We further adjust the green dictionary so that it reflects the South African context. We reviewed the South African literature and policy reports for occupations that are expected to reduce the human-induced negative impact on the environment and climate change. We found discussion of industries that will adapt due to the greening of the economy - energy generation, resource efficiency, emissions control, and natural resource management – and industries that are most vulnerable to job loss –mining, petrochemical, electricity, agriculture, and tourism sectors (PCC 2021; ILO 2018; McLean 2018; Montmasson-Clair 2012; Maia et al. 2011; Rutovitz 2010; Nhamo 2010). None of these studies, however, takes an occupational approach, i.e., looks at the actual activities or tasks individuals do in jobs that could affect the human-induced impact on the environment. The only list of green occupations that we found was compiled by the Department of Higher Education and Training (DHET) through their organizing framework for occupations (OFO).<sup>20</sup> We use the DHET list to adjust the Granata and Posadas (2024) green dictionary.

We follow a three-step process to adapt the green dictionary for the South African context and connect it to the PALMS database. First, we adjusted a few terms in the green dictionary based on the DHET list of green occupations: _heat pump_ , _biotechno_ , _chemistry_ , _geograph_ and _geophysics._ These were in the Granata and Posadas (2024) green dictionary but were not identified as strict green terms. However, they should be classified as strict in the South African context since the OFO list includes occupations with those root terms: geophysicist, biotechnologist, geophysical technician, and heat pump installer (DHET 2013). After this adjustment, we ended up with 321 strict green terms and 37 additional terms for the broad green list. Second, we use the South Africa-adjusted green dictionary and follow the method used by Vona (2019) to calculate the GTI scores for each occupation as per Equation 1. Third, to merge the GTI scores to PALMS, we used crosswalks to transform the ISCO-08 occupation codes to the ISCO-88 codes used in the PALMS.

Finally, we define an occupation – and the jobs within it – as green based on its calculated GTI. An occupation is defined as green if it has a GTI greater than zero. All the jobs corresponding to that occupation are assigned the same GTI and the same greenness status. We generate a list of strictly green occupations, which are those occupations with a GTI-strict value that is a positive number. The broadly green occupation list are those occupations with a GTI-broad value that is greater than zero.

We primarily use the strictly green occupations for our analysis, as the list of GTI-strict terms more concisely identifies jobs with tasks that are currently engaged in the green economy. But we present and discuss the results for both strictly and broadly green occupations, understanding that the results emerging from the analysis of strictly green jobs can be taken as the lower bound of green employment and broadly green jobs taken as the upper bound of green employment.

> 20 The OFO is DHET’s tool for identifying, reporting, and monitoring scarce and critical skills. This list cannot be easily mapped to household survey data because the structure of the occupation code differs from that of ISCO-88 (DHET 2013). For example, the OFO only has 8 one-digit ISCO occupations because major code 7 and 8 have been combined, making it hard to disentangle crafts and related trades and machine operators.

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