IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i6p1219-d1169510.html
   My bibliography  Save this article

The Influential Factors of the Habitat Quality of the Red-crowned Crane: A Case Study of Yancheng, Jiangsu Province, China

Author

Listed:
  • Yuxun Wang

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Liang Fang

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Chao Liu

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Lanxin Wang

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Huimei Xu

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

Abstract

In order to effectively protect the habitat of cranes, this study constructs an indicator evaluation system based on the ecology–economy–society complex system and adopts the comprehensive “entropy weight method and analytic hierarchy process” evaluation model and coupled coordination model to scientifically measure the degree of coordinated development of the EES system in Yancheng. Further, a negative binomial regression model based on LASSO was used to analyze the key factors affecting the habitat quality of red-crowned cranes, and a support vector regression model was used to predict the population size of the cranes. The results show that the degree of the coordinated development of the EES system exhibited a fluctuating upward phenomenon, and the population size of the cranes also had a similar evolutionary trend, which indicates that the interaction between the two was significant and that the degree of the coordinated development of the system had a positive impact on the quality of the habitat of the cranes. Three types of ecological indicators (normalized difference vegetation index, annual precipitation, and soil erosion area) and three types of social indicators (natural growth rate, rural Engel coefficient, and public library collection) are the key factors affecting the population size of the cranes. The prediction results of the support vector regression model showed that the population of the cranes showed a fluctuating upward trend during the prediction interval, with a maximum of 952 cranes and an overall growth rate of 69.70%. The population size of the cranes is related to human social activities and the surrounding ecological environment, and the main reason for the decline in the population size of the cranes is the destruction of the local vegetation cover by the rapidly growing population and frequent human activities. Therefore, to improve the habitat quality of the cranes, local government departments need to strengthen the publicity of wildlife conservation, reduce agricultural land reclamation and pesticide pollution, and promote the coordinated development of the EES system in the Yancheng area.

Suggested Citation

  • Yuxun Wang & Liang Fang & Chao Liu & Lanxin Wang & Huimei Xu, 2023. "The Influential Factors of the Habitat Quality of the Red-crowned Crane: A Case Study of Yancheng, Jiangsu Province, China," Land, MDPI, vol. 12(6), pages 1-20, June.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1219-:d:1169510
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/6/1219/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/6/1219/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
    2. Fu, Saiji & Tian, Yingjie & Tang, Long, 2023. "Robust regression under the general framework of bounded loss functions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1325-1339.
    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. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    2. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    3. Wang, Sheng & Zimmerman, Dale L. & Breheny, Patrick, 2020. "Sparsity-regularized skewness estimation for the multivariate skew normal and multivariate skew t distributions," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    4. Ozcan, Erhan C. & Görgülü, Berk & Baydogan, Mustafa G., 2024. "Column generation-based prototype learning for optimizing area under the receiver operating characteristic curve," European Journal of Operational Research, Elsevier, vol. 314(1), pages 297-307.
    5. Sotiropoulou, Kalliopi F. & Vavatsikos, Athanasios P. & Botsaris, Pantelis N., 2024. "A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece," Renewable Energy, Elsevier, vol. 221(C).
    6. Peng, Jingfu & Yang, Yuhong, 2022. "On improvability of model selection by model averaging," Journal of Econometrics, Elsevier, vol. 229(2), pages 246-262.
    7. Hossein Jargan & Abbas Rohani & Armaghan Kosari-Moghaddam, 2022. "Application of modeling techniques for energy analysis of fruit production systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2616-2639, February.
    8. Zhang, Yongli & Rolling, Craig & Yang, Yuhong, 2021. "Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    9. Huang, Y.W. & Chen, M.Q. & Li, Y. & Guo, J., 2016. "Modeling of chemical exergy of agricultural biomass using improved general regression neural network," Energy, Elsevier, vol. 114(C), pages 1164-1175.
    10. Jan Szczegielniak & Krzysztof J Latawiec & Jacek Łuniewski & Rafał Stanisławski & Katarzyna Bogacz & Marcin Krajczy & Marek Rydel, 2018. "A study on nonlinear estimation of submaximal effort tolerance based on the generalized MET concept and the 6MWT in pulmonary rehabilitation," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    11. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
    12. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
    13. Sijia Xiang & Weixin Yao, 2018. "Semiparametric mixtures of nonparametric regressions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 131-154, February.
    14. Wenjing Yang & Yuhong Yang, 2017. "Toward an objective and reproducible model choice via variable selection deviation," Biometrics, The International Biometric Society, vol. 73(1), pages 20-30, March.
    15. Admassu N. Lamu, 2020. "Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 903-915, August.
    16. Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
    17. Pei Zhao & Chao Wang & Shuhong Sun & Xi Wang & William E. Balch, 2024. "Tracing genetic diversity captures the molecular basis of misfolding disease," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    18. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
    19. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    20. Yuchen Chen & Yuhong Yang, 2021. "The One Standard Error Rule for Model Selection: Does It Work?," Stats, MDPI, vol. 4(4), pages 1-25, November.

    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:12:y:2023:i:6:p:1219-:d:1169510. 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.