Intrinsic Role of Green Technologies and Renewable Energy: A Pathway to Mitigate Climate Change in China
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
ABSTRACT The present study provides the nexus between green technology, renewable energy systems and sustainable economic growth in China by assessing comprehensive time series data from 1990 to 2021. It employs quantile‐on‐quantile regression and the Augmented Dickey–Fuller test to assess how policies promoting green technology and renewable energy systems impact China's sustainable economic trajectory towards building a low‐carbon economy for climate change mitigation. The results reveal that all variables become stationary at first difference, except for green technology. There is a positive correlation between green technology, renewable energy systems and sustainable economic growth. Notably, an increase in the rate of green technology and resource management efficiency tends to increase economic growth, emphasising their transformative potential for fostering sustainability. In contrast, the increase in interest rates hinders economic growth. Furthermore, inflation and gross capital formation exhibit positive associations with sustainable economic growth. Policymakers should focus on transition to a low‐carbon economy through targeted resource allocation for low‐carbon technologies and the policies promoting energy efficiency, especially in the urban and industrial sectors. The current study also identifies limitations, like data constraints, methodological challenges and policy interaction complexity. By highlighting these limitations and further exploration can assist multi‐criteria decision making and policymakers to foster green, prospect and sustainable climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it