Research Brief

Measuring the greenness of jobs in emerging economies: A big data text analysis approach

This brief presents a new big data and NLP-based approach to measure how green jobs are in emerging economies using a refined ILO green dictionary. It provides practical evidence on green skills, job quality and wages to inform labour market and skills policies for a just green transition.

Key points

This brief presents a novel methodology that combines big data, natural language processing (NLP), and a refined ILO green dictionary (472 terms across nine environmental sustainability domains) to identify green tasks and measure the greenness of jobs.
Applied to vacancy data, the method produces country-specific and time-varying measures of green-task intensity, with demonstrated feasibility in four middle-income economies where traditional labour market data are limited.
Findings show that green vacancies, which have a relatively higher green task intensity, demand a broad mix of competencies—both core and technical skills, across cognitive, socio-emotional and manual domains, as well as green-specific skills.
In some contexts, green vacancies are associated with better wages and desirable working characteristics, though benefits are uneven across countries and occupations.
The approach provides a practical tool to fill evidence gaps and inform inclusive skills and labour market policies that align sustainability with decent work.