Bibliographic record
Abstract
With the development of human economy the problem of environmental protection has become increasingly serious, which is related to the sustainability of development. Although GDP can effectively reflect the development level of a country, it can not measure the utilization of natural resources. Therefore, it is necessary to formulate a new calculation standard to combine economic development with environmental protection, and the model of GGDP was born. First of all, we choose a model developed according to The System Of National Accounts (SNA) and including four dimensions from the existing GGDP calculation models. There are 8 first-class indicators in the four dimensions, which are calculated by 19 factors. After the values of the four dimensions are calculated separately, we can get the GGDP values of a series of countries. Secondly, we choose the GGDP of 5 countries from 2000 to 2014 as independent variables, and the global carbon dioxide emissions as dependent variables, and establish a stepwise regression model. We find that the GGDP of China and Canada is positively correlated with global carbon dioxide emissions, while that of other countries is negatively correlated. Then, through the intuitive comparison between GGDP and GDP in countries, combined with the correlation degree between in stepwise regression model, we get the feasibility of replacing GDP with GGDP. Through systematic cluster analysis, the GGDP of 29 countries is divided into three categories, and the rank is different from the world ranking of GDP, can measure the advantage of environmental protection. By analyzing the calculation method of GGDP and the model, we can find the disadvantage of GGDP. After that, we select Japan to further analyze the results of using GGDP instead of GDP. Among the 29 countries, Japan ranks first in GGDP. Through the GGDP calculation, except GDP, NRD has the greatest influence on the reduction of GGDP. Therefore, Japan can improve by adjusting the secondary indicators under NRD. In recent years, the achievement of Japan's sustainable development goals is gradually declining, so it is beneficial to implement GGDP. Finally, according to the current economic and ecological situation of Japan, combined with the calculation method and model of GGDP selected in this paper, we prepared a convincing report to support the implementation of GGDP in Japan.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".