Energy Transition Pathways for Zambia: A Modelling Approach Using OseMOSYS
Bibliographic record
Abstract
Energy Transition Pathways for Zambia: A Modelling Approach Using OseMOSYS Abstract Zambia's energy sector is heavily reliant on hydropower, which accounts for over 83% of its 3,800 MW installed capacity as of December 2023. This dependency, coupled with the impacts of climate change, has led to increasing energy insecurity, particularly during frequent droughts, which disrupt electricity supply. Additionally, the country faces significant challenges in extending electricity access, especially in rural areas where the average access rate is about 8%. In response, the Zambian government has set ambitious targets to diversify the energy mix to achieve 30% electricity production from variable renewable sources. To support Energy Transition Pathways for Zambia and sustainable development, study employed the Advanced Energy System Modelling Using OseMOSYS an Open-Source Energy Modelling System to explore three energy transition scenarios: Business-As-Usual (BAU), Government Renewable Energy Policy (REW), and Drought Impact Scenario (DIS). The scenarios assess the integration of renewable energy, technological advancements, and policy interventions up to 2070. The results highlight the critical need for diversification in Zambia's energy mix. The REW scenario, which emphasizes renewable energy integration, shows significant potential for reducing greenhouse gases from about 2000 ktCO2 to about 1600 ktCO2 and stabilizing investment costs. The DIS scenario underscores the importance of resilient strategies in mitigating the impacts of droughts on the hydropower-dependent power system. Overall, this study provides essential insights and policy recommendations for guiding Zambia's transition towards a more sustainable and resilient energy future.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".