Spatial Variation and Distribution of Urban Energy Consumptions from Cities in China
Bibliographic record
Abstract
With support of GIS tools and Theil index, the spatial variance of urban energy consumption in China was discussed in this paper through the parallel comparison and quantitative analysis of the 30 provincial capital cities of mainland China in 2005, in terms of scale, efficiency and structure. The indicators associated with urban energy consumption show large spatial variance across regions, possibly due to diversities of geographic features, economic development levels and local energy source availability in China. In absolute terms, cities with the highest total energy consumption are mostly distributed in economic-developed regions as Beijing-Tianjin-Tangshan Area, Yangtze River Delta and Pearl River Delta of China, however, the per capita urban energy use is significantly higher in the Mid-and-Western regions. With regard to the energy mix, coal still plays the dominant role and cities in Mid-and-Western regions rely more on coal. In contrast, high quality energy carrier as electricity and oils are more used in southeast coastal zone and northern developed areas. The energy intensive cities are mainly located in the northwest, while the cities with higher efficiency are in southeast areas. The large spatial variance of urban energy consumption was also verified by the Theil indices. Considering the Chinese economy-zones of East, Middle and West, the within-group inequalities are the main factor contributing to overall difference, e.g., the Theil index for per capita energy consumption of within-group is 0.18, much higher than that of between group (0.07), and the same applies to other indicators. In light of the spatial variance of urban energy consumptions in China, therefore, regionalized and type-based management of urban energy systems is badly needed to effectively address the ongoing energy strategies and targets.
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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 | 0.000 | 0.000 |
| 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".