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A NOVEL APPROACH TO CHARACTERIZING THE RELATIONSHIP BETWEEN ECONOMIC GROWTH AND ENERGY CONSUMPTION

2021· article· en· W4200156661 on OpenAlexaffabout
Danylo Cherevatskyi, Roman G. Smirnov

Bibliographic record

VenueEconomy of Ukraine · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEconomicsRelevance (law)Consumption (sociology)Energy consumptionEconometricsEmpirical researchHamiltonian (control theory)EconomyMacroeconomicsMathematicsMathematical optimizationStatisticsEngineering

Abstract

fetched live from OpenAlex

At the macroeconomic level, the consumption of primary energy resources is a deterministic factor of economic development, which justifies the relevance and importance of introducing an appropriate mathematical model.Using the tools of data-driven dynamical systems and Hamiltonian mechanics, we determine the relationship between energy costs and the distance traveled by a particular national economy in the economic space.The basis for the calculations is the time series describing the evolution of a cumulative GDP, recalculated according to the original method from monetary units to linear geometric dimensions, and energy resources consumed over a fixed period of time.The relevance of the mathematic relationships so obtained is verified by comparing the model predictions with the empirical data produced by four national economies, namely those of Canada, Poland, Russia, and Ukraine. In all of the four cases the model demonstrated a nearly perfect fit to the actual data.The approach via approximation the evolutions of economic performance and energy consumption by parabolas proposed in the article lies the groundwork for developing a general theory that may be employed to analyze economic performance of a wide spectrum of national economies and world regions.

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 score0.412
Threshold uncertainty score0.538

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 head0.061
GPT teacher head0.230
Teacher spread0.169 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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