It should be noted that even if skills data from job vacancy postings is not best suited to identify green jobs, it brings valuable information to understand and design skills policy. Skills analysis from big data allows profiling of green jobs, including their skills requirements, and understanding other key features such as skills demand variation across industries and regions.

# **3. Using text analysis to develop an alternative methodology for the task-content approach**

This section introduces the text analysis methodology that identifies green occupations based on a green dictionary developed by the authors. The methodology falls under the task-content approach, which is our preferred approach for skills policy. It applies the green dictionary to a task database that is used for data collection in many countries, called the international standard classification of occupations (ISCO-08). One advantage of the methodology is that the green dictionary can also be easily applied to any other task database or adjusted to other country settings. This section also discusses the extent to which the methodology can be trusted by conducting two reliability tests. The first test compares the outcome of applying the methodology to the O*NET task database against green occupations listed in the O*NET Green Economy Program. The second test assumes that green skills are necessary to perform green tasks and checks the correlation between a green task index and a green skills intensity measure, both constructed by applying the same methodology to different databases.

## **A. Applying text analysis to the International Standard Classification of Occupations (ISCO) to produce the green task intensity index**

Classifying occupations according to their level of “greenness” consists of three steps: (1) creating a green dictionary, (2) applying it through text analysis techniques to an occupational database containing task statements, and (3) calculating the GTI index.

### Step 1: Development of the green dictionary

The first step was the development of a green dictionary, a database of terms that were used as the basis for the text analysis. The terms in the dictionary are not tasks, but rather words, word roots, and expressions directly linked to environmentally friendly concepts. Our assumption was that a task statement containing any of these key terms would be related to environmentally friendly tasks. The selection of terms was the result of a careful desk review of diverse sources. Naturally, since terms are short and general, most of them are present in multiple sources. Thus, the creation of the dictionary should be understood as a systematic iterative process of adding and confirming the appropriateness of terms as they consistently came up in the consulted sources. As we continued reviewing sources, fewer new terms were included for consideration in the dictionary.

For the creation of the dictionary, the starting point was to examine the terms commonly mentioned in the diverse material from the methodological approaches described in section 2, including the O*NET green project description, and from the labor environmental economics literature. First, the dictionary included terms that appeared in the BLS GTP survey questionnaire, most common terms from machine16