The GRINS (Growing Resilient, Inclusive, and Sustainable) project aims to foster economic and financial sustainability by exploring the role of the Circular Economy (CE) in reshaping labor markets, skill requirements, and job opportunities. This deliverable, developed by the University of Turin in collaboration with the Politecnico di Milano, focuses on understanding the impact of the CE transition on employment dynamics, skill demand, and wage differentials across Italian regions. 

As economies shift from a linear model (take-make-dispose) to a circular framework (reduce, reuse, recycle), the demand for specific competencies and professions is evolving. New policies at both the European and national levels promote circular practices, yet little is known about how these shifts translate into labor market specialization, regional disparities, and wage variations. 

This study seeks to bridge that gap by developing indicators, models, and AI-based job classification techniques to analyze CE-related employment trends in Italy. This deliverable consists of 3 chapters. The first one focuses on Circular Economy and Labor Market Transformation. The transition towards a circular economy is influencing labor markets in different ways. Some regions in Italy have developed a specialization in CE sectors and occupations, while others lag behind, highlighting structural imbalances. 

The study identifies six regional profiles, ranging from highly digitalized green labor markets to underdeveloped areas with limited CE engagement. A major finding is that digitalization plays a key role in strengthening the CE workforce, enabling more efficient job transitions and sectoral growth. Using three specialization indicators—sectoral employment in CE industries, CE occupations, and digital adoption—the study reveals that many Italian regions are undergoing a green transformation, yet skills and job roles do not always align. 

Some regions are highly specialized in CE-related industries, but not in CE occupations, indicating a skills mismatch that could hinder economic growth. In the second chapter it is presented a methodology to map Skills for the Circular Economy. A key objective of this research is to identify the essential and complementary skills needed for the circular transition. 

Using a data-driven approach, the study analyzes 161 workplace skills across 573 industries to determine which competencies are most relevant to CE jobs. The findings suggest that technical skills such as waste management, repair, recycling, and digital proficiency are vital for CE-related professions. 

However, soft skills like problem-solving, teamwork, and adaptability also play a significant role, especially in industries undergoing rapid transformation. Moreover, skill complexity varies across sectors, with enabling CE industries requiring highly specialized knowledge, whereas core CE industries rely more on manual and operational expertise. 

Regional disparities emerge in skill specialization, with low-income regions demonstrating a stronger presence in technical-physical CE roles, while high-income areas focus on knowledge-intensive positions. This highlights the need for targeted training programs to bridge the gap and promote labor mobility. The third chapter outlines a AI-Driven Job Classification and Workforce Optimization. 

To enhance job classification and workforce planning, the project employs Artificial Intelligence (AI) and machine learning models. A Large Language Model (LLM) with Retrieval-Augmented Generation (RAG) is used to analyze job postings, classify them into six CE-related categories, and assess the alignment between job descriptions and skill requirements. 

Initially, a Minimum Viable Product (MVP) was developed for the Piedmont region, extracting job data from Glassdoor. This was later scaled up to cover all of Italy, improving classification accuracy and broadening the scope of the analysis. The transition from a binary classification model to a six-class categorization system allows for a more nuanced understanding of job roles within the CE framework. Additionally, a custom dashboard using Python Streamlit was developed to replace Oracle Analytics Cloud (OAC), reducing costs while ensuring accessibility. A transition from OCI Autonomous Data Warehouse (ADW) to MySQL further optimized data management and analysis. 

Key Policy Implications. The study underscores the importance of aligning education and workforce development with CE labor market needs. Policymakers, industry leaders, and educational institutions must collaborate to enhance vocational training, particularly in areas such as recycling, sustainable manufacturing, and digital technologies.

Investing in regional workforce planning and AI-driven job-matching systems can help mitigate skill mismatches and increase employment opportunities in CE sectors. Overall, this research provides a comprehensive framework for understanding the labor market impacts of the CE transition and offers actionable insights for fostering a sustainable, resilient, and inclusive workforce in Italy.