Incorporating Environmental Pollution and Human Development in the Energy-Growth Nexus: A Novel Long Run Investigation for Pakistan
Bibliographic record
Abstract
Energy acts as a catalyst to boost the human development index (HDI) in a country. However, the overuse of energy leads to environmental deterioration, which is a byproduct of economic development. Due to the utilization of non-renewable energy sources for a long time, worldwide environmental conditions have become alarming. This study investigates the relationship between renewable and non-renewable energy consumption, economic growth, environmental sustainability, and the human development index (HDI) in Pakistan. The investigation incorporates population growth and technology variables to form a multivariate framework. We use a fully modified ordinary least squares (FMOLS) approach to time-series data from 1990–2017. To check the robustness of estimations, we apply the Gregory–Hansen test with a causality test under the VECM to confirm this association’s directions. Our findings confirm that non-renewable energy sources have a positive association with economic growth and CO2 emissions. However, human development, technology, and renewable energy boost economic development and reduce environmental pollution in Pakistan. The co-integration results confirmed the long run connectivity among all variables. The causality outcomes support the bidirectional causality between renewable and non-renewable energy consumption, economic growth, and CO2 emissions, both in the short and long run. These outcomes suggest that Pakistan should focus on energy shifts and gradually increase the share of renewables in its energy mix under the China Pakistan Economic Corridor (CPEC). Additionally, the government should increase human and technological development to enhance economic and environmental sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".