Impacts of Urbanization on Energy Consumption in the South Asian Association for Regional Cooperation Zone
Bibliographic record
Abstract
Energy resources play a vital role in the process of urbanization, and the high level of energy consumption has significantly created an alarming situation for environmental degradation. Increased demand for energy consumption in the South Asian Association for Regional Cooperation (SAARC) zone is a core concern for decreasing the existing reserves of energy, especially nonrenewable energy, when the growth of urbanization is increasing also. This study investigates the impacts of urbanization on energy consumption in this region by identifying factors that influence energy use. We employed globally used econometric techniques to examine the relationship between energy use and urbanization. The results of the study indicate that all the independent variables used in the model (except urban population growth) were statistically significant with a 99% level of confidence. In addition, the findings of this study recognized three long-run causalities running from the GDP (gross domestic product) to energy consumption, energy consumption to GDP, and energy consumption to the industry’s share of the countries’ GDP. We recommend (i) taking the initiative to invest in renewable energy, (ii) implementing green energy-efficient technologies in the industrial sector, and (iii) raising public awareness of the negative effects of energy use on the environment through education.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".