Research on the Relationship between Economic Growth, Environmental Governance Investment and Carbon Emission--Based on the Time Series Data from 2000 to 2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study on the relationship between investment in environmental governance, carbon emission and economic growth is helpful for the relevant government departments to coordinate the influence among them when formulating the policies of reducing emission and conserving energy, so as to take the comparative advantages of various factors and promote the benign interaction between economic development and environmental governance. In this paper, the data of Per capita GDP, per capita investment in environmental governance and per capita CARBON dioxide emissions in China from 2000 to 2019 are selected as the research basis, and variables are studied by means of Granger causality and impulse response function. As shown in the results, there is a single Granger relationship between investment in environmental governance and carbon emissions, that is, the increase of investment in environmental governance leads to the reduction of carbon emissions. The influence of economic growth on environmental governance investment is small, but in the long term, it can restrain the growth of carbon emissions. Investment in environmental governance can promote economic growth and stimulate a reduction in the emissions in the short term; Economic growth was hindered by the emissions in the long term and fail to stimulate increased investment in environmental governance. Based on these findings, this paper proposes policy Suggestions for optimizing the structure of environmental governance investment, improving the carbon emission monitoring and response mechanism, and strengthening the technological level of energy conservation and emission reduction.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it