Assessing Sustainable Development Through Wavelet‐Quantile Based Analysis: Comparative Insights From Four Developed Countries
Bibliographic record
Abstract
ABSTRACT Balancing economic growth with environmental sustainability remains a key challenge for developed economies. The load capacity factor (LCF), as a ratio of biocapacity to ecological footprint, provides an integrated measure of this balance. Yet, little is known about how gross domestic product (GDP), labor productivity, and green technologies interact with LCF over time. The present study employs wavelet coherence analysis (WCA) and wavelet quantile regression (WQR) to evaluate the impact of country characteristics such as GDP, population, patents on environmental technologies, renewable energy usage and labor productivity on the LCF in Australia, Canada, the United Kingdom (UK) and the United States of America (USA) during the period 1961–2019. The results suggest that (i) GDP generally affects the LCF negatively for countries; (ii) the population growth rate also has similar negative effects on the LCF; (iii) patents on environmental technologies affect the LCF positively as expected; (iv) finally, renewable energy usage and labor productivity's impact varies—beneficial in the UK, but detrimental in Australia, Canada, and the USA. However, in terms of WCA results, a positive correlation between renewable energy usage and LCF in Australia, Canada, and the USA was detected. These results focus attention on green innovation and renewable energy development, promoting labor productivity in accordance with the unique characteristics of countries. This comparative analysis addresses the temporal and spatial variability of sustainability drivers and provides recommendations for policymakers on balancing economic growth, green technologies, and sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".