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An Analytical Framework for the Assessment of Employment Impacts of Energy Transition

エネルギー転換の雇用影響評価のための分析枠組み (AI 翻訳)

Haritha Songola, Rudeena Jabar, Anjali Sharma

SocArXiv (OSF Preprints)プレプリント2026-06-28#エネルギー転換Origin: Global対象セクター: cross_sector
原典: https://osf.io/3agz4

🤖 gxceed AI 要約

日本語

本論文はエネルギー転換が雇用に与える影響を系統的にレビューし、236本の研究を分析。雇用推計のばらつき要因として評価手法、シナリオ前提、学習効果の欠如などを特定し、過小・過大評価を防ぐ分析枠組みを提案。

English

This paper systematically reviews 236 studies on employment impacts of energy transition, identifying methodological variations, scenario assumptions, and lack of learning effects as key factors causing divergent estimates. It proposes an analytical framework to benchmark labor impact assessments.

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政策では、グリーン成長戦略による雇用創出が重要な政策目標。本枠組みは、再エネ導入や産業転換による雇用効果を適切に評価する際の基準として活用可能。

In the global GX context

Globally, the just transition discourse emphasizes employment co-benefits. This framework helps standardize labor impact assessments, informing evidence-based climate policy design across countries.

👥 読者別の含意

🔬研究者:Energy transition researchers can adopt this framework to conduct more rigorous employment impact studies.

🏢実務担当者:Corporate sustainability planners can use the framework to anticipate workforce shifts during decarbonization.

🏛政策担当者:Policymakers should consider the framework's elements (learning effects, skill requirements) to design effective just transition policies.

📄 Abstract(原文)

The potential employment generation is an important co-benefit and policy motivation to pursue energy transition. Empirical evidence suggests that energy transition leads to net employment gains. However, the job estimates vary across studies due to methodological choices, underlying modelling assumptions and scope of analysis. In this study, we present a systematic review of the studies that have examined labor impacts of energy transition to understand the underlying mechanisms that impact employment estimates. Using the Context-Interventions-Mechanisms-Outcome framework, we selected 236 papers for the study. The paper identifies multiple factors that explain these variations: first, the type of assessment used in the study impacts the projected job numbers. Gross assessment (42% of studies in our sample) provide optimistic results without accounting for the potential job losses. Second, ambitious climate action scenarios in an economy leads to the increase in projected jobs. We find that 25% of studies are dependent on the scenario assumptions. Third, employment details such as employment factors, learning effects, job quality and skills determine the intensity of the job estimates. The review suggests that only 12% of the studies have included learning effects in their assessment and 15% have assessed the skill requirements for the manufacturing of renewables. Based on these limitations in the existing literature, we have developed an analytical framework that can serve as the benchmark for studying the labor impacts of energy transitions. The framework systematically identifies and highlights the importance of integrating different elements previously discussed in the labor assessment studies to avoid the over-estimation/underestimation of employment numbers.

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