Revisiting the Dynamics of Tourism, Economic Growth, and Environmental Pollutants in the Emerging Economies—Sustainable Tourism Policy Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tourism contributions to economic growth and well-being have been widely acknowledged; however, its impacts on the environment demand an integrated approach to policy improvement across institutions in the emerging economies for the development of sustainable tourism practices. This study investigates the causal relationship between tourism, economic growth (GDP, capital investment), energy consumption, and environmental pollutants in developing economies, explicitly focusing on the case of Pakistan. Various econometric procedures and techniques were applied to test the proposed hypotheses. The findings suggest that economic growth support tourism development. Tourists’ arrivals have a significant positive impact on energy consumption, capital investment, and CO2 emissions; besides, environmental pollutant (CO2) causes negative effects on tourism. The results suggest that a 1 unit increase in tourism increases CO2 emissions metric tons per capita by 0.26 units in the long-run. A 1 unit increase in capital investment increases CO2 emissions metric tons per capita by 0.21 units, and a 1 unit increase in energy consumption increases CO2 emissions metric tons per capita by 0.51 units in the long-run. In the short-run, a 1 unit increase in tourism, capital investment, and energy consumption rises CO2 emissions metric tons per capita by 0.045, 0.04, and 0.08 units, respectively. Sustainable tourism remains a sole option in developing economies to enhance the competitiveness of tourism as a tool for friendly developments. Thus, tourism policies are needed to be integrated with overall economic, environmental, and energy policies to encourage the shift towards sustainable tourism development to minimize environmental pollution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it