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

TL;DR

This paper develops a computational framework to identify and forecast green skill demand in Mexico's automotive industry using online job postings, highlighting emerging skills and supporting workforce planning.

Contribution

It introduces a novel pipeline combining multilingual embeddings and validation for green skill detection and forecasts demand using advanced time series models.

Findings

Transformer models outperform others with MAE around 2.5e-5

Demand is concentrated in operational sustainability practices

Fastest-growing skills include renewable energy and hydrogen technologies

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.