Modelling for policy assessment in the electricity supply sector of Pakistan
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a long‐term assessment of Pakistan's electricity policy in the context of both environmental and resource constraints. To increase the sustainability of energy supply, the Government of Pakistan introduced a series of reforms in the electricity supply sector during 1990‐1995. In response to these policy incentives, most of the independent power producer offers included coal, oil, and/or gas‐based power plants. Considering that Pakistan produces only up to 40 percent of its oil demand domestically and thermal power generation causes CO2emissions, there is a great need for an assessment of the existing electricity policy. Design/methodology/approach Drawing on system dynamics methodology, this study presents and utilizes a dynamic simulation model that captures the dynamics of the sectors underlying the electricity supply system including investments, capital, production, resources, financial resources, and the environment. Findings The key findings of this study are: policy incentives encouraged thermal‐based generation at the potential expense of hydro power generation; and the evolution of electricity supply related CO2emissions exhibits an exponential growth. Research limitations/implications While there are other emissions related to the electricity supply system with potentially severe environmental concerns, for example SO2, this study focuses only on CO2emissions. Originality/value The paper offers a system dynamics model and provides some useful policy insights for the electricity supply sector of Pakistan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".