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
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 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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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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