Towards an energy theory of value? A critical assessment of the correlation between flows of primary, net, and useful energy flows and monetary indicators for Canada, 1961-2022
Notice bibliographique
Résumé
My thesis examines the relationships between indicators of biophysical quality of energy sources and associated monetary indicators, taking Canada as a case-study for the period from 1961 to 2022. I test the hypothesis of a statistically significant relationship between the caloric value of energy sources (measured in joules) used in the Canadian economy and various associated monetary indicators of value (measured in constant Canadian dollars). I use three different measures of energy to test the impact of energy quality on monetary indicators: one measure not corrected for quality (primary and secondary energy flows) and two measures corrected for quality (net-energy ratios and exergy flows). I use four monetary indicators to examine the connections between biophysical quality and monetary value: price, cost of production, profitability and monetary output. I test the hypothesis at two different scales. I first test the correlation between the disaggregated standard Energy Return on Energy Invested (EROIst) of oil sands-derived crude produced in open-pit mining facilities and their associated spot prices, cost of production and profitability from 1997 to 2016 in the province of Alberta, Canada, using original data. I test the correlation between the each EROIst series and the prices, costs of production and profitability of each crude stream independently, using a simple econometric model using first differences in the variables. The regressions for both crude streams fail to find any statistically significant correlation between monetary and biophysical indicators. I reiterate the test at a macroeconomic level. Using the theoretical framework of aggregate production functions (APF), I build 11 multivariate regression models of the Canadian economy measuring the correlation between output production measured in Canadian dollars and labor, capital and energy flows for Canada from 1961 to 2022 using several measures of energy to correct for quality. To assess the methodological validity of the models, I review the history of production functions and their critique by post-Keynesian and ecological economists. The first series of models (1-8) uses provincially disaggregated data on output regressed over primary and secondary energy flows of energy, labor and capital from 1997 to 2022. The models find labor and energy-use to be statistically significant, with the former bearing more impact on output growth over the latter. These models display a slightly higher predictive power over a standard, 2-inputs model. The price of energy is more statistically significant than energy-use in energy-producing provinces. The second series of models (9-10) uses original data on the net-energy ratio of primary energy consumed and the EROIst of primary energy produced in Canada from 1961 to 2022. Neither are statistically significant when regressed over output production. The model testing for EROIst displays a higher Adjusted R^2 over the model testing for net-energy ratios of energy consumed and flows of primary and secondary energy. The validity of the two models is circumscribed provided the number of negative coefficients yielded. One last model estimates the correlation between inputs and output of the Canadian economy using an exergy-based production function, where flows of labor and capital are modeled as sub-function of the flows of muscle work, mechanical work and heat empowering their economic use. Flows of capital modeled as a sub-function of mechanical work and heat are found to be statistically significant predictors of output growth, meaning Biophysical Production Functions are useful modelling devices. However, their validity is limited by their dependence on monetary figures to aggregate flows of capital
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,016 |
| Études des sciences et des technologies | 0,002 | 0,011 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».