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Record W2598564337 · doi:10.1504/ijesd.2017.10004021

Impact of seasonal temperature rise on labour and capital productivity in manufacturing sector: a study with Canadian panel data

2017· article· en· W2598564337 on OpenAlexaffabout
Hasnat Dewan, Anichul Hoque Khan

Bibliographic record

VenueInternational Journal of Environment and Sustainable Development · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsProductivityAgricultural economicsPanel dataProduction (economics)Climate changePer capitaManufacturing sectorAgricultureEconomicsManufacturingPopulationAgricultural productivityNatural resource economicsEnvironmental scienceBusinessGeographyLabour economicsEconomic growthOceanographyDemography

Abstract

fetched live from OpenAlex

Rising global temperature is one of the manifestations of climate change. This study analyses the impact of average temperature rise in summer and winter on production in the manufacturing sector, which is mostly indoor production. This study also compares the differential impact of temperature rise on indoor and outdoor production. Using panel data from ten Canadian provinces for the period of 1997 to 2010, it finds that the rise of average temperature in summer causes labour and capital productivity in manufacturing sector to decline. As a result the production of manufacturing goods falls, but this impact is much weaker compared to that on outdoor production, i.e., the production in agriculture, forestry, fishing, and hunting sectors together. Average temperature rise in winter, however, leaves production in the manufacturing sector unaffected. These results are obtained by controlling for population growth, GDP per capita, and yearly-dummies. These findings may have some policy implications for Canada and other countries that have been experiencing warmer than usual summer and winter temperatures due to climate change.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.250
Teacher spread0.205 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2017
Admission routes2
Has abstractyes

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