Developing a conceptual partner selection framework for matching public–private partnerships of rural energy internet project using an integrated fuzzy AHP approach for rural revitalization in China
Bibliographic record
Abstract
The development of rural clean energy is the key to cope with the shortage of traditional energy supply in the rural revitalization strategy and improve the sustainability of rural energy supply. Under the background of digital age, the development and utilization of rural clean energy Internet has become the focus of rural economic development. The government partners of the Rural clean Energy Internet PPP project (RCEIPPPP) are the key to promoting the green and intelligent development of rural energy. In this paper, the index system of project partner selection is constructed, and the problem of government partner selection for RCEIPPPP is studied by AHP and fuzzy comprehensive evaluation. The results of this study are as follows: 1) Partners' financial ability, technical ability, management ability, performance experience, corporate reputation, cooperation ability and risk management are the influencing factors for government partner selection of rural clean energy Internet PPP projects (RCEIPPPPs); 2) Compared with other factors, financial ability, technical ability, management ability and performance experience are the four key factors that are more important in choosing partners; 3) The empirical research shows that AHP, fuzzy comprehensive evaluation and the index system constructed by this research can be applied to the practice of government partner selection for RCEIPPPPs. This study puts forward the evaluation system of government cooperation selection of energy Internet PPP projects from the theoretical level, improves the existing research methods, and makes the theoretical system in this field more complete. From the practical level, it provides scientific basis and suggestions for the government to make decisions on energy Internet PPP projects, and improves the engineering efficiency and quality of rural clean energy Internet construction. This study demonstrates the complexity of clean energy projects, the need for an integrated approach to decision-making, and the need for project managers to actively manage communication and collaboration with partners to ensure successful project implementation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".