Analysis of the Effects of Economic Growth and Development on Inequality and the Environment
Bibliographic record
Abstract
Economic growth is often only experienced by certain actors or households, causing income inequality.Economic growth is also synonymous with CO2 emissions.Economic growth will certainly increase CO2 emissions caused by increasing demand for energy consumption, which will cause environmental damage.The inverted Kuznets U curve is a theory that can be used to describe and explain economic development and growth.The research period is the last 12 years, namely 2010-2021.The research findings show that there is no inverted Kuznets U curve for the problem of inequality, which negates the effect of increasing per capita GRDP and economic growth on reducing inequality and poverty in Jambi Province.Second, the Mining and Quarrying Industry remains a sector that makes a dominant contribution to GRDP and is also the main driver of economic growth in Jambi Province, according to the results of Location Quotient and Shifts Share.Third, it is shown that environmental problems cause the Kuznets U curve to be inverted.According to data supporting the Environmental Kuznets Curve (EKC) hypothesis in Jambi Province, environmental pollution will eventually decrease due to economic expansion.The results of this study can be used as a basis for consideration in measuring the performance and quality of the economy of Jambi Province.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".