# **2. Approach**

# **Identification of Relevant Literature**

In April 2024, we conducted a search of the literature published since January 2009, using the official application programming interfaces of Scopus and Web of Science. Our search strategy involved keyword combinations previously validated in three systematic literature reviews related to green jobs (Apostel and Barslund 2024; Kozar and Sulich 2023; Stanef-Puică et al. 2022). The keyword combinations included “green job(s),” “green occupation(s),” “green employment,” “sustainable job(s),” “sustainable occupation(s),” “green transition job(s),” and “green-collar job(s).” (Box 1 lists the search queries.) These keywords were searched within titles, abstracts, author keywords, and “topics” as referenced in the databases. To ensure credible results, we restricted our focus to peer-reviewed publications, specifically articles and reviews (hereafter referred to as “articles”).

Our search approach differs significantly from that of O*NET _._ While O*NET meticulously indexed and categorized sources within its reference book, the specifics of its selection process are sparingly described. There is no detailed information on specific keywords or methods used in gathering the articles. It involved collecting and reviewing more than 60 publications, including academic journals, commissioned reports, industry white papers, and government technical reports. Additionally, O*NET conducted a substantial review of various internet sources related to the green sector’s workforce (O*NET 2013).

# **Identification of Green Job Titles in the Literature**

We used the RAG model to simulate the work traditionally performed by research assistants who would manually tag green job titles within articles. This manual tagging process, extending over 1,000 pages across the articles in the analysis set, is resource-intensive and susceptible to errors from human oversight, cognitive biases, and heuristic shortcuts. In contrast, the RAG model offers a robust and consistent approach.

A significant advantage of using the RAG model is the reproducibility of the results. By utilizing seed parameters available in the OpenAI models, specifically the Generative Pre-trained Transformer 4 (GPT-4)-0125-preview model, we ensured that our results were replicable, providing a degree of consistency that manual tagging struggles to achieve. Although the models cannot be entirely deterministic due to their inherent stochastic nature, the use of seed parameters helps to ensure that the results are highly consistent across multiple runs (Anadkat 2023). Furthermore, the advanced natural language understanding capabilities of the GPT-4 model enabled a nuanced analysis of the context in which job titles are discussed in the articles. This is particularly vital in our analysis set, where green and nongreen jobs are often mentioned in the same articles. The model’s ability to discern the context and classify job titles accordingly is a substantial improvement over older NLP approaches, such as less capable embedding models like BERT or fully supervised methods like named entity recognition (NER), which might not capture such subtleties or nuances.

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