Forecasting Green Skill Demand in the Automotive Industry: Evidence from Online Job Postings
Authors: Sabur Butt, Joshua N. Arrazola E., Hector G. Ceballos, Patricia Caratozzolo
Organizations: Institute for the Future of Education, Tecnológico de Monterrey, Monterrey, 64849, Mexico · Universidad Politécnica de Victoria, Victoria, Mexico
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.
Recent research in educational data mining, especially knowledge tracing, has focused on predicting learners' future knowledge states to support adaptive instruction. However, in many real-world educational settings, learning data are often available only as single-time-point assessments without temporal information, making existing time-series-based approaches difficult to apply. In this paper, we propose a novel framework for predicting future skill acquisition using only snapshot data. Specifically, we address the problem of predicting the next skill to be acquired from skill mastery patterns estimated by cognitive diagnostic models (CDMs). In the absence of temporal information, we exploit inclusion relations among learners' skill sets to induce a pseudo-temporal ordering, interpreting expanding skill sets as a proxy for learning progression. To efficiently approximate unobserved acquisition paths, we introduce a neural model that captures latent skill acquisition dynamics through expected skill increments. Experiments on both synthetic and real-world datasets demonstrate that the proposed method consistently outperforms baseline approaches, with particularly strong advantages as the skill space becomes larger. These results indicate that meaningful skill acquisition patterns can be inferred from snapshot data alone, providing a practical framework for adaptive learning support in data-constrained educational environments.
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-market matching efficiency and supporting personalized employment services. To the best of our knowledge, this work presents the first Chinese JobSkillNER dataset for recruitment texts. We propose annotation guidelines tailored to Chinese job postings and an LLM-empowered Macro-Micro collaborative annotation pipeline. The pipeline leverages the contextual understanding ability of large language models (LLMs) for initial annotation and then refines the results through expert sentence-level adjudication. Using this pipeline, we annotate more than 20,000 instances collected from four major recruitment platforms over the period 2014-2025. Based on these efforts, we release Chinese-SkillSpan, the first Chinese JobSkillNER dataset aligned with the ESCO occupational skill standard across four dimensions: knowledge, skill, transversal competence, and language competence (LSKT). Experimental results show that the dataset supports effective model training and evaluation, indicating that Chinese-SkillSpan helps fill a major gap in Chinese JobSkillNER resources and provides a useful benchmark for intelligent recruitment research. Code and data are available at https://sites.google.com/view/cn-skillspan-resources .
Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lowering the human-capital barrier to programming, ChatGPT enables incumbent freelancers to enter programming tasks. Moreover, these transitions are not homogeneous, with high-skilled freelancers contributing disproportionately to the shift. Our findings illuminate the multifaceted impacts of general-purpose AI on labor markets, highlighting not only the displacement of certain occupations but also the inducement of skill transitions within the labor supply. These insights offer practical implications for policymakers, platform operators, and workers.