Bridging the Gap: How AI is Identifying New Green Jobs for a Sustainable Future

A World Bank study, utilizing AI-driven text mining, identified 695 unique green job titles across 25 sectors globally, addressing the evolving green economy and gaps in traditional job taxonomies like O*NET. The research highlights the growing global scope of green jobs and the importance of continuously updating job classifications to reflect new roles in sustainability.

The World Bank, in collaboration with researchers from the University of Warsaw and TOBB University of Economics and Technology, produced a study that explores the growing significance of green jobs as the global economy transitions towards more sustainable practices in response to the mounting challenges posed by climate change. The study underscores the critical need for policymakers to understand the scope of green jobs to effectively shape labor markets, education, and employment strategies that support the green transition. Green jobs, defined as roles contributing to the preservation or restoration of the environment, are at the forefront of this shift. The research expands on the existing inventory of green job titles, addressing the limitations of previous classifications like the U.S. Department of Labor’s Occupational Information Network (O*NET) and employing cutting-edge natural language processing (NLP) technology to analyze contemporary academic literature on green employment.

AI-Powered Exploration of Green Job Titles

The researchers sought to update and globalize the list of green job titles by analyzing academic literature published after 2008 using Scopus and Web of Science databases. The retrieval-augmented generation (RAG) model, powered by GPT-4, was employed to process a vast amount of literature and identify potential green job titles. In total, 1,067 articles were reviewed, resulting in the identification of 695 unique job titles related to the green economy. These titles were then grouped into 25 distinct sectors, ranging from renewable energy and environmental management to green human resources. The study’s use of advanced AI models allowed for a level of analysis far beyond the capabilities of manual methods, and the RAG model's ability to cluster and categorize job titles helped to identify not only existing green roles but also potential new categories, highlighting the dynamic nature of the green job market. This categorization aligns with existing frameworks like ONET but expands them by identifying new roles and sectors that have emerged since ONET’s last major update in 2011. The research also found that while 17 percent of the job titles matched closely with O*NET’s classifications, many new titles did not align precisely, indicating the emergence of novel green roles that are not yet fully recognized.

Bridging the Gap Between Taxonomies and Emerging Green Jobs

A key issue identified by the study is the growing gap between existing green job taxonomies and the rapidly evolving green economy. ONET, developed primarily for the U.S. labor market, is increasingly insufficient for capturing the global scope of green jobs, especially in regions outside of the United States where production technologies and job tasks may vary significantly. For instance, low- and middle-income countries are witnessing different kinds of green job growth compared to high-income nations, and ONET’s U.S.-focused taxonomy does not fully address these variations. Furthermore, ONET’s classification of green jobs was last updated more than a decade ago, and the rapid expansion of green industries has rendered some of its categories outdated. This has prompted the need for a more comprehensive and regularly updated global inventory of green jobs. The researchers’ approach, leveraging AI, represents a solution to this challenge by providing a scalable and reproducible method to identify and classify green jobs across different economic contexts.

Green Job Research Expands Geographically

The study also sheds light on the broader geographic distribution of green job research. Initially, academic discussions of green jobs were concentrated in a few regions such as the United States, Europe, and China. However, by 2023, the literature had expanded to cover a more diverse range of countries, including those in Africa, Southeast Asia, and Latin America. This geographical expansion is indicative of the growing importance of green jobs in developing regions as they too transition toward more sustainable economies. The study’s findings show that the green economy is not limited to traditional high-tech sectors like renewable energy but also extends to areas such as human health, water management, and environmental policy. For example, the study identified green job roles in sectors like public administration, waste management, and electricity supply, which are increasingly central to discussions about sustainability in both developed and developing countries.

Identifying New Green Job Titles for a Greener Future

One of the significant contributions of this study is its identification of potential new green job titles that have not been previously recognized in established taxonomies like O*NET. These include roles such as “environmental compliance specialist,” “decentralized-energy engineer,” and “carbon auditor,” reflecting the increasing demand for specialized skills in emerging green sectors. The use of AI also allowed for the identification of roles that are not traditionally considered green but are critical to supporting the green economy. For instance, jobs in logistics, IT, and human resources are being transformed by sustainability initiatives, further broadening the scope of what constitutes a green job. The study suggests that many conventional jobs are becoming increasingly green as industries adapt to environmental regulations and climate change mitigation efforts. In this way, the line between green and non-green jobs is becoming increasingly blurred, emphasizing the need for continuous updates to job taxonomies to reflect this evolving landscape.

AI’s Role in Shaping Green Job Policy

The researchers argue that the use of AI in identifying and classifying green jobs offers a powerful tool for policymakers and businesses looking to better understand the labor market's role in the green transition. The study concludes by highlighting the need for further research to refine AI-driven methods for green job identification and to integrate these findings into labor market studies. As the green economy continues to evolve, AI-based tools like the RAG model will be essential for keeping job classification systems up-to-date and relevant in a rapidly changing world.