A machine learning approach to classifying sustainability practices in hotel management

Advancing sustainable efforts in hotels, especially small and medium-sized enterprises (SMEs), is important for addressing environmental and social impacts and promoting long-term viability in the hospitality industry. This paper uses machine learning techniques to classify sustainable practices in SMEs. Analyzing data from Booking.com's "Travel Sustainable Level" section, which includes 30 sustainability measures across five categories (waste, water, energy and greenhouse gases, nature, and destination and community), this study examines 19 factors affecting hotel sustainability. Non-linear machine learning models identify significant features: performance, star rating, size, group membership, and coastal location. These findings are validated through a model introducing 'Levels of Commitment to Sustainability, "categorizing hotels based on their adherence to these measures". Variables were more distinctly differentiated by commitment levels than Booking.com levels. Post-hoc analysis and expert interviews reveal insights into the least adopted sustainability measures and their perceived costs and benefits. This study introduces a novel classification approach for hotel sustainability, providing essential insights for effective sustainable hotel management. It highlights key factors influencing sustainability and emphasizes the significance of these factors for developing targeted strategies. Furthermore, this research contributes to a broader understanding of sustainability in hospitality, demonstrating the applicability of machine learning in evaluating sustainable practices.