The effects of tourism, energy consumption, and technological innovation on environmental quality amid geopolitical risk in Canada
Bibliographic record
Abstract
Tourism significantly impacts environmental performance through activities like transportation and accommodation, both of which contribute to carbon emissions. Geopolitical risks, such as the Russia-Ukraine conflict, further exacerbate these challenges by disrupting energy markets, increasing reliance on alternative energy sources, and driving higher fossil fuel emissions. This study examines the effects of tourism, energy consumption, economic growth, technological innovation, geopolitical risks, and economic policy uncertainty on carbon emissions in Canada by using annual time series data for the period 1990–2022. Employing the advanced Autoregressive Distributed Lag (ARDL) model, this study finds that tourism, energy consumption, economic growth, geopolitical risks, and economic policy uncertainty significantly contribute to rising carbon emissions in the long run. In contrast, technological innovation enhances environmental sustainability by mitigating emissions. From the Toda-Yamamoto predictive causality test, it is revealed that energy consumption, economic growth, technology innovation, and economic policy uncertainty show a bidirectional causal link with carbon emissions, while tourism and geopolitical risks exhibit a unidirectional relationship. Based on the findings of this study, Canada should prioritize investment in green technology innovation and implement policies to mitigate the environmental impact of tourism and energy consumption.
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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 | 0.000 | 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 | 0.000 |
| 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".