Effects of Energy Efficiency on Firm Productivity in Kenya’s Manufacturing Sector
Bibliographic record
Abstract
There is concern about probable energy efficiency and economic performance trade-off, particularly in developing countries which often require more energy consumption to spur their economies. This study assesses the relation between energy efficiency and total factor productivity in Kenya’s manufacturing sector by applying a sample of firms in the World Bank Enterprise Survey. Energy intensity is used as a proxy for energy efficiency while total factor productivity is estimated using the Levinsohn-Petrin Algorithm. A dynamic panel data model is applied in the analysis of the energy efficiency and total factor productivity relationship which is at the sub-sector and firm size levels. The sub-sectors of concern are: chemicals, pharmaceuticals and plastics, food, textile and garments and paper and other manufacturing sub-sectors. Firm sizes of interest are: small, medium and large. The findings show heterogeneity in energy intensity across sub-sectors. Total factor productivity is also found to be heterogeneous across sub-sectors and firms of different sizes. The estimates show that in general, energy efficiency significantly promotes total factor productivity. Other factors that promote total factor productivity include capital intensity, age, size, top manager’s years of experience, foreign ownership and exporting status. However, the effect of these variables varies across the sub-sectors and firm sizes. The study findings suggest that policies to improve energy efficiency should be accorded additional emphasis jointly with improvements in total factor productivity.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".