Eco-tourism and Sustainable Development: A Study on Linkages and Environmental Conservation
Bibliographic record
Abstract
Eco-tourism, a niche within the broader spectrum of tourism, has gained significant attention as a catalyst for achieving sustainable development goals. This abstract delves into the symbiotic relationship between eco-tourism and sustainable development, highlighting their collective potential to foster environmental conservation and socioeconomic prosperity. Eco-tourism is characterized by its commitment to minimizing negative impacts on natural ecosystems while providing immersive and educative experiences for tourists. The primary focus is on promoting responsible travel that supports local communities, protects biodiversity, and preserves cultural heritage. By adopting low-impact infrastructure development, environmental education, and community engagement practices, eco-tourism seeks to balance visitor enjoyment and long-term ecological preservation. On the other hand, sustainable development entails pursuing economic growth, social equity, and environmental integrity without compromising the needs of future generations. Eco-tourism aligns seamlessly with this ethos by leveraging tourism for positive change. The revenue generated from eco-tourism can contribute to local economies, incentivize conservation efforts, and bolster community development, thereby enhancing residents’ overall quality of life. The study emphasizes various types of eco-tourism sites in India, analyses the current scenario of the Indian tourism industry, and discusses multiple initiatives taken by the Government of India regarding eco-tourism. Data used in this study are of secondary in nature. The findings of the study reveal that the Ministry of Tourism and the Government of India have made multiple efforts and strategies in eco-tourism including eco-friendly recommendations, action plans for eco-tourism policy, and so on. However, eco-tourism promotional activities still need to be improved to attract tourists/visitors. Even climate change has significant impact on eco-tourism. These impacts are mainly due to changes in climatic conditions such as temperature, rainfall, other climatic factors, and the transformation of the natural environment, etc. The COVID-19 pandemic had significantly disturbed the tourism and hospitality sector worldwide. In 2008, there were financial crises but according to the World Trade Organization (WTO) the COVID-19 pandemic had a seven times higher impact on the tourism industry. The study highlights that tourism offers high-quality services which has a positive impact on social elements. Eco-tourism enjoys a significant advantage over general tourism concerning tourist inflows and socio-economic and environmental benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".