Analysis of Energy-Economic Dynamics in Canadian Provinces: Insights from Data Analytics
Bibliographic record
Abstract
Abstract This paper conducts study of the relationship between end-use energy demand and economic growth as indicated by the Gross Domestic Product (GDP) on a province-level within Canada. There is abundant literature investigating the direction of causality between energy use and economic growth. However, these studies tend to focus on country units or groupings of countries. Additionally, the direction of causality between energy and economic growth has yielded mixed results, which depend on the econometric methods used, the time period, and the inclusion of other economic growth/productivity factors, such as labour and capital. The contribution which this paper seeks to make is to determine the relationship between GDP and energy use within the sub-national units of Canada. This is especially important to help shape the broader conversation about the potential distributional impact of climate change policies on the economic well-being of the sub-nationals of Canada. This paper utilizes the well-known Cobb – Douglas functional form to examine the relationship between inputs, end-use energy demand, and output, defined as GDP for the provinces. Population, End Use Energy Demand and provincial level GDP time series data from 1995 to 2021 are obtained from StatsCan. Our descriptive data analysis revealed that the highly ranked provinces by growth rate of energy use per capita were not necessarily top ranking by GDP per capita growth rate. Taken together, since year 2000, 10 of out of Canada’s 13 provinces and territories have achieved growth in GDP per capita while reducing their Energy use per capita. Additionally, Granger causality showed broadly that the sub-nationals of Canada can be classed majorly as either having neutral relationship between energy and economic growth (46%) or are in the category where economic growth drives the energy use per capita (39%).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".