Government measures needed to promote building energy efficiency (BEE) in China
Bibliographic record
Abstract
Purpose The aim of this paper is to investigate the major differences between the government's role in building energy efficiency (BEE) in China and three developed countries, and to further the understanding of market expectations of the most effective government measures to encourage BEE development in China. Design/methodology/approach The approach taken was: establish a framework for a critical comparative study; compare and assess the BEE promotion measures available to governments in the USA, Canada, the UK and China; survey the BEE market expectations of building design professionals to better understand the favourable measures the Chinese Government could take to further promote BEE; and triangulate the findings of the comparative study and questionnaire survey to develop recommendations for BEE promotion in mainland China. Findings Economic incentives are important for BEE promotion at the current stage, but they are lacking in China. Active government interventions, such as adjusting energy pricing and implementing BEE legislation, are needed in China if BEE is to become economically viable and efficient. Research limitations/implications Owing to limited resources, the questionnaire survey did not reach every part of China. A further study should be carried out to extend the investigation to more areas of China and to obtain wider stakeholder coverage. Originality/value The originality of this paper lies in its development of a theoretical framework to further understanding of the government's role in BEE promotion and its use of first‐hand data collected from industry to verify market expectations of that role in China.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".