Investigating the Factors Influencing Energy Intensity in the South African Manufacturing Industry
Bibliographic record
Abstract
The study investigates the determinants of energy intensity in the South African manufacturing industry. The objectives are to analyse trends, to determine the drivers of manufacturing energy intensity and make policy recommendations. The study investigates the effects of manufacturing value added, foreign direct investments, energy prices and trade openness on manufacturing energy intensity. The study employs the Vector Error Correction Model on time series data for the period of 1980 to 2017. The findings of the study depict that manufacturing value added, foreign direct investment and energy prices are the most important determinants in explaining manufacturing energy intensity over the reviewed period. Manufacturing value added is found to be statistically significant both in the short and long run. Foreign direct investment is found to be statistically significant in the long run whereas, energy price is significant in the short run. In light of this, the study makes policy recommendations. With regards to total manufacturing value added, the study recommends that the industry be closely monitored. Government should subsidize energy efficient machinery and equipment and the use of old outdated technology should be banned. With regards to foreign direct investment, the study recommends that the FDI policy be reviewed such that it attracts foreign investors. The recommendation regarding energy prices is that government should encourage energy price reform and use subsidies to encourage energy saving enterprises.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".