green tasks used therein.<sup>17</sup> This data set, while quite rich, faces three primary shortcomings in our context. First, it may not be applicable to countries outside the US, especially middleincome and developing nations since production methods and technology may differ across economies or the required task profiles to realize those occupations may differ. Second, the GEP data were collected before 2009 while green technologies have evolved rapidly in the past 14 years. Additionally, it only considers jobs in 12 industries. Third, researchers report significant measurement error (Vona et al. 2019; Granata and Posadas 2024) when transforming 8-digit O*NET SOC codes to 4-digit ISCO codes.

Other studies have developed a methodology using text analysis of occupational tasks data to identify green jobs. Janser (2018) applied text analysis to a German occupational tasks database (BERUFENET) to identify green jobs in Germany. Granata and Posadas (2024) applied this method to ISCO occupational descriptions to profile green jobs in Indonesia.

Given that currently there is no occupational tasks database for South Africa, and the limitations of the O*NET GEP, we follow Granata and Posadas (2024) who apply a text analysis methodology to the ISCO-08 (developed in 2008) to identify occupations that perform green tasks. Granata and Posadas (2024) create a dictionary of green terms that are used to build a green occupation database. The dictionary of green terms is basically a collection of terms (words, roots, or expressions) that are commonly found in environmental economics literature. To make their list as comprehensive as possible, they draw green terms from various databases.<sup>18</sup> They reviewed more than 70 references to fine-tune the list. The final green terms dictionary includes 347 green terms, of which 308 are considered strict green because they are directly linked to the green economy. Another 39 terms are added to generate the broad green list of terms. These terms are not explicitly linked to environmental sustainability and climate change, but they could become directly linked if green technology or practices are adopted. These are added to the database to capture tasks that could be green. For example, the statement “land use” might not be directly linked to the green economy, but if greener technologies and practices are adopted in land use then this statement would be linked to the green economy. Any green terms clusters (a grouping of similar terms) can include only strict green terms, only terms that could be green that are used for the broad green definition, both, or neither. That said, the strict green list of terms is a subset of the broad green list of terms. Table 1 shows a sample of terms in the dictionary.

17 To build the GEP, the O*NET team reviewed the literature and identified 12 green sectors. Work activities identified as green included those that reduce the use of fossil fuels, decrease pollution and greenhouse gas emissions, increase the efficiency of energy usage, have to do with recycling materials and developing and adapting renewable sources of energy (Dierdorff et al. 2009). To identify green tasks within occupations and develop a definition of green jobs, the United States Department of Labor conducted representative surveys of job incumbents and interviewed industry experts. This information has been integrated into the United States standard occupations classification (SOC) and can be matched with labor force surveys.

18 Including the O*NET GEP, Burning Glass Technologies (BG) green list, US Bureau of Labor Statistics (BLS), GTP survey, IAB Janser (2018) list, UN Environmental GS, and the European Skills, Competences, Qualifications and Occupations (ESCO) skills taxonomy.

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