ICT and energy consumption in Sub-Saharan Africa: Effects and transmission channels
Bibliographic record
Abstract
This paper contributes to the literature on the relationship between information and communication technologies (ICTs) and energy consumption. Despite increasing attention on the subject, existing studies have not yet investigated the channels through which ICTs affect energy demand. We use a stochastic impact model extended to the population, wealth and technology regression model to estimate both the effect and transmission of ICTs on energy demand in 24 sub-Saharan African countries from 1995 to 2018. Empirical results show that ICT use, measured by mobile and fixed-line telephone penetration significantly reduces energy consumption. In addition, the mediation analysis reveals that ICTs not only have a direct negative effect on energy consumption but also an indirect negative effect through its impact on GDP per capita and industrial sector development and a mixed indirect effect through financial development. However, the total effect is negative and indicates that ICTs are reducing energy consumption in sub-Saharan Africa. To accentuate the negative effects of ICTs on energy consumption, Governments should design policies to improve access to credit for the private sector, reduce income inequalities among populations, promote the use of industrial development and provide financial incentives for the development of green technologies.
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 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 | 0.000 | 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 | 0.000 |
| 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".