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Record W4401809953 · doi:10.55016/ojs/sppp.v6i1.42444

The “Green Jobs” Fantasy: Why the Economic and Environmental Reality Can Never Live Up to the Political Promise

2013· article· en· W4401809953 on OpenAlexaffabout
Jennifer Winter, Michal C. Moore

Bibliographic record

VenueThe School of Public Policy Publications · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFantasyPoliticsEnvironmental ethicsPolitical scienceArtPhilosophyLiteratureLaw

Abstract

fetched live from OpenAlex

Agriculture is one of the least “green” — that is, the least environmentally friendly — sectors in Canada, based on its energy-use intensity and greenhouse gas emissions intensity. But agriculture is also the “greenest” sector in Canada, according to one measure that calculates the proportion of “green employment” in various industries. Welcome to the world of “green jobs,” where vague definitions often give energy-intensive, carbon-heavy industries a “green” stamp of approval. Examples include companies making solar panels, but using large volumes of energy to do so or where an accountant preparing financial returns is counted as a “green” worker at one office, but turns instantly “dirty” should he cross the street to do the same accounting work at another office. It is also a world where inefficient power generation is considered positive, if it means employing more “green workers” per unit of power output, regardless of any negative effects that may have on the economy. The concept of “green jobs” has become immensely popular among policy planners looking to address the problem of global warming, yet are aware of the economic costs of anti-carbon measures. The promise that western economies can reduce carbon emissions while creating thousands, if not millions, of “green jobs” — which will more than compensate for the job losses that will occur in sectors reliant on fossil fuels — has been especially embraced by politicians, relieved to find a pro-climate policy that also doubles as a pro-economic policy. Unfortunately, there is scant agreement on what fairly qualifies as a “green job,” and much evidence that what policy-makers frequently consider “green jobs” are, in fact, existing jobs, belonging to the traditional economy, but simply reclassified as “green.” By emphasizing “green jobs,” policy-makers risk measuring environmental progress based on a concept that can often be entirely irrelevant, or worse, can actually be detrimental to both the environment and the economy. Too often, “green job” policies reward inefficiency, while also failing to distinguish between permanent, full-time jobs and temporary or part-time jobs. In some cases they can also discourage trade, limit or thwart competition, result in greater job losses elsewhere in the economy, and demand massive government subsidies, with some government “green job” programs requiring hundreds of thousands of dollars, or even millions, to create a single job. The urge of politicians to champion “green employment” is understandable given its convenient, if frequently unrealistic promise of a politically saleable anti-carbon policy. However, a more reliable and meaningful measure of environmental progress ultimately has little to do with the number of jobs a particular company creates (after all, if economic efficiency — and hence, prosperity — is indeed a policy goal, the number of jobs created should ideally be as minimal as necessary for every unit of output). Rather, if minimizing energy use and greenhouse gas emissions is the desired policy outcome, then measuring the intensity of energy use and greenhouse gas emissions per unit of output can be the only meaningful metric. It may not have the political appeal that a promise of “green jobs” does. But unlike “green jobs,” both of these measures provide quantifiable, non-arbitrary metrics of environmental performance and progress. In other words, unlike the problematic, arguably illusory concept of “green employment,” measuring energy-use intensity and emissions intensity actually tells us very clearly and reliably whether we are making the environment better or worse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.040
GPT teacher head0.250
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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