Sustainable Energy Use in Dar es Salaam: Current Trends, Future Scenarios, and Policy Options
Bibliographic record
Abstract
In 2019, Africa accounted for only 5% of global energy demand and 3.7% of energy-related carbon dioxide emissions. However, Africa’s rapid urbanization will contribute to rising energy use and emissions, both regionally and globally. Using the case of Dar es Salaam, Tanzania, this thesis offers new insights relevant to the discourse on Africa’s evolving energy landscape. The thesis: (1) Estimates possible changes in Dar es Salaam’s residential energy use and greenhouse gas (GHG) emissions between 2015 and 2050, (2) Identifies key household and transport-related drivers of energy use and GHG emissions, (3) Assesses variations in energy use at the sub-city (ward) level, i.e., between settlements of differing socio-economic profiles and spatial location in the city, and (4) Examines institutional and societal factors that may constrain low-carbon development in Dar es Salaam. Three studies are presented to address the four aforementioned thesis aims. The first study – Modelling Future Patterns of Urbanization, Residential Energy Use and Greenhouse Gas Emissions in Dar es Salaam with the Shared Socio-Economic Pathways – employs a scenario-framework to scope different urban growth and GHG emissions pathways in Dar es Salaam. The work demonstrates an approach for projecting GHG emissions in an Africa city context that may be data constrained. The second study – Does Location Matter? Investigating the Spatial and Socio-Economic Drivers of Residential Energy Use in Dar es Salaam – shows the differences and clustering of energy use that exist at the ward level, and employs statistical methods to correlate energy use with different socio-economic and spatial characteristics of wards. The final study – Assessing Institutional and Societal Barriers to Low-Carbon Development in Dar es Salaam – asserts that processes to implement low-carbon measures (e.g., electrification and public transport projects) would need to engage multiple stakeholders in a collaborative process to leverage the power and mandate of different institutions. Together, these studies seek to inform energy and urban planning policies in Dar es Salaam that (1) enhance synergies between GHG mitigation investments, (2) support implementation strategies that consciously account for local energy use realities and infrastructure access needs, and (3) acknowledge linkages between sustainability, climate change, and socio-economic development strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".