Exploring the Role of Land Utilization, Renewable Energy, and <scp>ICT</scp> to Counter the Environmental Emission: A Panel Study of Selected <scp>G20</scp> and <scp>OECD</scp> Countries
Bibliographic record
Abstract
ABSTRACT Understanding the complex connections between land utilization, economic activity, technological development, and carbon emissions will be essential as the world struggles to address climate change and the resulting environmental problems. For empirical analysis, we have used pooled mean group (PMG) and Method of Moment Quantile Regression (MMQR) to precisely capture the details of these relationships across various quantiles. The study uses a balanced panel dataset from 1980 to 2019 that includes 10 emerging nations which are common in G20 and Organization for Economic Cooperation and Development (OECD), including Australia, Canada, France, Germany, Italy, Japan, the United Kingdom, the United States, and China. The study discovers an environmental Kuznets curve with an inverted U form, highlighting the complex link between economic development and environmental degradation in emerging countries. The study also clarifies how internet use, foreign direct investment, and renewable energy (REN) affect environmental consequences at different quantiles. Moreover, the findings confirm the adverse impact of carbon emission, FDI, and REN on land degradation. The findings have implications for sustainable development policy, highlighting the necessity of customized approaches for the distinct contexts and levels of development of every nation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".