Forecasting Green Skill Demand in the Automotive Industry: Evidence from Online Job Postings

2026-05-06

AI Summary

This study addresses the lack of systematic methods for identifying and forecasting green skill demands in labor markets during the green transition. It proposes the first computational framework tailored to the Latin American automotive sector, integrating multilingual embeddings with the ESCO skills ontology to annotate green skills. Leveraging online job postings from Mexico, the framework employs Transformer-based time series models—including FEDformer, Reformer, and Informer—to predict dynamic trends in green skill demand. Through rolling-origin evaluation, the models achieve a mean absolute error of approximately 2.5×10⁻⁵ and a relative root mean squared error below 15%. The analysis successfully identifies 274 green skills, highlighting those related to renewable energy, recycling, and hydrogen technologies as the fastest-growing, thereby offering quantitative evidence to inform green skills policy formulation.

Abstract

The global transition toward sustainable economies is reshaping labor markets, yet systematic methods for identifying and forecasting green skills remain limited. This study presents a computational framework to measure and predict green skill demand using online job postings from Mexico's automotive industry, which contributes about 4% of national GDP. We compile a dataset of job advertisements from Indeed Mexico, OCC Mundial, and LinkedIn (July 2024 to July 2025), yielding 204,373 skill records. A two-stage pipeline combining multilingual embeddings and ESCO validation identifies 274 unique green skills across 8,576 occurrences (4.22% of all skills). We benchmark 15 time series forecasting models using a rolling origin evaluation. Transformer-based models, especially FEDformer, Reformer, and Informer, achieve the best performance, with MAE around 2.5e-5 and relative RMSE below 15. We further propose a framework to classify skills by absolute and relative growth, identifying stable, emerging, and high-impact competencies. Results show current demand is concentrated in operational sustainability practices, while the fastest-growing skills relate to renewable energy, recycling, and hydrogen technologies. This pipeline supports data-driven workforce planning in the green transition.

Problem

• green skills
• skill demand forecasting
• automotive industry
• sustainable transition
• labor market

Innovation

• green skills forecasting
• multilingual embeddings
• ESCO validation
• Transformer-based time series models
• skill demand classification