用招聘信息预测汽车业绿色技能需求，发现氢能与回收技术增速最快。
Forecasting Green Skill Demand in the Automotive Industry: Evidence from Online Job Postings
- 通过多语言嵌入+ESCO标准筛选20万条招聘数据，识别出274种绿色技能。
- 基于时间序列模型预测需求，Transformer类方法误差低于15%（相对RMSE）。
- 分类发现氢能、回收等技能增长最快，适合政策制定者和企业人才规划。
全球向可持续经济转型正重塑劳动力市场，但系统性识别与预测绿色技能需求的方法仍有限。本研究构建计算框架，利用墨西哥汽车工业的在线招聘信息（2024年7月至2025年7月），涵盖Indeed Mexico、OCC Mundial和LinkedIn，共采集204,373条技能记录。通过两阶段流程——多语言嵌入与ESCO标准验证——识别出8,576次出现的274个独特绿色技能（占总技能的4.22%）。在滚动起源评估下对比15种时间序列模型，基于Transformer的FEDformer、Reformer和Informer表现最优，平均绝对误差（MAE）约2.5e-5，相对均方根误差（relative RMSE）低于15%。进一步提出按绝对与相对增长分类框架，识别出稳定、新兴与高影响能力。结果表明当前需求集中于运营可持续实践，而增速最快的是可再生能源、回收及氢能源技术相关技能。该流程可支持绿色转型中的数据驱动型人力资源规划。
原文摘要 · Abstract (English)
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.
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