Analyzing Actors’ Engagement in Sustainable Energy Planning at the Local Level in Ghana: An Empirical Study
Bibliographic record
Abstract
Actors play a crucial role in sustainable energy development yet interaction in different contexts is an area that has not received much scholarly attention. Sustainable energy transitions theories such as the Multi-Level Perspective, for instance, have been criticized for not describing precisely the nature of the interactions between actors and institutions within socio-technical systems. The goal of this study was to empirically examine local actors’ engagement and its impact on the planning and implementation of sustainable energy initiatives in the villages and remote areas in Ghana. Using the mixed methodology approach, interviews were performed, focus discussion groups were held, and archival data were collected, and social network modeling and case study analysis was performed. Our findings showed that sustainable energy development at the local level depends on an interplay between local government agencies, Non-Governmental Organizations (NGOs), central government agencies, local communities, and private sector organizations. Despite being the focal point at the local level, local government involvement in sustainable energy planning is limited. In the case of Ghana, sustainable energy planning remains centralized and is manifested in a low level of awareness of local actors on national energy plans. The implication for decision makers is that energy planning functions should be devolved to the local government. Such devolution is expected to ensure the integration of sustainable energies into local government plans for the well-coordinated implementation and effective monitoring of sustainable energy projects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".