INSTITUTIONAL EFFECT ANALYSIS COMPARING ENERGY EFFICIENCY RETROFITTING FOR EXISTING RESIDENTIAL BUILDINGS PATTERNS IN CHINA
Bibliographic record
Abstract
Buildings in China are important contributors to the country’s energy consumption and CO2 emissions. Energy use in building in northern heating regions of China accounts for more than 40 % of secondary energy consumption in urban areas. The total building areas has increased 75 % over the past decade. Approximately 2.5 billion m2 (nearly 1/3) of existing residential buildings in Northern China are worth retrofitting. Efficiency Retrofitting for Existing Residential Buildings (EERFERB) in China is undergoing a fast development support by both the Chinese Central Government and international governments and institutions. These international and domestic projects have not only played a significant role in enhancing China’s building energy efficiency, but also significantly promoted the quality of housing conditions for low- and middle- income populations. It is recognized that projects with government cooperation among countries and central-to-local government projects have different implementation patterns. These differences provide valuable lessons for selecting efficient project delivering institutions. This research will develop criteria to compare international government-to-government projects and domestic central government projects. Two typical projects in Beijing and Tangshan will be evaluated from the perspectives of regulatory support, organizations, retrofitting effects, financing mode, management structure, incentive and technical support, and community sustainability improvement. Based on it, both the strengths and weaknesses of both retrofitting delivery patterns are presented. Finally, lessons and experiences are extracted and the implications for instruction and decision-making for the Chinese EERFERB policy design are identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".