Opportunities and challenges in developing markets for energy service companies (ESCOs) to promote energy efficiency programs in Indonesia”
Bibliographic record
Abstract
It is predicted that between 2004 and 2030, around 80% of the world’s economic growth will be contributed by non-OECD countries (Ellis 2009). Energy Service Companies (ESCOs), could play a vital role in improving energy efficiency in these countries. ESCOs can help energy users, companies, industries, and commercial sectors in improving the efficiency of equipment by providing energy service (energy performance and/or credit risk). ESCOs were implemented quite successfully for promoting energy efficiency (EE) in many European Union (EU) countries and other developed countries such as the USA, Canada, and Japan. However, not many developing countries run ESCO successfully. This raised the question of potential barriers for using ESCO for EE programs in developing countries. To successfully implement and operate ESCO in developing countries like Indonesia, it is crucial to study and understand the opportunities and challenges encountered in running this program. \n \nThe aims/objectives of this study are to understand and verify the strengths, weaknesses, opportunities and threats of developing markets for Energy Service Companies (ESCOs) in Indonesia. This will be achieved by surveying the key stakeholders in the industry and identifying significant factors such as regulatory, financial, and awareness aspects for decision makers (governments). The results from this research will aim to provide recommendations for the decision makers, who can then review the significant factors that influence the development of ESCO in Indonesia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".