Green jobs and the Green economy in York
Notice bibliographique
Résumé
For a number of years, a purist definition of green jobs has been used. This definition is proving to be problematic and more inclusive definitions are gaining policy traction. One broader definition has been developed by IER and adopted within the UK – the GreenSOC. This broader definition offers three types of green jobs, one of which aligns loosely with the purist definition. \nOn the purist definition, there are around 1,800 people working in green sector jobs in York TTWA, this figure represents 1% of the current workforce. Analysis of job vacancy postings that ask for purist green skill terms in their vacancy adverts suggests that 2% of current vacancies ask for such skills. This latter figure has fluctuated between 1% and 4% of total job vacancy postings in York over the past three years. \nEmploying the inclusive definition, our estimates suggest that there are 28% of people in the York TTWA, and one quarter of York City residents, who are currently working in green jobs. These jobs are mostly green increased demand jobs, which comprise around 45% of green jobs in both areas or 12% of total employment. The next largest green jobs category are green enhanced skills and knowledge jobs, which constitute around one third of green jobs or 9% of all jobs. Finally, green new and emerging jobs (which is closest to the purist definition) account for about one in five green jobs or 6% of total jobs. \nThe nature of green jobs varies across broad occupational groups. Green enhanced skills and knowledge jobs are most prevalent in plant and machine process operative occupations, associate professional and technical, and managerial occupations. Green new and emerging are more significant in skilled trades, and professional occupations. Green increased demand jobs are sizeable in most broad occupations. \nAnalysis of occupations at a more detailed level shows that many of these jobs are currently green increased demand, and in occupations that concern the distribution, logistics and financing of the green economy (i.e. service sector jobs), as well as the manufacture, installation and maintenance of green products. \nThe job vacancy postings data calculates that just over one third of vacancies are for green jobs, and that two thirds of these jobs are green enhanced skills and knowledge (23% of all job postings), one quarter are green increased demand jobs (9%), and 6% are green new and emerging jobs (2%). \nMost green jobs, in both York TTWA and York City, are in skilled trades, associate professional and technical, and plant and machine process operative occupations. The job vacancy postings data indicates that skilled trades, and process, plant and machine operative occupations account for the largest proportion of green jobs in York TTWA. Both the current employment and job vacancy postings data indicate that there are few green jobs in administrative and secretarial, and caring, leisure and service occupations. \n38 \nA number of the top ten green detailed occupations in the employment and job vacancy postings data are the same. Green job postings tend to include more IT detailed occupations rather than skilled trades, especially in the construction sector. \nAnalysis of current employment and job vacancy postings by sector shows that it is the public administration, education and health, distribution, hotels and restaurants, and banking, finance and insurance sectors where most green jobs are located. However, as a proportion of jobs within sectors, agriculture, forestry and fishing, construction, and transport and communication have the largest proportion of green jobs. \nThe skills and knowledge requirements of green jobs are very similar to non-green jobs. This similarity is apparent when examining the skills and knowledge of specific green and non-green occupations as well as the skills, knowledge and skills terms in broader occupation groupings. Analysis of the skills, knowledge and skills terms of green jobs within broad occupation groups shows that there are a number of skills and knowledge requirements that are the same across green and non-green jobs, as well as different types of green jobs. \nThe reasons for the similarities between green and non-green jobs is because a number of key functional, transferable and technical skills are necessary to perform most jobs. It also reflects the fact that most green jobs are green increased demand jobs or green enhanced skills and knowledge jobs requiring no or incremental changes respectively in the tasks undertaken.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,012 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».