The Relationship Between Renewable Energy Consumption, Carbon Dioxide Emissions, Economic Growth, and Foreign Direct Investment: Evidence from Developed European Countries
Bibliographic record
Abstract
The objective of this study is to conduct an empirical examination of how renewable energy consumption, carbon dioxide emissions, growth in GDP per capita, and foreign direct investment are interrelated.The empirical investigation is based on panel data spanning 18 years, gathered from nine developed European nations: Germany, the United Kingdom, France, Italy, Spain, the Netherlands Switzerland, Turkey, and Poland, during the period from 2002 through 2019, encompasses significant macroeconomic factors that act as benchmarks for assessing a socioeconomic advancement.Additionally, in light of the drawbacks posed by Carbon dioxide (CO2) emissions, which are a significant hazard to all countries, there appears to be a growing potential for developing preventive measures.Regarding the methodology, various models are examined using panel regression econometric methods.This study utilizes Pooled Ordinary Least Squares (OLS), Pooled Ordinary Least Squares Robust (OLSR), Fixed Effects Method (FEM), and Random Effects Method (REM).A correlation matrix is used to ascertain the interrelationships among the variables under study.Additionally, the findings from the Hausman Test suggest that the Random Effects Method emerges as the most fitting technique for this investigation.Moreover, the regression outcomes obtained through the random effect approach indicate that renewable energy consumption notably decreases CO2 emissions in developed European nations, highlighting its critical role as a strategy for sustainability.While GDP per capita has a negative impact on CO2 emissions and does not indicate a level of significance.Further, Foreign Direct Investment has a positive impact on CO2 emissions although, does not indicate a level of significance.This paper's major goal is to advance our knowledge of this relationship and arrive at fresh insights that could be very useful to policymakers.The study's secondary goal is to bridge the existing literature gap for the specified period and selected countries, with a focus on distinct macroeconomic variables.
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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 | 0.001 | 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 | 0.001 |
| 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".