Measuring Workforce Greenness via Job Adverts: Part 3, Skills
In our first and second instalments in a series of posts about green jobs, we explored how at Nesta we are measuring the greenness of the labour market using job adverts.
Our greenness measures cut across three components:
- Occupation
- Industry
- Skills
Our previous posts discussed how we used natural language processing techniques to measure green occupations and industries. In this third instalment, we’ll discuss our methodology for determining what proportion of skills in a job advert were green, how well it worked, and some high level analysis of green skills resulting from our work.
But first, we’ll discuss the datasets we used to work out what exactly a ‘green skill’ was.
Defining green skills
Unfortunately, there isn’t a single agreed definition of what constitutes a ‘green skill’.
The European Centre for the Development of Vocational Training (Cedefop) defines them as:
“the knowledge, abilities, values and attitudes needed to live in, develop and support a society which reduces the impact of human activity on the environment” [1].
Alternatively, other definitions don’t detail individual skills, but instead roll them up into the context of green occupations. The International Labour Organisation (ILO) doesn’t define “green skills” specifically, but defines “skills for green jobs” as:
“Skills … that are necessary to successfully perform tasks for green jobs … and to make any job greener. The term includes both core and technical skills, and covers all types of occupations that contribute to the process of greening products, services and processes, not only in environmental activities but also in other sectors” [2].
More recently, the European Classification of Occupations, Skills and Competences (ESCO) developed a skills taxonomy for the green transition, adding ‘green’ labels to their taxonomy of skills and knowledge concepts.
Their approach combined a manual cross referencing of ESCO concepts with Cedefop’s green skill definition and the application of a machine learning model to identify green skills.
This approach resulted in 571 concepts being labelled as ‘green’, for example “coordinate forestry research” was classified as a green skill.
Over in the US, there has also been considerable work defining green tasks and topics.
In 2011, O*NET revisited and extended their list of green tasks, expertly curating lists of green enhanced skills and green new and emerging occupations — identifying the key green tasks within them.
This resulted in a list of 1,371 green tasks across 138 occupations, for example “apply principles of specialized fields of science, such as agronomy, soil science, forestry, or agriculture, to achieve conservation objectives” is a task for ’soil and water conservationists’.
Building further on this, in 2022 O*NET expertly curated 72 green topics, which were much broader than the green tasks, for example “forestry”.
In our work on green skills, we didn’t want to (and weren’t best placed to) create a new definition for green skills or manually create a bespoke list of green skills.
We therefore opted to use ESCO’s green skills taxonomy and O*NET’s list of green topics, and focused on trying to understand which of these green skills were being mentioned at a job advert level.
We were aware there is a lot of nuance in the way green skills are worded in job adverts — so needed to rely on an automated and sophisticated linguistic approach.
Finding green skills
Our process of finding green skills in job adverts can be split into three steps:
Step 1: Skills are extracted from the job advert.
Step 2: Extracted skills are semantically mapped to various green terms.
Step 3: Skills are classified as green or not.
Step 1: Extracting skills
The first step in identifying proportions of green skills is finding out which skills are being asked for in a job advert.
Luckily for us, Nesta had already trained a Named Entity Recognition (NER) model to extract skills for job adverts, so we were able to use this! This model works well at identifying parts of the job advert which are likely to be describing skills, and is able to extract skills it hasn’t seen before. The table below gives some examples of job advert texts and the skills extracted from them.
You can read more about how we did this in our blog article about it, dig into some analysis of common skills in our interactive blog, or have a go at using it yourself using our code or our online tool.
The following table shows two examples of skills extracted from job adverts using our NER model:
Step 2: Semantically mapping skills to green terms
Once extracted, we then found semantic similarities between our extracted skills and various green terms. As mentioned earlier, we used ESCO’s green skills taxonomy and O*NET’s list of green topics for this.
To do this, we embedded all the green ESCO skills (using both the preferred and alternate skill names) and all the O*NET green topics using the “all-MiniLM-L6-v2” Sentence Transformers pretrained model. The cosine similarity scores were then calculated, allowing us to find the closest green ESCO skill, the closest O*NET green topic, and the number of O*NET green topics exactly found in the entity (by using exact word matching). The table below shows these scores for a sample of skills.
During this process we also broke up any particularly long skills into smaller components using a sliding window — this helped with the matching.
The following table shows semantic similarities between skills extracted from job adverts and the green ESCO taxonomy and the list of O*NET green topics:
Step 3: Classifying skills as green
We trained a random forest classifier to classify whether a skill is green or not using three features:
- The cosine similarity score between the embedded entity and the closest ESCO green skill embedding.
- The cosine similarity score between the embedded entity and the closest O*NET green topics embedding.
- The number of O*NET green topics that were found in the entity (using exact word matching).
Our training data included 971 skills manually labelled as not-green, and 743 labelled as green.
The final step in our process of finding which proportion of skills in a job advert were green was applying this classifier to each of our extracted skills. If we classified a skill as green then we used the closest ESCO green skill preferred name as a way to standardise the skill’s name — this was useful for our analysis.
The following table gives the final green skill results from the running example:
Therefore, the first job advert in our running example had 4 skills, 2 of which were green (50%). The second job advert had 3 skills, 3 of which were green (100%).
Evaluation
For the green skill classifier our test set results are as follows:
We also evaluated the full pipeline by manually labelling 514 skills from 500 job adverts. We labelled how well we thought the skill was extracted from the job advert, and the appropriateness of the green ESCO skill that was mapped to skills classified as green.
Our findings showed that:
- 92% of the time skills are well extracted from job adverts
- 54% of the time green skills are excellently mapped to green ESCO skills
- 38% of the time green skills are well mapped to green ESCO skills
- 8% of the time green skills are poorly mapped to green ESCO skills
Some examples of the different qualities of mapping to green ESCO skills are below:
Analysis of green skills
Below are some of our high level results after applying the skills extraction to over 4 million job adverts.
We can see that the most common green skill was “health and safety regulations” which featured in almost 160,000 job adverts, followed by “implement environmental protection measures” which was in around 20,000 job adverts.
Future steps
This blog discussed how we extracted green skills from job adverts. Applying this algorithm to millions of job adverts allows us to say something about the greenness of the labour market from a skills perspective. Teamed with the occupation and industry measures of green jobs discussed in the previous blogs, we will be using all three of these measures in our upcoming Green Jobs Explorer online tool. This will be released at the end of April — stay tuned for more on this!
References
- Cedefop (2012). Green skills and environmental awareness in vocational education and training. Luxembourg: Publications Office. https://www.cedefop.europa.eu/files/5524_en.pdf [accessed 11/03/24].
- International Labour Organization. 2019. Skills for a greener future: a global view. https://www.ilo.org/wcmsp5/groups/public/---ed_emp/documents/publication/wcms_732214.pdf [accessed 11/03/24].
- European Commission (2022) Green Skills and Knowledge Concepts: Labelling the ESCO classification. https://esco.ec.europa.eu/en/about-esco/publications/publication/green-skills-and-knowledge-concepts-labelling-esco [accessed 11/03/24].
- Dierdorff, E. C., Norton, J. J., Gregory, C. M., Rivkin, D., & Lewis, P. (2011). Greening of the world of work: Revisiting occupational consequences. National Center for O* NET Development. Available at: https://www.onetcenter.org/reports/Green2.html [accessed 11/03/24].
- National Center for O*NET Development (2022) Green Topics: Identifying Linkages to Occupations and Education Programs Using a Linguistic Approach. Available at: https://www.onetcenter.org/reports/Green_Topics.html
