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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.05280 [cs.LG] |
| (or arXiv:2605.05280v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05280 arXiv-issued DOI via DataCite (pending registration) |
From: Sabur Butt [view email]
[v1]
Wed, 6 May 2026 16:51:53 UTC (165 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。