低炭素移行における新興スキルと賃金格差：オンライン求人データからの証拠
Emerging skills and wage gaps in the low-carbon transition: evidence from online job vacancy data (原題)
Aurélien Saussay, Misato Sato, Francesco Vona
🤖 gxceed AI 要約
日本語
米国のオンライン求人データを用い、NLPと既存のグリーン分類を組み合わせて低炭素職を特定する手法を開発。低炭素雇用は低技能職で多いが、同一職種内ではより複雑なスキルを要求し、賃金プレミアムは小さい。賃金プレミアムは時間とともに減少し、企業固定効果に起因する。低炭素雇用は高炭素雇用と空間的に相関し、富裕地域に多い。
English
Using U.S. online job vacancy data, this paper develops a skill-based NLP method to identify low-carbon jobs within occupations. Low-carbon job creation is prevalent in low-skilled occupations but requires more complex skills, yielding a modest wage premium that declines over time and is driven by firm fixed effects. Low-carbon wage premia are smaller than high-carbon ones, especially in STEM. Low-carbon jobs correlate spatially with high-carbon employment and are more common in wealthier areas.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX移行において、労働市場のスキル転換は重要な政策課題。SSBJ開示や産業政策と連動し、企業の人材育成や再教育戦略に示唆を与える。日本でも求人データを用いた同様の分析が可能であり、政策立案の参考になる。
In the global GX context
This paper contributes to global GX scholarship by providing a transparent, skill-based method to identify low-carbon jobs, relevant for just transition policies and workforce planning. It offers evidence on wage dynamics and spatial patterns that can inform international climate policy and corporate reskilling strategies.
👥 読者別の含意
🔬研究者:Provides a novel NLP-based methodology for identifying low-carbon jobs and analyzing wage gaps, useful for labor market and transition research.
🏢実務担当者:Offers insights into skill requirements and wage trends for low-carbon roles, aiding workforce planning and talent strategy.
🏛政策担当者:Highlights the need for targeted reskilling programs and spatial considerations in just transition policies.
📄 Abstract（原文）
Standard occupational classifications obscure identifying which jobs directly con tribute to decarbonization. Using U.S. online job vacancy data, we develop a trans parent, skill-based approach that identifies low-carbon jobs within occupations, by combining advanced natural language processing with text linked to established green classifications. We show that low-carbon job creation is more prevalent in low-skilled occupations, yet low-carbon jobs systematically require more complex skill sets than comparable generic jobs in the same occupations. These higher skill requirements are associated with a modest wage premium that declines over time and is largely driven by firm fixed effects. Reskilling patterns and low-carbon wage premia vary substantially across occupations, and the latter are markedly smaller than the high-carbon wage premia, especially in STEM occupations. Finally, low carbon jobs are more spatially correlated with high-carbon employment than just renewable energy jobs, but are also more prevalent in wealthier areas.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1086/743961first seen 2026-09-06 05:09:39
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