Electricity Consumption, Exchange-rebate Facility, and Banning of Imported Second-hand Refrigerators: Reviewing an On-going Process
Bibliographic record
Abstract
Second-hand and old refrigerators are relatively not energy-efficient.They also use refrigerants such as chloroflourocarbons and hydroflourochlourocarbons.They therefore cause climate change and ozone-layer depletion.Among these and other reasons was the implementation of the Montreal protocol in 1987.The Multilateral Fund for the Implementation of the Montreal Protocol was therefore established to assist needy nations to facilitate the implementation of the protocol.Ghana therefore qualified for the fund and thereby in March 2013 introduced a rebate programme on refrigerators.Households were invited at will to submit old refrigerators for new ones at subsidized prices.As an on-going process, however, it appears the patronage of the facility is not encouraging.The purpose of the study being qualitative and quantitative was therefore to ascertain the response of the public to the exchange-rebate facility in relation to electricity cost and banning of secondhand refrigerators.Cape Coast Polytechnic in Ghana was used as a case study.One-hundred-and-twenty questionnaire were administered at a response rate of 95 percent.Interviews were also conducted among seven suppliers and retailers.Stratified and systematic random sampling methods were employed.SPSS Version 17 was used to analyze data.Cross-tabulations and chi-square tests were also utilized in analyzing the data.It was found that monthly electricity cost per household ranged between 8.00 ($2.49) and GH¢155.00($35.74).Only 11% of those using imported second-hand refrigerators had patronized the facility.It is recommended that CEPS and the Energy Commission should intensify their monitoring activities at harbors, national borders and retail shops that still sell imported second-hand refrigerators in the country.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".