Sustain Renewable Energy – Lessons for Bangladesh from an Interprofessional Study Conducted in West Michigan, USA
Bibliographic record
Abstract
Objective: Between 1999–2018, the Global Climate Risk Index placed Bangladesh in the top 10 countries most affected from extreme weather events associated with climate change. Implementation of alternative energy may minimize climate change in vulnerable countries. Our objectives were to characterize public knowledge and perceptions of costs and benefits of renewable energy in west Michigan, USA, and recommend areas in which policy discussions on renewable energy should focus. Method: Via email and postal service, we distributed a survey to 1,000 randomly-selected university employees, and 1,000 residents of primarily Ottawa and Kent counties in west Michigan (Grand Valley State University Institutional Review Board #20-118-H). Result: A total of 313 respondents completed the survey, including 170 university employees and 122 county residents. Results suggest that 12.5% of people older than age 60, and people with no college degree use alternative energy sources more than other age classes or those with higher education. Females (p = 0.0636) and people who have lived in their homes for 10–15 years (p = 0.0802) perceived renewable energy as less costly than other sources. Although females perceived less knowledge than males (p = 0.0001), there were no differences in perceived knowledge level among respondents of various ages, education levels, careers, salary, or whether they owned a home. Respondents aged 40–49 and 60–69 also perceived lower pollution from renewable energy than other age groups (p = 0.0393 and p = 0.0779, respectively). Conclusion: With a broader, more diverse population in future work, we anticipate more variability in responses, but similar trends. The prospect of implementing renewal energy is positive and suggests that policy makers should supply incentives, promote education, and invest resources for effective implementation. The Bangladesh government can support studies to understand peoples’ perception of alternative energy sources and explore socially suitable interventions to address climate change.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".