Sustainability and renewable energy challenges and strategies in Beijing, China
Bibliographic record
Abstract
This study aims to provide a better understanding of renewable energy use and its potential in Beijing, by utilizing qualitative content analysis to collect secondary data and coding process to analyze data regarding renewable energy opportunities, challenges, and strategies in Beijing. The results found that Beijing lacks the natural resources of wind and water which affect the development of wind farms and hydro power. Yet, Beijing is one of the major producers for solar water heaters as Beijing contains relatively abundant solar resources. As Beijing is the capital of China, it has enough economic and political supports to vigorously develop the technology of various renewable energy. The most important role of developing renewable energy in Beijing is to lead other Chinese cities to use renewable energy generated energy instead of fossil fuels such as coal, to control the air pollution and mitigate the climate change, by reducing carbon emissions. Thus, this study suggests that Beijing should increase cooperation with other regions which have abundant renewable energy to remedy its deficiency. This research could make positively contribution to understand how renewable energy development increase sustainability and solve environmental issues of energy uses by focusing on the city of Beijing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".