Table1_Comparing the effects of agricultural intensification on CO2 emissions and energy consumption in developing and developed countries.XLS
Bibliographic record
Abstract
<p>Energy consumption has become a requirement in the modern world, and without it, the economies of developing nations cannot prosper. Consistent economic growth is a challenge for countries of all economic levels, not just the less developed ones. We test the EKC hypothesis by analyzing the relationships between GDP growth, energy consumption, agricultural output, and the consequences of carbon dioxide (CO<sub>2</sub>) emissions. From 1991 to 2016, we used panel and quantile regression analysis to compare emissions in nine developing countries with those in 13 developed countries. There is the beginning of a reverse U-shaped relationship between agricultural energy use and greenhouse gas emissions. As a result, the verified EKC hypothesis paves the way for a watershed moment in the progress of industrialized nations’ economies. As an added bonus, agricultural results have a positive impact on CO<sub>2</sub> emissions from using liquid fuels. It has a negative impact on CO<sub>2</sub> emissions by 19.12% and causes a 4.802 percent increase in environmental degradation. Feed cropping, deforestation, biomass burning, and deep soil and cropping also have negative effects on the environment, especially in developing countries. There is a negative correlation between CO<sub>2</sub> emissions and economic growth in developing countries and their energy consumption. Although the EKC hypothesis for CO<sub>2</sub> emissions was rejected at lower quantiles, it was validated for Qatar, Canada, China, and other high-emitting economies according to the empirical estimation of quantile regression. The findings of this study have important policy implications for reducing carbon dioxide emissions, suggesting that policymakers account for the stage of economic growth currently being experienced when formulating measures to cut energy use and protect the environment. In particular, policies aimed at reducing energy consumption could.</p>
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".