Resource Dynamism of the Rwandan Economy: An Emergy Approach
Bibliographic record
Abstract
Africa is experiencing unprecedented economic growth that requires planners to understand the interactions between the social, economic, and ecological systems to ensure its sustainable development. The present paper uses the emergy method to analyse the Rwandan economy from 1975 to 2016. Emergy-based sustainability indicators were used to analyse and compare two distinct periods of economic growth: the pre- and post-Tutsi genocide periods. The results revealed that, by 2016, the total emergy use had increased by approximately 74% of the emergy recorded in 1975. The increase in total emergy use was associated with an increase in imports with contributions from 6.5 to 46.2% and the renewable resource contribution decrease from 93.5 to 53.8%. The emergy analysis, which covered 41 years, categorises Rwanda as a non-renewable resource-poor country. The total emergy use of the pre-genocide period was significantly lower than the post-genocide period. Based on the 2016 emergy self-support of 54% and the emergy sustainability index of 2.52, Rwanda has the highest import dependence compared to other developing countries listed in this paper and tends toward a developed country like Canada, Portugal, and so on. An imperative decision needs to be made in terms of the management of the economic system of Rwanda, as imports are becoming the highest impetus of the Rwandan economy but are also the top major cause of a long-run sustainability downfall. Thus, the present study recommends a scrutinised selection system of imports by increasing raw materials, particularly non-renewable resources, and by subsequently increasing the internal transformation to be exported. This recommendation is also applicable to other developing countries with similar non-renewable resource statuses.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".