IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i5p680-d1394007.html
   My bibliography  Save this article

Coupling Coordination Analysis of County Tourism Development and Multidimensional Poverty Based on Nighttime Light Data

Author

Listed:
  • Hai Xiao

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

  • Jiahao Yu

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

  • Yifan Zhang

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

  • Chuliang Xin

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

  • Jiangjun Wan

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

  • Xiaohong Tang

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611800, China)

Abstract

In China, tourism development is a crucial approach to poverty alleviation. With the consolidation of poverty alleviation achievements and the promotion of rural revitalization, it is of great significance to explore the relationship between tourism development and poverty alleviation from the perspective of multidimensional poverty. Therefore, this study took 28 key assistance counties for rural revitalization in the Sichuan–Chongqing region (hereinafter referred to as “key counties”) as the research objects, introduced NPP-VIIRS nighttime light (NTL) data, and a coupling coordination degree (CCD) model to explore the coordination relationship and mechanism between them. The results showed that from 2015 to 2020, the tourism development index (TDI) and estimated comprehensive development index (ECDI) of the key counties increased by 112.57% and 115.12%, respectively. In addition, the spatial differences in tourism development and multidimensional poverty both showed a narrowing trend. According to the results of the CCD model, the key counties basically faced coordination obstacles in the early stage, which were mainly transformed into reluctant coordination and moderate coordination in the later stage. This indicated that tourism poverty alleviation showed a coordinated development trend overall. However, the study also found that there may not be synchronicity between tourism development and poverty alleviation and analyzed the mechanism of their interaction. Overall, the study confirmed the positive impact of tourism development on alleviating multidimensional poverty. In addition, the study found that measuring multidimensional poverty based on NTL data has a high accuracy and can provide support for poverty research. These research results have an important reference value for China to carry out sustainable tourism poverty alleviation and comprehensively promote rural revitalization.

Suggested Citation

  • Hai Xiao & Jiahao Yu & Yifan Zhang & Chuliang Xin & Jiangjun Wan & Xiaohong Tang, 2024. "Coupling Coordination Analysis of County Tourism Development and Multidimensional Poverty Based on Nighttime Light Data," Land, MDPI, vol. 13(5), pages 1-22, May.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:680-:d:1394007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/5/680/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/13/5/680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2019. "Multidimensional Poverty and Material Deprivation with Discrete Data," Themes in Economics, in: Satya R. Chakravarty (ed.), Poverty, Social Exclusion and Stochastic Dominance, pages 191-209, Springer.
    2. Jiantuo Yu, 2013. "Multidimensional Poverty in China: Findings Based on the CHNS," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(2), pages 315-336, June.
    3. Kubiszewski, Ida & Costanza, Robert & Franco, Carol & Lawn, Philip & Talberth, John & Jackson, Tim & Aylmer, Camille, 2013. "Beyond GDP: Measuring and achieving global genuine progress," Ecological Economics, Elsevier, vol. 93(C), pages 57-68.
    4. John Gibson & Susan Olivia & Geua Boe‐Gibson, 2020. "Night Lights In Economics: Sources And Uses," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 955-980, December.
    5. repec:lic:licosd:41920 is not listed on IDEAS
    6. Alam, Md. Samsul & Paramati, Sudharshan Reddy, 2016. "The impact of tourism on income inequality in developing economies: Does Kuznets curve hypothesis exist?," Annals of Tourism Research, Elsevier, vol. 61(C), pages 111-126.
    7. Gibson, John & Olivia, Susan & Boe-Gibson, Geua & Li, Chao, 2021. "Which night lights data should we use in economics, and where?," Journal of Development Economics, Elsevier, vol. 149(C).
    8. Zhizhu Lai & Dongmei Ge & Haibin Xia & Yanlin Yue & Zheng Wang, 2020. "Coupling coordination between environment, economy and tourism: A case study of China," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    9. Wenli Jing & Wei Zhang & Pingping Luo & Lian Wu & Lei Wang & Kanhua Yu, 2022. "Assessment of Synergistic Development Potential between Tourism and Rural Restructuring Using a Coupling Analysis: A Case Study of Southern Shaanxi, China," Land, MDPI, vol. 11(8), pages 1-21, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omoniyi Alimi & Geua Boe-Gibson & John Gibson, 2022. "Noisy Night Lights Data: Effects on Research Findings for Developing Countries," Working Papers in Economics 22/12, University of Waikato.
    2. Beyer, Robert & Yao, Jiaxiong & Hu, Yingyao, 2022. "Measuring Quarterly Economic Growth from Outer Space," VfS Annual Conference 2022 (Basel): Big Data in Economics 264007, Verein für Socialpolitik / German Economic Association.
    3. GIBSON, John & ZHANG, Xiaoxuan & PARK, Albert & YI, Jiang & XI, Li, 2024. "Remotely measuring rural economic activity and poverty : Do we just need better sensors?," CEI Working Paper Series 2023-08, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    4. John Gibson & Geua Boe-Gibson, 2020. "Three Facts About Night Lights Data," Working Papers in Economics 20/03, University of Waikato.
    5. repec:ags:aaea22:335528 is not listed on IDEAS
    6. Gibson, John & Jiang, Yi & Susantono, Bambang, 2023. "Revisiting the role of secondary towns: How different types of urban growth relate to poverty in Indonesia," World Development, Elsevier, vol. 169(C).
    7. McSharry, Patrick & Mawejje, Joseph, 2024. "Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    8. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    9. Haoqi Qian & Zhengyu Shi & Libo Wu, 2021. "Inferring Economic Condition Uncertainty from Electricity Big Data," Papers 2107.11593, arXiv.org, revised May 2023.
    10. Jesson A. Pagaduan, 2022. "Do higher‐quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines," Asian Economic Journal, East Asian Economic Association, vol. 36(3), pages 288-317, September.
    11. Bonggeun Kim & John Gibson & Geua Boe‐Gibson, 2024. "Measurement errors in popular night lights data may bias estimated impacts of economic sanctions: Evidence from closing the Kaesong Industrial Zone," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 375-389, January.
    12. Roberts,Mark, 2021. "Tracking Economic Activity in Response to the COVID-19 Crisis Using Nighttime Lights — The Case of Morocco," Policy Research Working Paper Series 9538, The World Bank.
    13. John Gibson & Yi Jiang & Bambang Susantono, 2022. "Revisiting the role of secondary towns: Effects of different types of urban growth on poverty in Indonesia," Working Papers in Economics 22/05, University of Waikato.
    14. Guo, Junping & Qu, Song & Zhu, Tiehui, 2022. "Estimating China’s relative and multidimensional Poverty: Evidence from micro-level data of 6145 rural households," World Development Perspectives, Elsevier, vol. 26(C).
    15. Bruno Morando, 2024. "Testing the GAEZ agronomic model in the fields:Evidence from Uganda," Economics Department Working Paper Series n320-24.pdf, Department of Economics, National University of Ireland - Maynooth.
    16. Guo, Junping & Qu, Song, 2021. "Multidimensional and Relative Poverty in Rural China: Evidence from Micro-Level Data of 6145 Households," 2021 Conference, August 17-31, 2021, Virtual 315040, International Association of Agricultural Economists.
    17. John Gibson & Bonggeun Kim & Geua Boe-Gibson, 2022. "How effective are sanctions on North Korea? Popular DMSP night-lights data may bias evaluations due to blurring and poor low-light detection," Working Papers in Economics 22/06, University of Waikato, revised 14 Nov 2022.
    18. Chen, Liming & Lu, Yang & Nanayakkara, Aruna, 2023. "Rural road connectivity and local economic Activity: Evidence from Sri Lanka’s iRoad program," Transport Policy, Elsevier, vol. 144(C), pages 49-64.
    19. Ke-Mei Chen & Chao-Hsien Leu & Te-Mu Wang, 2019. "Measurement and Determinants of Multidimensional Poverty: Evidence from Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 459-478, September.
    20. Ismaila Rimi Abubakar, 2022. "Multidimensional Poverty among Nigerian Households: Sustainable Development Implications," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(2), pages 993-1014, November.
    21. John Gibson, 2021. "Better Night Lights Data, For Longer," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 770-791, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:680-:d:1394007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.