Balancing job creation and job loss in China’s low-carbon power shift: Insights into formal and informal employment
中国の低炭素電力転換における雇用創出と雇用喪失のバランス：公式・非公式雇用への洞察 (AI 翻訳)
Qinglan Luo, Xinyu Liu
🤖 gxceed AI 要約
日本語
中国の低炭素電力転換による雇用への影響を、異なる技術経路が雇用の規模と質に与える影響に焦点を当てて分析。産業連関モデルを用いてサプライチェーンを分解し、公式雇用と非公式雇用を区別。石炭依存の経路は非公式の化石燃料雇用を延長するが、再生可能エネルギーへの急速な転換はより多くの高品質な公式雇用を生み出す。政策としてリスキリングやサプライチェーンの国内化、労働の公式化などが必要。
English
This study analyzes the employment impacts of China's low-carbon power transition, focusing on how different technology pathways affect job quantity and quality. Using an inter-sectoral flow model, it segments supply chains and differentiates formal and informal employment. Findings show that a fast non-fossil pathway generates more and higher-quality formal jobs, while coal-based pathways preserve informal fossil fuel jobs. Policy measures such as reskilling, localization of renewable supply chains, and labor formalization are recommended for a just transition.
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
この研究は中国の事例ですが、日本のエネルギー転換における雇用問題にも示唆を与えます。特に、再生可能エネルギーへの移行が雇用の質に与える影響や、石炭依存からの移行における公正な移行政策の重要性は、日本でも関心が高いテーマです。
In the global GX context
This paper provides critical insights for global energy transition discussions, particularly on the trade-offs between job quantity and quality in low-carbon transitions. The findings on formal vs informal employment and the need for targeted reskilling and regional support are relevant for policymakers worldwide, including those in the EU and US focusing on just transition frameworks.
👥 読者別の含意
🔬研究者:Insights into the sectoral and employment quality dimensions of energy transition, useful for models of labor market impacts.
🏢実務担当者:Highlights the importance of planning for job quality and formalization in renewable energy deployment.
🏛政策担当者:Underlines the need for reskilling, localization, and formalization policies to ensure a just transition.
📄 Abstract（原文）
This study explores the employment impact of China’s low-carbon power transition, focusing on how different technological pathways influence both the scale and quality of jobs. The research addresses the twofold challenge of reducing carbon emissions while preserving labor market stability. The study employs an inter-sectoral flow model to break down the supply chain into manufacturing, construction and installation, operation and maintenance, and fuel provision segments, and also differentiates between registered (formal) and unregistered (informal) employment. The findings suggest that a coal-supported pathway not only extends fossil-related jobs, especially informal fuel-related employment, but also limits the development of higher-quality, formal jobs in renewables. In contrast, the fast non-fossil pathway leads to more significant employment increases in total, generates more formal jobs in technical and managerial roles, and is more compatible with long-term sustainability goals. Labor-intensive deployment phases are evident for renewable technologies such as wind and solar, while fossil fuel employment is progressively declining. The transition is not just about the number of jobs but also about the quality of jobs, according to these results. Policy considerations arising from the study underline the necessity of reskilling and upskilling measures, localizing renewable supply chains, labor formalization, and the provision of regional support that is targeted to constitute a just, inclusive, and sustainable energy transition in China. • Study assesses job impacts of China’s low-carbon power transition. • Fast non-fossil pathway creates more and higher-quality formal jobs. • Coal-based paths preserve informal fossil fuel employment. • Renewables drive labor-intensive jobs in deployment phases. • Policies must support reskilling and a just energy transition.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.grets.2026.100417first seen 2026-05-22 04:42:52 · last seen 2026-05-27 04:48:14
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。