first examine how well the text analysis works against this gold standard for U.S. occupations. We do this using the narrow dictionary, which only contains strictly green terms. Second, as an alternative exercise, we test the reliability of the identification strategy by applying the same dictionary to a skill database and observing how the GTI correlates with green skills intensity.

# Reliability test #1: The proposed text analysis methodology applied to O*NET task taxonomy versus O*NET GEP

To carry out this test, we apply our dictionary and text analysis techniques to the O*NET task statements database (version 23.3, which uses O*NET SOC 2010 and contains green occupations and task statements). In total there are 19,636 unique task statements for 974 occupations. Our green narrow dictionary successfully captures 77 percent of O*NET GEP green tasks. At the same time, our green dictionary identifies 293 green tasks that the O*NET GEP missed. The differences may be due to the GEP focus being circumscribed to certain sectors and qualitative work. Table 3.4 shows the detailed comparison of the results of applying both methodologies. A thorough analysis of the task statements confirms the validity of our methodology. Tasks that are green to O*NET but are not green according to the text analysis are: _’coordinate with other marketing team members and workers such as graphic artists to develop and implement marketing programs’_ , or _’prepare, maintain, or revise quality assurance documentation or procedures_ ,’ or to ‘ _monitor the flow of energy in response to changes in consumer demand_ ‘, or ‘ _fill out defective equipment reports_ .‘ Our dictionary also detects 300 green tasks within occupations that are non-green to O*NET. Examples are the tasks _’determine or recommend radioactive decontamination procedures, according to the size and nature of equipment and the degree of contamination_ ,’ or _’conduct audits at hazardous waste sites or industrial sites or participate in hazardous waste site investigations_ .’ However, a few tasks that are green to O*NET GEP are misclassified and should even be considered brown. For example, Power plant operators, a green enhanced occupation, has as tasks to ‘ _operate, control, or monitor gasifiers or related equipment, such as coolers, water quenches, water gas shifts reactors, or sulfur recovery units, to produce syngas or electricity from coal_ .’ The quality check performed found very few tasks that may have been classified as green, such as ‘ _conduct well field site assessment_ ’ or ‘ _deposit recoverable materials into chutes or place materials on conveyor belts_ ’. Potentially, these could be fixed by adding terms to the dictionary.

**Table 3.4 O*NET GEP tasks vs Text analysis methodology**

||**Text analysi**<br>**narrowgree**<br>|**s based on**<br>**n dictionary**<br>||
|---|---|---|---|
|**O*NET GEP task classification**|**Not green tasks**|**Green tasks**|**Total tasks**|
|**Non-green task**|17,957|293|18,250|
|**Existing green tasks**|113|169|282|
|**New green tasks**|204|900|1,104|
|**Total tasks**|18,274|1,362|19,636|

_Source_ : authors As expected, misclassifying green task statements affects the extensive and intensive margins of the GTI. At the occupational level (extensive margin), Table 3.5 shows that our methodology categorizes 862 occupations (or 89 percent) in the same way as O*NET GEP. Specifically, 675 are not on O*NET GEP's list 23