Nature dependent tourism - Combining big data and local knowledge

Area of Science:

• Environmental Economics
• Tourism Management
• Marine Spatial Planning

Background:

• Quantifying nature's value for tourism is crucial for sustainable development, particularly in the Eastern Caribbean's Blue Economy initiatives.
• Understanding tourist preferences through user-generated content (UGC) and AI/ML is an emerging field.
• Small island nations face unique challenges in valuing and managing nature-dependent tourism.

Purpose of the Study:

• To model and map nature-dependent tourism sectors across five Eastern Caribbean island nations.
• To develop a practical and scalable approach for measuring tourism's nature-dependency.
• To provide data supporting sustainable resource management and Blue Economy policies.

Main Methods:

• Utilized extensive user-generated content (UGC) to analyze tourist activities and preferences.
• Applied artificial intelligence (AI) and machine learning (ML) approaches to model tourism data.
• Incorporated local expert input for data correction and refinement.

Main Results:

• Developed detailed models and maps of nature-dependent tourism sectors.
• Generated statistics on the nature-dependency of tourism across the studied nations.
• Demonstrated a scalable methodology for quantifying tourism's environmental value.

Conclusions:

• The developed approach is practical and scalable for assessing nature-dependent tourism.
• The findings provide valuable data for sustainable resource management and marine spatial planning.
• This work supports the promotion and implementation of the Blue Economy framework in small island developing states.