Analysis of Urbanization and Energy Consumption Using Time Series Data: Evidence from the SAARC Countries
Bibliographic record
Abstract
Urbanization has posed some tremendous challenges which are related to environmental stresses through increased energy consumption. These challenges have drawn attention to the need to implement urbanization with sustainable energy consumption globally. The present study aims to identify the urbanizing factors that cause energy consumption in the SAARC countries. The South Asian Association for Regional Cooperation is considered in the study during the period of 1975-2014. The data are analyzed by using simple statistics and econometric techniques, such as the ordinary least squares (OLS) method for the country level. The study has found that all urbanizing variables significantly affect energy consumption with different levels in different countries, as shown by the OLS method. The coefficient of GDP is statistically significant at 1% level of significance for Bangladesh, Pakistan and Sri Lanka, while at 5% and 10% levels for India and Nepal, respectively. The coefficient of the industrial sector share in GDP is statistically significant at 1% level of significance for Bangladesh, Nepal and Pakistan. The result shows that a 1% increase in the service sector’s share in GDP leads to a reduction in energy consumption of 0.15%, 0.34% and 1.61%, respectively, in Bangladesh, Nepal and Sri Lanka. The result for urban population indicates that a 1% increase in urban population leads to an increase in energy consumption by 1.94%, 2.32%, 0.85% and 3.87%, respectively, for Bangladesh. India, Nepal and Sri Lanka. Green technology and energy efficiency technologies to use in the industries, encourage using public transportation, sustainable energy and urbanization are potential policy recommendations.
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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 | 0.001 | 0.005 |
| 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".