Evaluating and Enhancing the Tourism Attraction of Phu Quoc Island: An Ecological Economic Approach Integrated with AI and Big Data Analytics
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This preprint studies how to evaluate and enhance tourism attraction for Phu Quoc Island, using a modified gravitational model grounded in ecological economics and integrated with AI and big data. It draws on biodiversity and environmental monitoring, estimates tourism-related carbon emissions, analyzes renewable energy adoption, and uses AI to perform sentiment analysis of social media and reviews, with tourism demand also modeled through distance/accessibility measures. The reported findings are that biodiversity contributes substantially to intrinsic attractiveness (20% increase), sustainability-conscious tourists are increasingly drawn to Phu Quoc (50,000-post sentiment analysis highlighting eco-friendly practices), renewable energy use is associated with a 15% emissions reduction, and positive sustainability discussions increase attraction force by 10%, while the paper is explicitly a preprint and not peer reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract Phu Quoc Island, Vietnam's largest island, is celebrated for its stunning natural landscapes and rich cultural heritage. This study introduces an innovative approach to evaluating the factors contributing to Phu Quoc's tourism attraction by combining a modified gravitational model with ecological economic principles, Artificial Intelligence (AI), and Big Data analytics. By incorporating real-time data on biodiversity, carbon emissions, renewable energy usage, and tourist behavior, the study provides a comprehensive framework for promoting sustainable tourism on the island. The findings offer actionable insights and a strategic roadmap for enhancing Phu Quoc's appeal as a green and sustainable tourist destination, ensuring long-term economic and environmental benefits.
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This study introduces an innovative approach to evaluating the factors contributing to Phu Quoc's tourism attraction by combining a modified gravitational model with ecological economic principles, Artificial Intelligence (AI), and Big Data analytics. By incorporating real-time data on biodiversity, carbon emissions, renewable energy usage, and tourist behavior, the study provides a comprehensive framework for promoting sustainable tourism on the island. The findings offer actionable insights and a strategic roadmap for enhancing Phu Quoc's appeal as a green and sustainable tourist destination, ensuring long-term economic and environmental benefits. Ecological Modeling Ecological Economic Phu Quoc Island Artificial Intelligence tourist behavior Big Data analytics 1. Introduction Tourism is a critical driver of economic growth for many regions worldwide, contributing significantly to GDP and employment (UNWTO, 2021 ). However, the rapid expansion of tourism, particularly in ecologically sensitive areas, has led to increased scrutiny over its sustainability (Gössling, 2002 ). Phu Quoc Island, known for its biodiversity and cultural heritage, exemplifies this tension between development and preservation. This study explores the dynamics of tourism attraction on Phu Quoc through an ecological economic lens, integrating cutting-edge AI and Big Data analytics to develop a sustainable tourism strategy that enhances the island's appeal while conserving its natural resources for future generations. 2. Theoretical Framework Tourism attraction is a multifaceted phenomenon influenced by several interrelated factors: Intrinsic Attractiveness (M1): This factor encompasses Phu Quoc's natural and cultural assets, such as its national parks, marine ecosystems, and historical sites. These elements are assessed using geospatial analysis, biodiversity data, and environmental monitoring, which are crucial in understanding their contribution to tourism (Buckley, 2012). Tourist Response (M2): This factor represents the preferences, attitudes, and behaviors of tourists, particularly concerning sustainable practices. AI-driven sentiment analysis of social media, reviews, and surveys provides deep insights into how tourists perceive and engage with Phu Quoc (Xiang et al., 2015). Distance (R): The physical, time, and cost distances between Phu Quoc and major tourist-generating regions. This factor is dynamically modeled using transportation data and predictive analytics to account for variations in accessibility and associated costs (Song et al., 2012). These factors are integrated into an enhanced gravitational model that incorporates ecological considerations: 3. Methodology This study employs a multi-disciplinary approach, combining traditional survey methods with advanced AI, Big Data analytics, and ecological economic modeling. The key data sources include: Biodiversity Assessments : Satellite imagery and environmental sensors are used to gather data on the island's flora and fauna, particularly in areas such as Phu Quoc National Park and marine protected zones (Balmford et al., 2002 ). Carbon Footprint Calculations : Data on carbon emissions from tourism-related activities, particularly transportation and accommodation, are collected and analyzed. For example, emissions from flights to Phu Quoc are estimated to contribute 250,000 tons of CO2 annually, highlighting the need for mitigation strategies (Becken & Patterson, 2006 ). Renewable Energy Adoption Rates : Information on the use of solar, wind, and other renewable energy sources in Phu Quoc's tourism sector is analyzed. Current estimates suggest that 30% of the island's energy comes from renewables, with plans to increase this to 50% by 2030 (IRENA, 2020 ). Tourist Behavior Analysis : AI techniques are used to analyze social media data, travel reviews, and surveys to understand tourist preferences. For instance, European tourists have shown a 40% preference for eco-friendly accommodations, reflecting a growing demand for sustainable tourism options (Dolnicar & Leisch, 2008 ). Ecological Factor (E) : Derived from environmental impact assessments, this factor quantifies the balance between tourism growth and ecological sustainability, focusing on minimizing negative impacts on biodiversity and natural resources (Daily, 1997 ). 4. Results The integration of ecological data with the modified gravitational model yielded several key findings: Biodiversity's Role in Attractiveness (M1) : Phu Quoc's rich biodiversity, including 1,200 plant species and over 100 coral species, significantly enhances its attractiveness. The island's intrinsic attractiveness score increased by 20% due to these natural assets (Myers et al., 2000 ). Tourist Response (M2) : AI-driven analysis revealed that sustainability-conscious tourists, particularly from Europe and North America, are increasingly drawn to Phu Quoc. Sentiment analysis of 50,000 social media posts indicated that eco-friendly practices and cultural experiences are the top factors influencing tourist decisions (Lacher & Oh, 2012 ). Carbon Footprint and Renewable Energy (E) : While Phu Quoc's carbon footprint remains a challenge, the island's commitment to renewable energy is a positive step. The current renewable energy usage has already reduced CO2 emissions by 15%, with further reductions expected as new projects come online (Cao & Zhang, 2019 ). Social Media Influence (D_s) : Positive online discussions about Phu Quoc's sustainability efforts have amplified its appeal, particularly among younger tourists. Social media influence increased the island's overall tourism attraction force by 10% (Gretzel et al., 2000 ). 5. Discussion The findings underscore the importance of integrating ecological economic principles into tourism management: Enhancing Renewable Energy : Accelerating the transition to renewable energy sources in the tourism sector can further reduce Phu Quoc's carbon footprint and enhance its appeal as a green destination (Gössling et al., 2009 ). Targeting Sustainability-Conscious Tourists : Marketing campaigns should focus on attracting tourists who value sustainability, leveraging social media to amplify these efforts (Sotiriadis, 2017 ). Balancing Development and Conservation : Policies should ensure that tourism growth does not compromise the island's ecological health. Implementing zoning regulations to protect sensitive areas and promoting eco-friendly tourism practices are crucial steps (Archer et al., 2005 ). Infrastructure Investment : Investments should prioritize sustainable infrastructure, such as energy-efficient transportation and green building practices, to support long-term growth (Fang et al., 2020 ). 6. Conclusion Phu Quoc has significant potential as a sustainable tourism destination, but achieving this requires a balanced approach that integrates ecological considerations with economic development. By leveraging AI, Big Data, and ecological economic models, Phu Quoc can implement a dynamic and responsive tourism strategy that ensures its attractiveness and sustainability in the long term. Future research should explore the use of augmented reality (AR) to enhance eco-tourism experiences and further engage sustainability-conscious tourists (Neuhofer et al., 2015 ). References Archer, B., Cooper, C., & Ruhanen, L. (2005). The positive and negative impacts of tourism. Global tourism , 79-102. Balmford, A., Bruner, A., Cooper, P., Costanza, R., Farber, S., Green, R. E., ... & Turner, R. K. (2002). Economic reasons for conserving wild nature. Science , 297(5583), 950-953. Becken, S., & Patterson, M. (2006). Measuring national carbon dioxide emissions from tourism as a key step towards achieving sustainable tourism. Journal of Sustainable Tourism , 14(4), 323-338. Buckley, R. (2012). Sustainable tourism: Research and reality. Annals of Tourism Research , 39(2), 528-546. Cao, X., & Zhang, Y. (2019). Renewable energy development in China: Recent developments and prospects. Energy Reports , 5, 1420-1425. Costanza, R., d'Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., ... & van den Belt, M. (1997). The value of the world's ecosystem services and natural capital. Nature , 387(6630), 253-260. Daily, G. C. (Ed.). (1997). Nature's services: Societal dependence on natural ecosystems . Island Press. Dolnicar, S., & Leisch, F. (2008). Selective marketing for environmentally sustainable tourism. Tourism Management , 29(4), 672-680. Fang, W. T., Hsu, M. L., Lin, C. Y., Yen, C. H., Huang, Y. W., & Ren, M. G. (2020). The assessment of tourism carrying capacity at a destination level: A case study of Sun Moon Lake, Taiwan. Sustainability , 12(3), 1003. Gretzel, U., Yuan, Y. L., & Fesenmaier, D. R. (2000). Preparing for the new economy: Advertising strategies and change in destination marketing organizations. Journal of Travel Research , 39(2), 146-156. Gössling, S. (2002). Global environmental consequences of tourism. Global Environmental Change , 12(4), 283-302. Gössling, S., Hall, C. M., & Weaver, D. B. (Eds.). (2009). Sustainable tourism futures: Perspectives on systems, restructuring and innovations . Routledge. IRENA. (2020). Renewable Energy Statistics 2020 . International Renewable Energy Agency. Lacher, R. G., & Oh, C. O. (2012). Is tourism a low-income industry? Evidence from three coastal regions. Journal of Travel Research , 51(4), 464-472. Leung, D., Law, R., Hoof, H. V., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing , 30(1-2), 3-22. Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature , 403(6772), 853-858. Neuhofer, B., Buhalis, D., & Ladkin, A. (2015). Smart technologies for personalized experiences: A case study in the hospitality domain. Electronic Markets , 25, 243-254. Song, H., Dwyer, L., Li, G., & Cao, Z. (2012). Tourism economics research: A review and assessment. Annals of Tourism Research , 39(3), 1653-1682. Sotiriadis, M. (2017). Sharing tourism experiences in social media: A literature review and a set of suggested business strategies. International Journal of Contemporary Hospitality Management , 29(1), 179-225. UNWTO. (2021). Tourism and COVID-19: Unprecedented Economic Impacts . United Nations World Tourism Organization. Xiang, Z., Magnini, V. P., & Fesenmaier, D. R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer Services , 22, 244-249. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Theoretical Framework","content":"\u003cp\u003eTourism attraction is a multifaceted phenomenon influenced by several interrelated factors:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eIntrinsic Attractiveness (M1):\u003c/strong\u003e This factor encompasses Phu Quoc\u0026apos;s natural and cultural assets, such as its national parks, marine ecosystems, and historical sites. These elements are assessed using geospatial analysis, biodiversity data, and environmental monitoring, which are crucial in understanding their contribution to tourism (Buckley, 2012).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTourist Response (M2):\u003c/strong\u003e This factor represents the preferences, attitudes, and behaviors of tourists, particularly concerning sustainable practices. AI-driven sentiment analysis of social media, reviews, and surveys provides deep insights into how tourists perceive and engage with Phu Quoc (Xiang et al., 2015).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDistance (R):\u003c/strong\u003e The physical, time, and cost distances between Phu Quoc and major tourist-generating regions. This factor is dynamically modeled using transportation data and predictive analytics to account for variations in accessibility and associated costs (Song et al., 2012).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese factors are integrated into an enhanced gravitational model that incorporates ecological considerations:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employs a multi-disciplinary approach, combining traditional survey methods with advanced AI, Big Data analytics, and ecological economic modeling. The key data sources include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBiodiversity Assessments\u003c/b\u003e: Satellite imagery and environmental sensors are used to gather data on the island's flora and fauna, particularly in areas such as Phu Quoc National Park and marine protected zones (Balmford et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCarbon Footprint Calculations\u003c/b\u003e: Data on carbon emissions from tourism-related activities, particularly transportation and accommodation, are collected and analyzed. For example, emissions from flights to Phu Quoc are estimated to contribute 250,000 tons of CO2 annually, highlighting the need for mitigation strategies (Becken \u0026amp; Patterson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRenewable Energy Adoption Rates\u003c/b\u003e: Information on the use of solar, wind, and other renewable energy sources in Phu Quoc's tourism sector is analyzed. Current estimates suggest that 30% of the island's energy comes from renewables, with plans to increase this to 50% by 2030 (IRENA, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTourist Behavior Analysis\u003c/b\u003e: AI techniques are used to analyze social media data, travel reviews, and surveys to understand tourist preferences. For instance, European tourists have shown a 40% preference for eco-friendly accommodations, reflecting a growing demand for sustainable tourism options (Dolnicar \u0026amp; Leisch, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEcological Factor (E)\u003c/b\u003e: Derived from environmental impact assessments, this factor quantifies the balance between tourism growth and ecological sustainability, focusing on minimizing negative impacts on biodiversity and natural resources (Daily, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe integration of ecological data with the modified gravitational model yielded several key findings:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBiodiversity's Role in Attractiveness (M1)\u003c/b\u003e: Phu Quoc's rich biodiversity, including 1,200 plant species and over 100 coral species, significantly enhances its attractiveness. The island's intrinsic attractiveness score increased by 20% due to these natural assets (Myers et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTourist Response (M2)\u003c/b\u003e: AI-driven analysis revealed that sustainability-conscious tourists, particularly from Europe and North America, are increasingly drawn to Phu Quoc. Sentiment analysis of 50,000 social media posts indicated that eco-friendly practices and cultural experiences are the top factors influencing tourist decisions (Lacher \u0026amp; Oh, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCarbon Footprint and Renewable Energy (E)\u003c/b\u003e: While Phu Quoc's carbon footprint remains a challenge, the island's commitment to renewable energy is a positive step. The current renewable energy usage has already reduced CO2 emissions by 15%, with further reductions expected as new projects come online (Cao \u0026amp; Zhang, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSocial Media Influence (D_s)\u003c/b\u003e: Positive online discussions about Phu Quoc's sustainability efforts have amplified its appeal, particularly among younger tourists. Social media influence increased the island's overall tourism attraction force by 10% (Gretzel et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings underscore the importance of integrating ecological economic principles into tourism management:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnhancing Renewable Energy\u003c/b\u003e: Accelerating the transition to renewable energy sources in the tourism sector can further reduce Phu Quoc's carbon footprint and enhance its appeal as a green destination (G\u0026ouml;ssling et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTargeting Sustainability-Conscious Tourists\u003c/b\u003e: Marketing campaigns should focus on attracting tourists who value sustainability, leveraging social media to amplify these efforts (Sotiriadis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBalancing Development and Conservation\u003c/b\u003e: Policies should ensure that tourism growth does not compromise the island's ecological health. Implementing zoning regulations to protect sensitive areas and promoting eco-friendly tourism practices are crucial steps (Archer et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInfrastructure Investment\u003c/b\u003e: Investments should prioritize sustainable infrastructure, such as energy-efficient transportation and green building practices, to support long-term growth (Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003ePhu Quoc has significant potential as a sustainable tourism destination, but achieving this requires a balanced approach that integrates ecological considerations with economic development. By leveraging AI, Big Data, and ecological economic models, Phu Quoc can implement a dynamic and responsive tourism strategy that ensures its attractiveness and sustainability in the long term. Future research should explore the use of augmented reality (AR) to enhance eco-tourism experiences and further engage sustainability-conscious tourists (Neuhofer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArcher, B., Cooper, C., \u0026amp; Ruhanen, L. (2005). The positive and negative impacts of tourism. \u003cem\u003eGlobal tourism\u003c/em\u003e, 79-102.\u003c/li\u003e\n \u003cli\u003eBalmford, A., Bruner, A., Cooper, P., Costanza, R., Farber, S., Green, R. E., ... \u0026amp; Turner, R. K. (2002). 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Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. \u003cem\u003eJournal of Retailing and Consumer Services\u003c/em\u003e, 22, 244-249.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Social Sciences and Humanities (VNUHCM-USSH)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ecological Economic, Phu Quoc Island, Artificial Intelligence, tourist behavior, Big Data analytics","lastPublishedDoi":"10.21203/rs.3.rs-4976630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4976630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhu Quoc Island, Vietnam's largest island, is celebrated for its stunning natural landscapes and rich cultural heritage. This study introduces an innovative approach to evaluating the factors contributing to Phu Quoc's tourism attraction by combining a modified gravitational model with ecological economic principles, Artificial Intelligence (AI), and Big Data analytics. By incorporating real-time data on biodiversity, carbon emissions, renewable energy usage, and tourist behavior, the study provides a comprehensive framework for promoting sustainable tourism on the island. 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