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In conventional discourses on sustainability, the relationship between economics and ecology is central. A number of nations' economies benefited from responsible tourism following these conferences. By supporting local businesses and attractions, ‘green’ tourism helps communities achieve their natural and cultural objectives while also preserving their limited resources. In terms of sustainable travel, Kerala was an early leader. This study looks at RT initiatives in various stages, with an emphasis on green tourism's sustainable responsible travel practises. The green economic development bottom line method was used for this descriptive research. These results highlight the difficulties inherent with RT implementation. Our review of secondary data shows that the first rollout of RT was unsuccessful, but that subsequent stages showed great promise. In order to create sustainable tourism on a worldwide scale, the study also highlights the necessity for more research in other culturally distant places.

The aim of this chapter is to develop a strong research base for the academia and the industry to understand the importance of data analytics in International Trade. This chapter focuses on the case of cotton trade from India and explores different methodologies developed by the World Bank and International Trade Center to analyze the Big Data available on export and import. Through Big Data analysis, this chapter attempts to find out the export performance, market demand, export potential, and attractive markets for Indian cotton. This chapter also explores the trade competitiveness of Indian cotton over the years. The data through appropriate analysis can address some simple yet complicated questions in trade like what export potential the commodity holds, if the commodity is competitive or not in international market, what are new markets to look up to, and other similar questions. In other words, this information could make huge difference in decision-making of traders and policymakers directly, and farmers indirectly.