The Evolutionary Trend of CO_2 Emissions and Its Spatial Differentiation in China: Based on R/S Method
Bibliographic record
Abstract
The paper calculated the Hurst index and fractal dimension for evolution of total CO2 emissions and emission intensity of the 30 provinces in mainland China during 1990-2008 by adopting R/S method.Then the evolutionary characteristic of total emission and emission intensity of CO2 at provincial level was analyzed.The paper classified four types of regions based on the spatial distribution pattern of Hurst index,and compared the variances of evolutionary characters for total emission and emission intensity among them.The result shows that 75.86% of the provinces in mainland China have the strong sustainable increasing trend in the evolution of total CO2 emission,and for the evolution of CO2 emission intensity,64% of the provinces have the strong sustainable decreasing trend.The sustainable decreasing trend of emission intensity in majority region is good news for the accomplishment of the target of cutting CO2 emissions per unit of GDP by 20-25% from 2005 levels by 2020.However,for the most of provinces,the durability of increasing of total emission is higher than the emission intensity decreasing;in addition,there still are some regions which have the weaker decreasing trend in evolution of emission intensity,even the trend of emission intensity decreasing in some regions showed anti-sustainability.All of these indicated that it is still difficult for China to cut CO2 emission further,especially to fulfill the target of decoupling the CO2 emission from economic growth cutting because China's economic development is still at the stage of high speed of industrialization and urbanization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".