Green jobs and the Green economy in York
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".