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Optimizing Recreation in Yellow River Mouth Park Based on Reference-Dependent Analysis

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Advanced Data Mining and Applications (ADMA 2024)

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Abstract

National parks are increasingly popular for both nature conservation and ecotourism, leading to higher demand for recreational activities. This study focuses on understanding the differences in preferences for recreational attributes within China’s Yellow River Mouth National Park. By examining how people’s reference points influence their preferences, the study aims to provide targeted recommendations for park management. The study utilized discrete choice experiments to collect information on preferences for five characteristics (amount of vegetation, clarity of water, amount of litter, congestion at attractions, and cost of tickets) and examined the data using mixed logit models and hierarchical Bayesian logit models. The findings show that there is a significant asymmetry in the public’s preferences for the park’s natural and management attributes, which has important implications for park planning and management. Specifically, people have higher preferences for vegetation coverage and water clarity but lower preferences for the quantity of garbage and ticket prices. The level of crowding at tourist attractions has a significant impact on preferences, and reference-dependent effects also play a role, with variations observed in the reference-dependence parameters across different attributes. However, factors such as environmental attention, recreational satisfaction, recreational importance, age, education level, and average monthly income do not have a significant influence on preferences for the five attributes.

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Authors and Affiliations

  1. International Business College, Shandong Technology and Business University, Yantai, 264000, China

    Qin Gao

  2. School of Management Science and Engineering, Shandong Technology and Business University, Yantai, 264000, China

    Linke Qin, Xiaosong Wang, Songsong Cui & Zhenrui Li

  3. School of Computing and Information Technology, University of Wollongong, Wollongong, 2500, Australia

    Hao Meng & Jun Shen

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  1. Qin Gao
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  2. Linke Qin
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  3. Hao Meng
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  4. Jun Shen
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  5. Xiaosong Wang
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Correspondence to Jun Shen .

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Editors and Affiliations

  1. Macquarie University, Sydney, NSW, Australia

    Quan Z. Sheng

  2. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  3. Australian National University, Canberra, ACT, Australia

    Jing Jiang

  4. Macquarie University, Sydney, NSW, Australia

    Xuyun Zhang

  5. The University of Adelaide, Adelaide, SA, Australia

    Wei Emma Zhang

  6. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  7. Macquarie University, Sydney, NSW, Australia

    Jia Wu

  8. University of Dubai, Dubai, United Arab Emirates

    Wathiq Mansoor

  9. Macquarie University, Sydney, NSW, Australia

    Congbo Ma

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Gao, Q. et al. (2025). Optimizing Recreation in Yellow River Mouth Park Based on Reference-Dependent Analysis. In: Sheng, Q.Z., et al. Advanced Data Mining and Applications. ADMA 2024. Lecture Notes in Computer Science(), vol 15387. Springer, Singapore. https://doi.org/10.1007/978-981-96-0811-9_17

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