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Record W4251739204 · doi:10.1504/ijbg.2020.104959

A general equilibrium analysis of technological change to cope with environmental degradation affecting product prices

2020· article· en· W4251739204 on OpenAlex
Afshin Amiraslany, Amarjit Gill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Business and Globalisation · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTechnological changeGeneral equilibrium theoryClimate changeAdaptation (eye)EconomicsEnvironmental degradationNatural resource economicsProduct (mathematics)Goods and servicesIndustrial organizationPartial equilibriumEnvironmental economicsBusinessMicroeconomicsMacroeconomicsEconomyEcology

Abstract

fetched live from OpenAlex

Adaptation is a natural way of responding to climate change's harmful impacts. As technological change plays a significant role in adaptation, this paper discusses the effects of change in technology by employing a general equilibrium (GE) model. A two-sector, two-factor, two-country general equilibrium model is developed to analyse the interaction among environmental policy, trade, and adaptation strategy of producers (technological change). The results suggest that any technological change for adapting to the climate change negatively affects the relative price of the final goods; therefore, a small open country will have comparative advantage to export its goods and services. However, an environmental policy of increasing emissions tax will decrease the country's exports. This study adds to the relevant literature by introducing one separate mitigation sector to the GE model to investigate both mitigation and adaptation as a solution to environmental degradation.

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.

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)0.000
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 score0.059
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.222
Teacher spread0.185 · 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