Data Science Applications in Circular Economy: Trends, Status, and Future

Area of Science:

• Environmental Science
• Computer Science
• Industrial Ecology

Background:

• The circular economy (CE) model seeks to decouple economic growth from finite resource consumption.
• Data science (DS) has emerged as a key enabler for advancing CE principles.
• A comprehensive review is needed to consolidate knowledge on DS applications in CE.

Purpose of the Study:

• To critically review and synthesize the role of data science in accelerating the transition towards a circular economy.
• To identify key areas where DS contributes to CE strategies.
• To outline future research directions and opportunities.

Main Methods:

• A critical literature review approach was employed.
• Focus on four key areas: socioeconomic metabolism, waste reduction, product lifecycle extension, and waste reuse/recycling.
• Analysis of DS methods applied within these CE domains.

Main Results:

• DS enhances CE by improving material efficiency and product design, reducing waste generation.
• DS facilitates the extension of product lifespans through repair and maintenance optimization.
• DS methods are crucial for optimizing waste reuse, recycling infrastructure, and understanding socioeconomic metabolism.

Conclusions:

• Data science significantly accelerates the circular economy transition across multiple strategic areas.
• Current limitations and challenges in DS application for CE exist.
• A clear roadmap for future research is proposed to further integrate DS into CE frameworks.