A new measurement approach for identifying high-polluting jobs across European countries:
This paper develops a novel classification of high-polluting occupations for a large sample of European countries. Unlike previous efforts in the literature, the classification exploits country-level data on air polluting emission intensity by industry. The country-level data allows to capture impor...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Paris
OECD Publishing
2024
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| Schriftenreihe: | OECD Economics Department Working Papers
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| Online-Zugang: | Volltext |
| Zusammenfassung: | This paper develops a novel classification of high-polluting occupations for a large sample of European countries. Unlike previous efforts in the literature, the classification exploits country-level data on air polluting emission intensity by industry. The country-level data allows to capture important cross-country differences, due to differences in technology and in production focus. Applying the new classification to European Labour Force Survey data shows that, on average across the countries covered, about 4% of workers are employed in high-polluting jobs, ranging from 9% in Czechia and the Slovak Republic to around 2% in Austria. These shares do not exhibit any clear decreasing trend over the past decade. High-polluting jobs are unequally distributed, being over-represented among men, workers with lower and medium educational attainment and those living in rural areas |
| Beschreibung: | 1 Online-Ressource (26 Seiten) 21 x 28cm |
| DOI: | 10.1787/f5127e4c-en |
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| indexdate | 2024-12-09T19:00:23Z |
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| spelling | Causa, Orsetta Verfasser aut A new measurement approach for identifying high-polluting jobs across European countries Orsetta, Causa, Maxime, Nguyen and Emilia, Soldani Paris OECD Publishing 2024 1 Online-Ressource (26 Seiten) 21 x 28cm txt rdacontent c rdamedia cr rdacarrier OECD Economics Department Working Papers This paper develops a novel classification of high-polluting occupations for a large sample of European countries. Unlike previous efforts in the literature, the classification exploits country-level data on air polluting emission intensity by industry. The country-level data allows to capture important cross-country differences, due to differences in technology and in production focus. Applying the new classification to European Labour Force Survey data shows that, on average across the countries covered, about 4% of workers are employed in high-polluting jobs, ranging from 9% in Czechia and the Slovak Republic to around 2% in Austria. These shares do not exhibit any clear decreasing trend over the past decade. High-polluting jobs are unequally distributed, being over-represented among men, workers with lower and medium educational attainment and those living in rural areas Economics Nguyen, Maxime ctb Soldani, Emilia ctb https://doi.org/10.1787/f5127e4c-en Verlag kostenfrei Volltext |
| spellingShingle | Causa, Orsetta A new measurement approach for identifying high-polluting jobs across European countries Economics |
| title | A new measurement approach for identifying high-polluting jobs across European countries |
| title_auth | A new measurement approach for identifying high-polluting jobs across European countries |
| title_exact_search | A new measurement approach for identifying high-polluting jobs across European countries |
| title_full | A new measurement approach for identifying high-polluting jobs across European countries Orsetta, Causa, Maxime, Nguyen and Emilia, Soldani |
| title_fullStr | A new measurement approach for identifying high-polluting jobs across European countries Orsetta, Causa, Maxime, Nguyen and Emilia, Soldani |
| title_full_unstemmed | A new measurement approach for identifying high-polluting jobs across European countries Orsetta, Causa, Maxime, Nguyen and Emilia, Soldani |
| title_short | A new measurement approach for identifying high-polluting jobs across European countries |
| title_sort | a new measurement approach for identifying high polluting jobs across european countries |
| topic | Economics |
| topic_facet | Economics |
| url | https://doi.org/10.1787/f5127e4c-en |
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