Blockchain and AI Synergy: Transforming Circular Economy

Blockchain and AI Synergy: Transforming Circular Economy José Rodrigues 1and João Zambujal-Oliveira 2 1University of Madeira (FCS) 2NOVA LINCS (FCT) | University of Madeira (FCS) | [email protected] Focus and Approach .The synergy between Blockchain (BC) and Artificial Intelligence (AI) offers transformative potential for the Circular Economy (CE). .Which facilitators and barriers most influence the adoption of BC and AI in CE practices? .Application of MCDM tools — AHP,ISM, and DEMATEL. .Prioritize causal facilitators (e.g., traceability and smart contracts) and mitigate major barriers (e.g. scalability and regulatory uncertainty). Introduction .The Circular Economy offers an alternative to the traditional linear model, aiming to optimize resource use and minimize waste [1]. .Successful implementation depends on understanding the mechanisms that facilitate the transition to Circular Business Models (CBM) [2]. .In the context of Industry 4.0, technologies such as Blockchain and Artificial Intelligence act as key enablers of circular strategies [3], [4]. Figure 1:Diagram showing the integration of BC with CE practices, including adoption barriers and BC-IoT integration. Numbers indicate reviewed articles per category [5]. Research Purpose .Research Question: Which facilitators and barriers most influence the adoption of Blockchain and Artificial Intelligence technologies in the Circular Economy? Objectives: .Identify and classify the facilitators and barriers to BC and AI adoption in CE. .Examine how multi-criteria decision-making (MCDM) methods support technology adoption in logistics and supply chains. Methodological Framework .Analyzing influencing factors requires methods capturing both importance and interdependence [6]. Figure 2:Methodology for analyzing barriers to CE implementation using DEMATEL: barrier identification, interrelation analysis, and cause-effect classification [7]. .AHP ranks qualitative and quantitative criteria [8]. .ISM maps hierarchical and causal links [9]. .DEMATEL integrates both, clarifying cause–effect relations [10]. Results The role of emerging technologies in the Circular Economy is increasingly significant, as organizations adopt digital tools to enable circular strategies. .Alignment with Research Question .Identifies facilitators and barriers for BC adoption. .Shows AI’s contribution to circular decision-making. .Demonstrates MCDM methods (DEMATEL) for structuring and prioritizing factors. .Facilitators for Blockchain .11 positive causal facilitators, including traceability, transparency, disintermediation, smart contracts, collaboration, decision-making, efficiency, low transaction costs, interoperability, incentivisation. .Highlights key drivers for BC adoption in CE. Figure 3:Blockchain-based supply chain network using peerto-peer architecture, proof-of-work, and smart technologies (RFID, IoT, AI, cloud) for transparent, traceable material flow from supplier to customer and back via circular reuse [11]. .Barriers for Blockchain .Causal barriers: scalability, market risks, regulatory and technological risks. .Effect barriers: high costs, poor economic behavior, privacy concerns, underground economy uses. .Emphasizes technical and regulatory challenges. .AI Capabilities in CE .Perceptive: monitors resource flows. .Predictive: forecasts demand and environmental impact. .Prescriptive: supports optimal decisions. .Enables data-driven circular operations. Conclusions .Transformative potential: BC and AI enhance transparency, financing, and resource efficiency in the Circular Economy. .Facilitators and barriers: Prioritize causal BC facilitators (traceability, smart contracts); Manage key barriers (scalability, regulatory risks, high costs) using DEMATEL. .AI capabilities: Supports perceptive, predictive, and prescriptive functions; Offers collaborative, self-learning, and collective intelligence. .Research gaps: Limited studies on AI enablers/barriers and BC–AI synergy highlight opportunities for future research. Managerial Insights .Choose methods wisely: Align MCDM approaches with problem type, decision-maker preferences, and resources. .Focus on key facilitators: Prioritize causal factors like traceability and smart contracts. .Address barriers: Mitigate scalability, regulatory, and cost challenges. .Explore synergies: Combine BC, AI, and big data for enhanced CE outcomes. .Use structured analysis: MCDM tools support prioritization and decision-making. References