The ECERA Digital Circular Economy White Paper (20 October 2020) investigates how the integration of digital technologies can scale up and sustain circular‑economy practices in European industry. It establishes a common vocabulary linking the maturity levels of digital transformation—data collection, integration and analytics—with circular‑economy activities: descriptive (“what is”), preventive (“what if”) and prescriptive (“what’s next”). The paper underscores that digitalisation is not only an enabler of circular strategies (e.g., reuse, remanufacturing, recycling) but must itself become circular, embedding dematerialisation, lifetime extension and recyclability into the digital infrastructure.

Key technological enablers are mapped at three application levels:

• Processes (e.g., robotisation, additive manufacturing, sensor technologies, machine learning) optimize material flows and production efficiency.

• Products (e.g., Internet of Things, blockchain, digital twins) allow tracking, tracing and service‑based models (products‑as‑a‑service).

• Platforms (e.g., apps, websites, industrial symbiosis marketplaces) connect stakeholders and drive dematerialisation.

This taxonomy illustrates how digitalisation underpins each circular strategy, from product design to end‑of‑life recovery.

Data is the lifeblood of the digital circular economy. The White Paper highlights the “Age of Data,” noting that 9.15 billion IoT devices were installed in 2018, projected to reach 41.6 billion by 2025, creating unprecedented opportunities for circular‑economy applications through real‑time sensing and big‑data analytics. The “Smart Circular Economy Framework” couples increasing levels of data integration and analytics to higher resource‑efficiency and circularity, guiding firms on how to leverage IoT, cloud, big data, AI and Distributed Ledger Technologies progressively.

A suite of case studies from European manufacturers illustrates digital‑driven circular innovations:

• A milking‑robot producer uses continuous performance monitoring via a web marketplace to guarantee refurbished equipment quality.

• An e‑bike manufacturer for people with disabilities links battery data to a central system to predict failures and avert “catastrophic unloading.”

• Capital‑goods suppliers (compressors, LED‑lighting) offer condition‑based and predictive maintenance through remote sensor data.

These examples demonstrate how sensor data and analytics create new “as‑a‑service” models, extending asset lifetimes and unlocking value.

Functional electronics—comprising nano‑electronics, flexible/printed electronics and intelligent smart systems—merge digital and physical realms to embed sensing, actuation and cognitive functions directly into materials and components. A demonstrator (“smart composite” wind‑turbine blade) integrates printed strain and temperature sensors plus de‑icing heaters. This enables real‑time health monitoring and preventive repairs, yielding a <u>15 % decrease in inspection activities</u> and associated maintenance costs, while improving reliability under harsh conditions.

To track materials and substances through complex lifecycles, the paper advocates:

• Digital twins that mirror products and record composition, usage, maintenance and upgrade history.

• Digital passports capturing static metadata (e.g., material provenance, ecological footprint, recyclability).

• Distributed Ledger Technologies and smart contracts for secure, forgery‑proof data storage and trustless transactions (e.g., ensuring recyclate quality before purchase).

These tools tackle information asymmetries and foster transparent, efficient circular markets.

Artificial Intelligence sits atop the maturity stack, converting vast, heterogeneous data into actionable insights. AI enables demand prediction, intelligent design, automated sorting and prescriptive maintenance. For instance, machine‑vision sorting systems have demonstrated <u>up to 93 % accuracy in classifying waste streams</u>, reducing contamination and complying with quality standards. By unveiling hidden patterns and automating decision‑making, AI accelerates circularity across the value chain.

However, the digital economy’s own sustainability must not be overlooked. ICT currently accounts for 5–9 % of global electricity demand, set to rise with data centers, cloud services and blockchain. Meanwhile, <u>over 50 million tonnes of e‑waste</u> are generated yearly, rife with critical raw materials that are expensive and complex to recover. The paper argues for a circular digital economy in which modular, standardised devices enable easy repair and recycling, and leasing or service models ensure asset return (“urban mining”).

Recommendations coalesce into six pillars:

1. Safe Circular Data Space: Leverage GAIA‑X or DLT for secure, privacy‑preserving data storage and sharing.

2. Common Standards: Develop and update protocols for data formats, interfaces and ecodesign, balancing agility and interoperability.

3. Information Uptake System: Scale digital twins/passports and smart contracts to automate lifecycle tracing and circular transactions.

4. Modular, Serviceable Design: Enshrine product‑as‑a‑service models through modular hardware and open interfaces.

5. Urban Mine Activation: Improve collection logistics via sensor‑enabled networks and incentivised leasing schemes.

6. Monitoring & Learning: Harness AI and real‑time data to track circularity metrics, inform policy and ensure regulatory compliance.