucial to track exactly which 'ends' circular activities are achieving. Are they helping us reach environmental goals? What are their economic and social impacts? Data is critical to answering these questions. Once we start getting answers, we can accelerate action. Data can power circular transitions by helping design tangible, practical steps forward and disseminating insights through digital tools, that in turn can rapidly scale solutions.
Our evidence base for the impact of circular strategies is growing, albeit haphazardly. Around the world, researchers and governments are puzzling together data to form a clearer, more comprehensive picture of the circular economy. But while we are inching forward, much work remains to be done.
No two nations can follow identical transition paths, but data needs remain the same: all nations require insight on their current baseline, a sense of which interventions may be most relevant to their context, and which outcomes may stem from their implementation. If a country is interested in tracking its emissions or measuring the ups and downs of its labour market, data for these two indicators is required as a precursor for establishing a reasonable baseline. Circular economy interventions are about redesigning systems of production, consumption, trade and logistics. To assess interventions and see what impact they could have on indicators ranging from material use to emissions to jobs—we need a wealth of data to run our models.
Data gaps, unfortunately, are likely, particularly at the subnational level. Currently, no nations have complete information to guide their circular economy transition, though nations with existing infrastructure for tracking and measurements are in a better position to scope and start accounting for circular economy and related impact indicators. Eurostat, for example—the statistical office of Europe—contains a whole repository of circular economy indicators, as well as various indicators linked to the Sustainable Development Goals. Though these indicators are useful for high-level comparisons of circularity across countries, they are not detailed enough to determine complex policies at the national or sub-national level—or to track related impacts.
How is this done? To operationalise and track circular economy strategies, policymakers and decision makers usually start by compiling estimates for well-defined case studies, which are typically contained to a region or an industry. For example, to design and implement an effective sorting and recycling system on a regional or local scale, it's first critical to gain insight into the volumes and composition of products flowing through the system.
Unfortunately, data we use every day—such as that for employment, emissions and material consumption—can be spotty. At Circle Economy, rooted in the Circular Jobs Initiative, we’re trying to get an understanding of how many people are currently working on circular economy activities and the quality of their work—and how this could change as we implement circular strategies across sectors. While some circular jobs are well defined, and classified as such—such as repair, or those working in waste management—others are less direct, such as developing digital platforms for material exchange, or operational innovations and procurement choices within construction or agriculture. Currently, these other activities aren't captured by classification systems—and as a result, policymakers lack a comprehensive baseline from which to start their analysis of the effect of circular interventions on the labour market.
The same difficulties crop up for emissions: have you ever split the bill and found the total to be short, sometimes a lot shorter? The same scallywagging is happening with national emissions reporting. According to this Washington Post article, independently measured global emissions come in at 57 billion tonnes, whilst totalling all nationally reported figures is 44 billion tonnes. That leaves approximately 13 billion tonnes unaccounted for—and in this case, no country will take credit for what's left willingly. Accounting for emissions that occur within borders—using, for example, the EDGAR model—can invite bias and unreliable figures.
Tracking material consumption can be even more challenging: within businesses, manufacturers have some information about the composition of their materials, components and products that are used in their processes, but may not be willing to divulge these trade secrets publicly.
A bad habit that many fall prey to: kicking off an analysis by gathering as much information as possible, on as many topics as you can think of—and then mining that data to find answers to a hazily defined question. It's like doing a nine-piece puzzle with 1000 random pieces and no reference picture. Collecting and tracking even a single data point opens its own can of worms—and this effect is heightened exponentially when considering data collection on a worldwide scale, especially considering our highly globalised, dynamic economy. Plus, overloading institutions, public and private, with an increasing list of data requirements also increases the complexity of managing and maintaining this data on an ongoing basis, as well as putting it to good use.
New data capture, through surveys and once-off projects, is interesting for research, but would need to be systematically incorporated into an existing (ongoing) data collection infrastructure. Countries that have both a circular agenda and a digital agenda could bring the two together, recognising that digital solutions can be built in support of circular goals, and that data arising out of artificial intelligence can be a facilitator and accelerator of the transition to a circular economy. If structured effectively, it can help unlock circular economy opportunities by improving design, operating business models and products, and optimising infrastructure at various scales.
Countries with less established public sector infrastructure may need to rely more on independent models managed by research institutes and on data provided by the private sector. In both cases, but particularly the latter, the government’s main role is to set the right incentives for data sharing from the beginning, putting citizen privacy and wellbeing first, and mitigating the exposure of corporate trade secrets by forming and managing data trusts, alliances or collaboratives along the value chain to aggregate private sector data for public sector decision making. Industry leaders could also band together to develop a shared language around, and accelerate the implementation of circular practices, particularly by leveraging digital technologies and data.
If we can consider and select our questions carefully, and consider in advance which insights we can draw from having these questions answered, and which consequent actions we can take, this could be an overall more efficient method to jump-start circular transitions. And with such an outline, it is much easier to utilise and combine existing datasets, for instance from alternative data—such as earth or website data, or private sector data and therefore minimise costs and efforts for new data capture.
To address this challenge, we create Data Alliances. Data Alliances are coalitions of organisations that contribute research, data and/or expertise to uncover how the circular economy can be implemented and make an impact. Thanks to their collective action, all members benefit from shared insights and new connections—and that's how we can continue to drive circular action at scale.