IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i12p1202-d691117.html
   My bibliography  Save this article

Do Internet Skills Increase Farmers’ Willingness to Participate in Environmental Governance? Evidence from Rural China

Author

Listed:
  • Qiang He

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Xin Deng

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Chuan Li

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Zhongcheng Yan

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

  • Yanbin Qi

    (College of Economics, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

Environmental pollution is threatening the sustainable development of rural areas. Increasing farmers’ willingness to participate in environmental governance (FWPEG) can effectively reduce this threat. Fortunately, the internet can speed up the process. However, it is unclear whether and to what extent the mastery of internet skills will increase FWPEG. This study uses data from 3503 farmers in 30 provinces in mainland China. It uses the TE and IVQTE models to correct selection bias and quantitatively assess the impact of mastery of internet skills on FWPEG. The results show: (1) mastering internet skills can significantly increase FWPEG, and after correcting the endogenous deviation, the marginal benefit of farmers mastering internet skills is 0.124; (2) in the 34–81% quantile range, internet skills show a declining development trend in FWPEG, which is in line with “the law of diminishing marginal utility”, and mastery of the impact of internet skills on FWPEG has “leaping” (33% → 34%)” and “sagging (81% → 82%)” characteristics; (3) compared to that of the east, internet skills in central and western regions have a more significant role in promoting FWPEG. In general, internet skills can effectively increase FWPEG, and the impact will be more pronounced in underdeveloped areas. The influence of internet skills on FWPEG will gradually weaken with the increase of FWPEG. The results of this research help to coordinate the relationship between government environmental governance and rural environmental autonomy and provide some new ideas for realizing global rural revitalization.

Suggested Citation

  • Qiang He & Xin Deng & Chuan Li & Zhongcheng Yan & Yanbin Qi, 2021. "Do Internet Skills Increase Farmers’ Willingness to Participate in Environmental Governance? Evidence from Rural China," Agriculture, MDPI, vol. 11(12), pages 1-18, November.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1202-:d:691117
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/12/1202/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/12/1202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raquel S. Dias & Daniela V. T. A. Costa & Helena E. Correia & Cristina A. Costa, 2021. "Building Bio-Districts or Eco-Regions: Participative Processes Supported by Focal Groups," Agriculture, MDPI, vol. 11(6), pages 1-14, May.
    2. Newig, Jens & Fritsch, Oliver, 2008. "Environmental governance: Participatory, multi-level - and effective?," UFZ Discussion Papers 15/2008, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    3. Markus Frölich & Blaise Melly, 2013. "Unconditional Quantile Treatment Effects Under Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 346-357, July.
    4. Jin Guo & Junhong Bai, 2019. "The Role of Public Participation in Environmental Governance: Empirical Evidence from China," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    5. Mustafa Hakkı Aydoğdu & Mehmet Reşit Sevinç & Mehmet Cançelik & Hatice Parlakçı Doğan & Zeliha Şahin, 2020. "Determination of Farmers’ Willingness to Pay for Sustainable Agricultural Land Use in the GAP-Harran Plain of Turkey," Land, MDPI, vol. 9(8), pages 1-15, August.
    6. Hassan, Suziana & Olsen, Søren Bøye & Thorsen, Bo Jellesmark, 2019. "Urban-rural divides in preferences for wetland conservation in Malaysia," Land Use Policy, Elsevier, vol. 84(C), pages 226-237.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    8. Conley, Kelsey & Whitacre, Brian, 2015. "Does Broadband Matter for Rural Entrepreneurs or ‘Creative Class’ Employees?," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196832, Southern Agricultural Economics Association.
    9. Shiwang Yu & Jianxia Bao & Wen Ding & Xue Chen & Xiaonan Tang & Jianli Hao & Wei Zhang & Prateek Singh, 2021. "Investigating the Relationship between Public Satisfaction and Public Environmental Participation during Government Treatment of Urban Malodorous Black River in China," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    10. Francesco Mantino & Francesco Vanni, 2018. "The Role of Localized Agri-Food Systems in the Provision of Environmental and Social Benefits in Peripheral Areas: Evidence from Two Case Studies in Italy," Agriculture, MDPI, vol. 8(8), pages 1-19, August.
    11. Tu, Zhengge & Hu, Tianyang & Shen, Renjun, 2019. "Evaluating public participation impact on environmental protection and ecological efficiency in China: Evidence from PITI disclosure," China Economic Review, Elsevier, vol. 55(C), pages 111-123.
    12. Wanglin Ma & Xiaobing Wang, 2020. "Internet Use, Sustainable Agricultural Practices and Rural Incomes: Evidence from China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1087-1112, October.
    13. Xin Deng & Miao Zeng & Dingde Xu & Yanbin Qi, 2020. "Does Social Capital Help to Reduce Farmland Abandonment? Evidence from Big Survey Data in Rural China," Land, MDPI, vol. 9(10), pages 1-17, September.
    14. Huang, Wencheng & Zhang, Yue & Yu, Yaocheng & Xu, Yifei & Xu, Minhao & Zhang, Rui & De Dieu, Gatesi Jean & Yin, Dezhi & Liu, Zhanru, 2021. "Historical data-driven risk assessment of railway dangerous goods transportation system: Comparisons between Entropy Weight Method and Scatter Degree Method," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    15. Li, Bo & Ding, Junqi & Wang, Jieqiong & Zhang, Biao & Zhang, Lingxian, 2021. "Key factors affecting the adoption willingness, behavior, and willingness-behavior consistency of farmers regarding photovoltaic agriculture in China," Energy Policy, Elsevier, vol. 149(C).
    16. Garmendia, Eneko & Stagl, Sigrid, 2010. "Public participation for sustainability and social learning: Concepts and lessons from three case studies in Europe," Ecological Economics, Elsevier, vol. 69(8), pages 1712-1722, June.
    17. Lokesh Jain & Harish Kumar & R. K. Singla, 2015. "Assessing Mobile Technology Usage for Knowledge Dissemination among Farmers in Punjab," Information Technology for Development, Taylor & Francis Journals, vol. 21(4), pages 668-676, October.
    18. Brian Whitacre & Roberto Gallardo & Sharon Strover, 2014. "Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(3), pages 649-670, November.
    19. Ronna Cong & David M. Drukker, 2001. "Treatment effects model," Stata Technical Bulletin, StataCorp LP, vol. 10(55).
    20. Muhammad Imran Qureshi & Usama Awan & Zeeshan Arshad & Amran Md. Rasli & Khalid Zaman & Faisal Khan, 2016. "Dynamic linkages among energy consumption, air pollution, greenhouse gas emissions and agricultural production in Pakistan: sustainable agriculture key to policy success," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 367-381, October.
    21. Ma, Wanglin & Zhou, Xiaoshi & Renwick, Alan, 2019. "Impact of off-farm income on household energy expenditures in China: Implications for rural energy transition," Energy Policy, Elsevier, vol. 127(C), pages 248-258.
    22. Deng, Xin & Xu, Dingde & Zeng, Miao & Qi, Yanbin, 2019. "Does early-life famine experience impact rural land transfer? Evidence from China," Land Use Policy, Elsevier, vol. 81(C), pages 58-67.
    23. Zhang, Guoxing & Deng, Nana & Mou, Haizhen & Zhang, Zhe George & Chen, Xiaofeng, 2019. "The impact of the policy and behavior of public participation on environmental governance performance: Empirical analysis based on provincial panel data in China," Energy Policy, Elsevier, vol. 129(C), pages 1347-1354.
    24. Yuxin Zhu & Dazuo Tian & Feng Yan, 2020. "Effectiveness of Entropy Weight Method in Decision-Making," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-5, March.
    25. Deng, Xin & Xu, Dingde & Zeng, Miao & Qi, Yanbin, 2019. "Does Internet use help reduce rural cropland abandonment? Evidence from China," Land Use Policy, Elsevier, vol. 89(C).
    26. Ismet Boz, 2018. "Determinants of farmers’ enrollment in voluntary environmental programs: evidence from the Eregli Reed Bed area of Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(6), pages 2643-2661, December.
    27. Soon, Jan-Jan & Ahmad, Siti-Aznor, 2015. "Willingly or grudgingly? A meta-analysis on the willingness-to-pay for renewable energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 877-887.
    28. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    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. Qianhui Ma & Shaofeng Zheng & Peng Deng, 2022. "Impact of Internet Use on Farmers’ Organic Fertilizer Application Behavior under the Climate Change Context: The Role of Social Network," Land, MDPI, vol. 11(9), pages 1-19, September.
    2. Jiayi Tang & Haibo Ruan & Chao Wang & Wendong Xu & Changgui Li & Xuan Dong, 2022. "Social Network, Cognition and Participation in Rural Health Governance," IJERPH, MDPI, vol. 19(5), pages 1-17, March.

    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. Xue Yang & Shili Guo & Xin Deng & Dingde Xu, 2021. "Livelihood Adaptation of Rural Households under Livelihood Stress: Evidence from Sichuan Province, China," Agriculture, MDPI, vol. 11(6), pages 1-19, May.
    2. Rui Min & Hongxin Yang & Xu Mo & Yanbin Qi & Dingde Xu & Xin Deng, 2022. "Does Institutional Social Insurance Cause the Abandonment of Cultivated Land? Evidence from Rural China," IJERPH, MDPI, vol. 19(3), pages 1-11, January.
    3. Huanxin Yang & Kai Huang & Xin Deng & Dingde Xu, 2021. "Livelihood Capital and Land Transfer of Different Types of Farmers: Evidence from Panel Data in Sichuan Province, China," Land, MDPI, vol. 10(5), pages 1-21, May.
    4. Lin Dong & Zuobao Wang & Yuxiang Zhou, 2023. "Public Participation and the Effect of Environmental Governance in China: A Systematic Review and Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-16, March.
    5. Thanh‐Tung Nguyen & Trung Thanh Nguyen & Ulrike Grote, 2023. "Internet use and agricultural productivity in rural Vietnam," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1309-1326, August.
    6. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    7. Maier, Michael, 2011. "Tests for distributional treatment effects under unconfoundedness," Economics Letters, Elsevier, vol. 110(1), pages 49-51, January.
    8. Zheng, Hongyun & Ma, Wanglin & Wang, Fang & Li, Gucheng, 2021. "Does internet use improve technical efficiency of banana production in China? Evidence from a selectivity-corrected analysis," Food Policy, Elsevier, vol. 102(C).
    9. Rietveld, Cornelius A. & Webbink, Dinand, 2016. "On the genetic bias of the quarter of birth instrument," Economics & Human Biology, Elsevier, vol. 21(C), pages 137-146.
    10. Padmaja Ayyagari & David Frisvold, 2015. "The Impact of Social Security Income on Cognitive Function at Older Ages," NBER Working Papers 21484, National Bureau of Economic Research, Inc.
    11. Lars Lefgren & Frank McIntyre, 2006. "The Relationship between Women's Education and Marriage Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 24(4), pages 787-830, October.
    12. Balestra, Simone & Backes-Gellner, Uschi, 2017. "Heterogeneous returns to education over the wage distribution: Who profits the most?," Labour Economics, Elsevier, vol. 44(C), pages 89-105.
    13. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    14. Kai Huang & Xin Deng & Yi Liu & Zhuolin Yong & Dingde Xu, 2020. "Does off-Farm Migration of Female Laborers Inhibit Land Transfer? Evidence from Sichuan Province, China," Land, MDPI, vol. 9(1), pages 1-14, January.
    15. Hiroaki Kaido & Kaspar Wüthrich, 2021. "Decentralization estimators for instrumental variable quantile regression models," Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
    16. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    17. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    18. Cai, Yi & Sun, Yucheng & Qi, Wene & Yi, Famin, 2022. "Impact of smartphone use on production outsourcing: evidence from litchi farming in southern China," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 25(4), September.
    19. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    20. Xin Deng & Zhongcheng Yan & Dingde Xu & Yanbin Qi, 2020. "Land Registration, Adjustment Experience, and Agricultural Machinery Adoption: Empirical Analysis from Rural China," Land, MDPI, vol. 9(3), pages 1-14, 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:jagris:v:11:y:2021:i:12:p:1202-:d:691117. 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.