The Relationship Between Financial Crisis and Energy Efficiency: A Sectoral Study in Turkey
Bibliographic record
Abstract
Energy efficiency, which refers to services and output being produced with less energy input, has become an important issue in terms of increasing environmental performance, energy security and international competitiveness today. Sectoral energy efficiency directly affects energy consumption per output on one hand, and contributes positively to profitability through costs on the other. It results in a competitive advantage by enabling investments to be made at a lower cost compared to other sectors and shortening the break-even period of the investments. However, a decrease in cash flow due to difficult financing conditions created by the financial crisis, which emerged in the housing market in the US in 2008 and became a global crisis through the financial sector, affected the energy sector by creating a negative impact on energy demand and supply. The cancellation of projects due to lack of financing, reduction of oil supply and drilling operations of energy companies, and reductions in refined pipelines are examples of the problems experienced on the supply side. On the consumption side, equipment and device sales decreased. The effect of the financial crisis on energy efficiency will be examined in this study. Using the Logarithmic Mean Divisia Index Decomposition method, the causes of changes in energy intensity and energy use will be analyzed on a sectoral basis in Turkey. The aim of this study is to explain the sectoral changes caused by the financial crisis with the help of energy intensity measure based on an analysis conducted with current data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".