Impact of Water Management Policies on Volatility Transmission in the Energy Sector
Bibliographic record
Abstract
Purpose: This study evaluates the impact of the water management policies of energy companies on their volatility interactions with energy commodities. Design/methodology: We tested for volatility transmissions between 66 energy funds and fossil-fuel commodities. After identifying possible integrations, we investigated whether water management policies, after controlling for other fund characteristics, impact the probability of integration. Results: Our findings indicate strong volatility transmission from oil prices to energy funds. However, a reverse of this information flow was not observed. From the perspective of natural gas, we found strong bi-directional integration with energy funds. When we analyzed the influence of fund characteristics on the previously established integrations, water management policies do not impact the probability of the integration of oil. However, these policies are shown to have a significant influence on integration with the natural gas market. Originality/value: While there are multiple studies that show the integration between energy companies and corresponding commodities, according to our knowledge, this is the first study that evaluates the significance of water management policies with respect to volatility integration. This study highlights the importance of water-related policies with respect to the susceptibility of energy firms to volatility contagion from the natural gas market.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".