A Stochastic Approach to Energy Policy and Management: A Case Study of the Pakistan Energy Crisis
Bibliographic record
Abstract
The energy policy of a country dictates its ability to better manage and deal with an energy crisis. A sustainable energy policy deals with not only energy production but also with energy consumption. In the past, the government of Pakistan has lacked such an approach. This study aims to develop a policy-making framework to improve the energy management of Pakistan through a probabilistic approach. Stochastic analysis is performed in this study and the uncertainty in energy data is used to propose a holistic energy policy. Energy-utilization data from 17 different sources are used to compare the accuracy of energy-consumption data from 1989 to 2013. The analysis reveals that there exists an uncertainty in energy-consumption data and the major cause of this uncertainty is energy theft. The analysis shows that the industry has the highest uncertainty in its energy-data utilization, followed by the transport and the domestic sectors of Pakistan. Based on stochastic analysis, seven recommended energy-policy guidelines are presented to manage the energy crisis in the country. The analysis proposes that Pakistan needs to take measures to control energy theft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".