Green growth pathway through green innovation and human capital under low and high regime: From the perspective of energy intensity
Bibliographic record
Abstract
Green growth has recently become an interesting field of research, as the pairing of economic growth and environmental preservation is seen as an urgent need. Despite this importance, few studies have investigated the underlying factor of green growth (GG), especially those relating to green innovation (GI) and human capital (HCI) as catalysts of energy intensity (IE). The current study aims to investigate the repercussions of human facets and the green patents on energy intensity-driven green growth. We use the panel threshold regression (PTR) supported by the Exponential Panel Smooth Regression (EPSR) method spanning the period 1997-2019 to the case of 16 countries which include most and least eco-friendly countries. Our findings disclosed that below a threshold value of the human capabilities, green technological innovation remains without negative effects on EI. Our results also revealed that only the group of most eco-friendly countries (MEFC) is those which benefit from green innovation by moving from a low to high regime of human capital index. The group of least eco-friendly countries (LEFC) cannot benefit from green innovation to foster GG even by translating from low regime to high regime. In addition, human capital exerts an adverse effect on EI in the case of low regime; and therefore, for a low threshold value of the HCI. The outcomes of the present study can clarify the need to implement future action plans in terms of arbitration between the environmental quality in its different forms and savings in terms of energy consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".