Employing the Panel Quantile Regression Approach to Examine the Role of Natural Resources in Achieving Environmental Sustainability: Does Globalization Create Some Difference?
Bibliographic record
Abstract
In the modern era of globalization, natural resources have become an important factor in shaping a sustainable future; however, the evidence on the role of globalization in reducing the adverse environmental impacts of natural resources is relatively scarce. The current study explores the dynamic interaction between energy consumption, economic development proxied through the human development index, population, natural resources, globalization, and ecological footprint under the core idea of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT). This research applies panel data for the period from 1999 to 2018 in nine countries with the highest oil production (Brazil, Canada, China, Iran, Kuwait, Russia, Saudi Arabia, United Arab Emirates, and the United States). The results of this study are based on the panel Method of Moments Quantile Regression (MMQR). Empirical findings foundthat economic development, energy consumption, population, and natural resources contribute to increased environmental degradation, while globalization seems the main source of environmental sustainability. Concerning the indirect impacts of globalization, expanded interaction and integration among oil-producing countries helped to inhibit ecological footprint; nevertheless, natural resources complicate the design of a sustainable future by promoting environmental degradation. Additionally, a bidirectional causality relation was discovered between population, energy consumption, globalization, and ecological footprint; however, the panel Dumitrescu and Hurlin causality test results revealed a unidirectional causality association from economic development to ecological footprint and from natural resources to ecological footprint. Our findings shed new light on the criticality of globalization in achieving environmental sustainability by providing cleaner practices that will prevent rent-seeking.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".