A path towards environmental sustainability: exploring the effects of technological innovation and investment freedom on load capacity factor
Bibliographic record
Abstract
This study examines the impact of investment freedom, technological innovation, renewable energy, and economic growth on load capacity factor (LCF) within the context of Sustainable Development Goals (SDGs) 7 and 13 for a group of high investment freedom countries (Luxembourg, Germany, Austria, Australia, Canada, Denmark, Netherlands, United Kingdom, Chile, Singapore, New Zealand, United States of America, Belgium, Finland, Uruguay, Latvia, Spain, Sweden, and Switzerland). Furthermore, this research assesses the impacts of achieving carbon neutrality by 2030. The data set covers the years between 1995 and 2019. Moreover, the validity of the load capacity curve (LCC) hypothesis is analyzed in all countries. Long-run coefficients are estimated using the Regularized Common Correlated Effects (rCCE) estimator, and the robustness analysis is performed using Common Correlated Effects (CCE) estimators. The overall assessment of the panel reveals that the LCC hypothesis is invalid in the selected nations, with the exception of Belgium. Other findings indicate that investment freedom reduces the LCF for New Zealand. However, investment freedom increases LCF, improving environmental quality in Latvia. Technological innovation decreases LCF for Singapore and increases it for Germany. Renewable energy increases LCF for the UK and Spain. Finally, policy implications for improving environmental quality are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".