Cash, Credit, or Kin? Financing Pathways and the Uneven Sustainability of Off‐Grid Solar Electrification in Tanzania and Malawi
Bibliographic record
Abstract
ABSTRACT Solar home systems (SHSs) are celebrated as a technological antidote to sub–Saharan Africa's chronic electricity deficits, yet the financial and social mechanisms that move them from warehouse to household remain underexplored. Drawing on 157 interviews with SHS owners in Tanzania and Malawi, this study examines the financial pathways used to gain access to SHSs and how each pathway reconfigures sustainability outcomes aligned to some Sustainable Development Goals. We find that the credit model compresses the wait for electricity from months to days but embeds new vulnerabilities of remote disconnection and debt anxiety; cash‐only strategies safeguard dignity and post‐purchase reliability but delay electrification; and hybrid finance leverages social capital to widen access while collateralizing reputation. Conceptually, we coin the term “moral–monetary infrastructures” to foreground liquidity horizons, reputational sanctions, and remote shut‐off technologies as co‐producing elements of energy transitions. We extend this further by showing how social infrastructure underpins access and continuity. By reframing both finance and social relations as infrastructural, the study deepens socio‐technical transition theories and enriches energy justice frameworks with alternative temporal, psychosocial, and relational dimensions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".