An Empirical Analysis of Denmark's Energy Economy and Environment and Its Sustainable Development Policy
Bibliographic record
Abstract
As a model for energy transition to low-carbon economy, Denmark is of great importance for studying internal relationships between economic growth, both energy consumption and production, and Carbon emissions. Based on Denmark’s data for the total consumption of petroleum oil and gas resources, total production of oil and gas resources, gross domestic product(constant 2010 US$) and CO2 emissions over the time span 1984-2016, Johansen test shows that there is no cointegration relationship between CO2 emissions and oil and gas consumption, and the autoregressive distributed lag (ARDL) boundary cointegration test shows that there is no cointegration relationship between CO2 emissions and economic growth. Nevertheless, ARDL boundary cointegration test is used to confirm the existence of cointegration between economic growth and both the energy production and consumption. We then establish an error correction model to analyze the short-term relationship between these two cointegrated metrics. The Granger causality test indicates that there is one-way causality between economic growth and energy consumption and energy production; in particular, economic changes help explain changes of energy consumption and production in the future. Finally, the empirical analysis results are further discussed with consideration of Denmark’s energy policies and the current state of its energy economy. The results of the present study can help the other countries in the design of energy development, the clean and low carbon energy transition policies for sustainable and long-term economic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".