İngiltere’de Enerji Ar-Ge Harcamaları ile Enerji Tüketimi İlişkisi: Yapısal Kırılmalı Eşbütünleşme Analizi
Bibliographic record
Abstract
In 2018 compared to 1990, the UK'S GDP by 75.44%, renewable energy R&D expenditure by 160.47%, and renewable energy use increased by 1318,478% increased, while decreased energy consumption by 14.92% and energy losses by 18.15%. In other words, In addition to increasing the use of renewable energy, the UK has also managed to reduce total energy consumption and total energy losses. In addition, in the same period, Canada, which is among the G7 countries, increased its total energy consumption by 40.85%, the USA 16.49%, France 10.06%, and Italy 2.74%, while Germany decreased by 13.99% and Japan 2.89%. England, on the other hand, managed to reduce it at a remarkable rate of 14.92%. Therefore, the difference of this study from other studies is that the question of how the UK earned more income with less energy as a result of its R&D expenditures in the field of energy in the relevant period is investigated by econometric methods. While doing this, R&D expenditures and total energy consumption in the fields of renewable energy, non-renewable energy, and nuclear energy were used in the 1990-2018 period for the UK. For this purpose, the stability of the variables was investigated with the Perron (1989) and Zivot and Andrews (1992) tests, in which structural breaks were taken into account. Then, the long-term relationship was tested with the Gregory and Hansen cointegration test and a cointegration relationship was found. Then, it is tested with Fully Modified Ordinary Least Square (FMOLS) and Canonical Cointegrating Regression (CCR) estimators for a long-term relationship. The findings showed that while the R&D expenditures for nuclear energy decreased the energy use the most for the UK in the long term, it was found that the R&D expenditures for renewable energy increased the most.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".