Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The study expands the inventory of green job titles using AI-driven text mining, identifying 695 unique green job titles from 1,067 articles published after 2008, indicating a significant increase in research on green jobs globally [4][15][59] - The research utilizes a retrieval-augmented generation model to categorize jobs within various green economy sectors, aligning closely with established frameworks like the U.S. Department of Labor's ONET [4][14][48] - The findings highlight the effectiveness of advanced natural language processing models in identifying emerging green job roles, contributing to the discourse on the green economy transition [4][16][60] Summary by Sections Introduction - The urgency of a green transition is emphasized, necessitating analysis of its impacts on labor markets and the development of effective strategies for education and employment [8] - The report notes the limitations of existing green job classifications, particularly ONET, which is outdated and U.S.-centric [11][12] Methodology - The study employed natural language processing techniques to identify green job titles from a comprehensive literature review, focusing on peer-reviewed articles [17][19] - The retrieval-augmented generation model was used to enhance the identification process, allowing for a larger analysis set compared to traditional methods [14][20] Results - A total of 695 unique green job titles were identified, with a significant portion in engineering and technician-level roles, reflecting the diverse nature of the green economy [36][37] - The geographical spread of articles has expanded, indicating a growing global interest in green jobs, with notable contributions from various regions [33][36] Comparison with ONET - The study found that 17% of the identified job titles matched those in ONET, suggesting the presence of new roles not currently recognized in existing classifications [45][47] - The research proposes the creation of 25 distinct clusters of job titles interpreted as green economy sectors, some of which are not represented in O*NET [48][49]