Track Description
In recent years, the Circular Economy (CE) has been increasingly recognized as a pivotal paradigm to decouple economic growth from resource depletion and environmental degradation. However, the transition from linear to circular models is still hindered by inertia in organizational routines, decision-making protocols, and institutional frameworks. From a performance management perspective, this transition requires aligning circular strategies with measurable performance outcomes. A core challenge lies in bridging the persistent disconnect between information, measurement, and action. Indeed, decision-makers often lack timely, reliable, and actionable data about product life cycles, material flows, and system dynamics. This gap undermines performance monitoring, target-setting, and continuous improvement, limiting the capacity to govern productivity in circular contexts.

This track aims to explore the critical role of metrics, measures, and key performance indicators (KPIs) in enabling informed circular decision-making and systematic performance improvement. Indeed, existing circularity indicators are often fragmented and poorly aligned with practical decision-making needs. At the same time, assessment tools tend to deliver static, one-dimensional evaluations that overlook trade-offs and interdependencies. Such limitations constrain organizations’ ability to design performance-oriented strategies, activate continuous improvement cycles, and link CE actions to productivity and value creation.

The track emphasizes how circular KPIs, data-driven assessments, and integrated evaluation frameworks can serve as strategic levers to align strategies with long-term sustainability goals and measurable performance targets.

In this perspective, circular decision-making is framed as a systemic, context-sensitive process that requires lifecycle thinking, ecosystem resilience, and stakeholder engagement.
The track welcomes theoretical, methodological, and empirical contributions that address, among others:

• Decision Support Systems for circular decision-making and performance management: conceptual models, analytical frameworks, and practical applications that inform circular choices across micro, meso, and macro scales;
• Metrics and indicators: development, comparison, and integration of tools for assessing and disclosing circularity, with a focus on decision-making needs with links to performance outcomes;
• Digitalization and Artificial Intelligence for performance-driven circular decisions: how Big Data and AI enable data-driven and adaptive circular decision-making and productivity gains;
• Sustainability implications of circular practices: assessment of the environmental, social, and economic impacts of circular practices, supported by life cycle–based methodologies to guide comprehensive and balanced circular decision-making.

Overall, the track aims to consolidate the scientific debate on how measurement, assessment, and data can act as enablers of circular decision-making and performance improvement within the new sustainability agenda.