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Forecasting the Energy-Driven Green Transition of European Labour Markets: A Composite Readiness Index
by
Man, Mariana
, Hojda, Mihaela Hortensia
, Oncioiu, Ionica
, Ghiberdic, Marius Florin
in
Algorithms
/ composite index
/ Data mining
/ Digital technology
/ Econometric models
/ Econometrics
/ economic forecasting
/ Employment
/ Energy transition
/ Forecasts and trends
/ green economy
/ Infrastructure
/ Innovations
/ Labor market
/ labour market
/ Machine learning
/ Regions
/ Renewable resources
/ Research & development expenditures
/ Surveillance
/ Unemployment
2026
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Forecasting the Energy-Driven Green Transition of European Labour Markets: A Composite Readiness Index
by
Man, Mariana
, Hojda, Mihaela Hortensia
, Oncioiu, Ionica
, Ghiberdic, Marius Florin
in
Algorithms
/ composite index
/ Data mining
/ Digital technology
/ Econometric models
/ Econometrics
/ economic forecasting
/ Employment
/ Energy transition
/ Forecasts and trends
/ green economy
/ Infrastructure
/ Innovations
/ Labor market
/ labour market
/ Machine learning
/ Regions
/ Renewable resources
/ Research & development expenditures
/ Surveillance
/ Unemployment
2026
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Forecasting the Energy-Driven Green Transition of European Labour Markets: A Composite Readiness Index
by
Man, Mariana
, Hojda, Mihaela Hortensia
, Oncioiu, Ionica
, Ghiberdic, Marius Florin
in
Algorithms
/ composite index
/ Data mining
/ Digital technology
/ Econometric models
/ Econometrics
/ economic forecasting
/ Employment
/ Energy transition
/ Forecasts and trends
/ green economy
/ Infrastructure
/ Innovations
/ Labor market
/ labour market
/ Machine learning
/ Regions
/ Renewable resources
/ Research & development expenditures
/ Surveillance
/ Unemployment
2026
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Forecasting the Energy-Driven Green Transition of European Labour Markets: A Composite Readiness Index
Journal Article
Forecasting the Energy-Driven Green Transition of European Labour Markets: A Composite Readiness Index
2026
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Overview
The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable.
Publisher
MDPI AG
Subject
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