IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5336-d271304.html
   My bibliography  Save this article

The Impact of China’s Grain for Green Program on Rural Economy and Precipitation: A Case Study of Yan River Basin in the Loess Plateau

Author

Listed:
  • Biyun Guo

    (Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China
    State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, Qinghai, China)

  • Taiping Xie

    (Rural Economy Institute, Tongling University, Tongling 244000, Anhui, China)

  • M.V. Subrahmanyam

    (Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China)

Abstract

Large-scale deforestation and abandoned planting will bring about the destruction of the ecological environment and the deterioration of the climate. In 1999, China initiated its “Grain for Green” Program (GGP) to improve the ecological environment, control soil erosion, and adjust the agricultural industrial structure to promote the sustainable development of the rural economy. In this paper, economic statistics, rainfall, and remote sensing data are used to analyze the impact of the GGP on agricultural and rural economic development and regional precipitation in the hilly and gully regions of the Loess Plateau. The results show that since the implementation of the program, the employment structure of the labor force has changed and the regional economic growth and farmers’ income have increased. From 2000 to 2016, the total gross domestic product (GDP) and per capita GDP of the Yan River Basin increased. The conversion of large-scale sloping farmland to forestry and grassland resulted in the decrease of farmland area and the increase of forestry area. The maximum, minimum, and mean value of vegetation coverage increased year by year. With the increase of vegetation coverage, the surface roughness, soil water content, and evapotranspiration improved and annual average precipitation grew significantly after the implementation of the program (2000 to 2018). From 1970 to the implementation of the project (1999), the annual average rainfall decreased at the trough from 1988 to 1999, and there was an overall upward trend from 1970 to 2018. The GGP has an important impact on the economy and people’s income in the Yan River Basin, and the vegetation change caused by the variation of land use types has a certain impact on regional rainfall. Under the background of global and regional climate change, it is of great significance to fully understand the impacts of vegetation conversion on climate and its mechanism for objective assessment of driving factors in regional and global climate, as well as for scientific planning of future land use.

Suggested Citation

  • Biyun Guo & Taiping Xie & M.V. Subrahmanyam, 2019. "The Impact of China’s Grain for Green Program on Rural Economy and Precipitation: A Case Study of Yan River Basin in the Loess Plateau," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5336-:d:271304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5336/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/19/5336/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qin Liu & Tiange Shi, 2019. "Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    2. Jieming Chou & Tian Xian & Runze Zhao & Yuan Xu & Fan Yang & Mingyang Sun, 2019. "Drought Risk Assessment and Estimation in Vulnerable Eco-Regions of China: Under the Background of Climate Change," Sustainability, MDPI, vol. 11(16), pages 1-14, August.
    3. Carlos J. Baños & María Hernández & Antonio M. Rico & Jorge Olcina, 2019. "The Hydrosocial Cycle in Coastal Tourist Destinations in Alicante, Spain: Increasing Resilience to Drought," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    4. Xiaowan Liu & Zongxue Xu & Dingzhi Peng, 2019. "Spatio-Temporal Patterns of Vegetation in the Yarlung Zangbo River, China during 1998–2014," Sustainability, MDPI, vol. 11(16), pages 1-11, August.
    5. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiahui Zhou & Peng Gao & Changxue Wu & Xingmin Mu, 2023. "Analysis of Land Use Change Characteristics and Its Driving Forces in the Loess Plateau: A Case Study in the Yan River Basin," Land, MDPI, vol. 12(9), pages 1-20, August.
    2. Iuliana Vijulie & Mihaela Preda & Ana Irina Lequeux-Dincă & Roxana Cuculici & Elena Matei & Alina Mareci & Gabriela Manea & Anca Tudoricu, 2019. "Economic Productivity vs. Ecological Protection in Danube Floodplain. Case Study: Danube’s Sector between Olt and Vedea," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    3. Yingjuan Li & Qiong Lin & Jianyu Zhang & Liuhua Fang & Yi Li & Lianjun Zhang & Chuanhao Wen, 2023. "Convergence Analysis of the Overall Benefits of Returning Farmland into Forest in the Upper Yangtze River Basin, China," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    4. Caixia Hou & Mengmeng Zhang & Mengmeng Wang & Hanliang Fu & Mengjie Zhang, 2021. "Factors Influencing Grazing Behavior by Using the Consciousness-Context-Behavior Theory—A Case Study from Yanchi County, China," Land, MDPI, vol. 10(11), pages 1-16, October.

    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. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 869-909, December.
    2. Mauricio Velasquez, 2016. "Compositions vs Gini: A new metric to evaluate the effects of land-income disparities," 2016 Papers pve364, Job Market Papers.
    3. Janina Janurek & Sascha Abdel Hadi & Andreas Mojzisch & Jan Alexander Häusser, 2018. "The Association of the 24 Hour Distribution of Time Spent in Physical Activity, Work, and Sleep with Emotional Exhaustion," IJERPH, MDPI, vol. 15(9), pages 1-14, September.
    4. J. A. Martín-Fernández, 2021. "“Compositional Data Analysis in Practice” by Michael Greenacre Universitat Pompeu Fabra (Barcelona, Spain), Chapman and Hall/CRC, 2018," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 109-111, April.
    5. Andriansyah, Andriansyah & Messinis, George, 2016. "Intended use of IPO proceeds and firm performance: A quantile regression approach," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 14-30.
    6. Zhao, T.; & Sutton, M.; & Meacock, M.;, 2023. "Use of compositional covariates in linear regression: problems and solutions," Health, Econometrics and Data Group (HEDG) Working Papers 23/16, HEDG, c/o Department of Economics, University of York.
    7. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    8. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    9. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    10. Fengjie Gao & Si Zhang & Rui Yu & Yafang Zhao & Yuxin Chen & Ying Zhang, 2023. "Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China," Land, MDPI, vol. 12(6), pages 1-19, June.
    11. Dorothea Dumuid & Željko Pedišić & Javier Palarea-Albaladejo & Josep Antoni Martín-Fernández & Karel Hron & Timothy Olds, 2020. "Compositional Data Analysis in Time-Use Epidemiology: What, Why, How," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    12. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.
    13. Rieser, Christopher & Filzmoser, Peter, 2023. "Extending compositional data analysis from a graph signal processing perspective," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    14. Sandra Ricart & Rubén A. Villar-Navascués & Maria Hernández-Hernández & Antonio M. Rico-Amorós & Jorge Olcina-Cantos & Enrique Moltó-Mantero, 2021. "Extending Natural Limits to Address Water Scarcity? The Role of Non-Conventional Water Fluxes in Climate Change Adaptation Capacity: A Review," Sustainability, MDPI, vol. 13(5), pages 1-31, February.
    15. Ziyang Wang & Peiji Shi & Xuebin Zhang & Huali Tong & Weiping Zhang & Yue Liu, 2021. "Research on Landscape Pattern Construction and Ecological Restoration of Jiuquan City Based on Ecological Security Evaluation," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    16. Haixiang Zhang & Jun Chen & Zhigang Li & Lei Liu, 2021. "Testing for Mediation Effect with Application to Human Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 313-328, July.
    17. Defever, F. & Riaño, A., 2022. "Firm-Destination Heterogeneity and the Distribution of Export Intensity," Working Papers 22/01, Department of Economics, City University London.
    18. Quim Zaldo-Aubanell & Ferran Campillo i López & Albert Bach & Isabel Serra & Joan Olivet-Vila & Marc Saez & David Pino & Roser Maneja, 2021. "Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study," IJERPH, MDPI, vol. 18(7), pages 1-20, April.
    19. Shanshan Cao & Zhaoli He & Songmao Wang & Jinlan Niu, 2023. "Decoupling Analysis of Water Consumption and Economic Growth in Tourism in Arid Areas: Case of Xinjiang, China," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    20. Qin Ji & Jianping Yang & Can Wang & Hongju Chen & Qingshan He & Zhenqi Sun & Quntao Duan & Yao Li, 2021. "The Risk of the Population in a Changing Climate over the Tibetan Plateau, China: Integrating Hazard, Population Exposure and Vulnerability," Sustainability, MDPI, vol. 13(7), pages 1-20, March.

    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:jsusta:v:11:y:2019:i:19:p:5336-:d:271304. 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.