Time-Frequency Nexus between Tourism Development, Economic Growth, Energy Consumption, and Ecological Footprint in Singapore
Bibliographic record
Abstract
Singapore has been listed as one of the top-visited countries and has the highest ecological deficit. Despite the abundance of previous studies, the distinction between short, medium, and long term by decomposing tourism development, economic growth, energy consumption, and ecological footprint has been largely ignored. This study aims to investigate the lead-lag nexus structures between ecological footprint and Singapore's economic activities from 1978 to 2016. By adopting the wavelet analysis and scale-by-scale Granger causality test, the outcomes show that energy consumption positively impacts ecological footprint at high frequencies, while tourism and economic growth positively drive ecological footprint at high and medium frequencies. We also find that the positive impact of macroeconomic variables on ecological footprint has not been evident since 2003. Additionally, the wavelet-based Granger test confirms a bi-directional causal between economic growth and ecological footprint at all frequencies, whilst there is a bi-directional relationship between tourism, energy consumption, and ecological footprint at high frequency. Based on these findings, the research may further strengthen the belief of Singapore’s policy-makers on the promotion of tourism and suggests some helpful lessons for emerging countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".