Integration of Regression Analysis and Monte Carlo Simulation for Probabilistic Energy Policy Guidelines in Pakistan
Bibliographic record
Abstract
Forecasting energy demand and supply is the most crucial concern for energy policymakers. However, forecasting may introduce uncertainty in the energy model, and an energy policy based on an uncertain model could be misleading. Without certainty in energy data, investors cannot quantify risk and trade-offs, which are compulsory for investments in energy projects. In this work, the energy policies of Pakistan are taken as a case study, and flaws in its energy policymaking are identified. A novel probabilistic model integrated with curve fitting methods was proposed and was applied to 17 different energy demand and supply variables. Monte Carlo simulation (MCS) was performed to develop probabilistic energy profiles for each year from 2017 to 2050. Results show that the forecasted energy supply of Pakistan in the years 2025 and 2050 would be 70.69 MTOE and 131.65 MTOE, respectively. The probabilistic analysis showed that there is 14% and 6% uncertainty in achieving these targets. The research shows the expected energy consumption of 70.33 MTOE and 189.48 MTOE in 2025 and 2050, respectively, indicating uncertainties of 65% and 31%. Based on the results, eight energy policy guidelines and recommendations are provided for sustainable energy resource management. This study recommends developing a robust and sustainable energy policy for Pakistan with the help of transparent governance.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".