IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v149y2021ics0301421520308120.html
   My bibliography  Save this article

Key factors affecting the adoption willingness, behavior, and willingness-behavior consistency of farmers regarding photovoltaic agriculture in China

Author

Listed:
  • Li, Bo
  • Ding, Junqi
  • Wang, Jieqiong
  • Zhang, Biao
  • Zhang, Lingxian

Abstract

Agricultural fossil energy consumption increases carbon dioxide emissions and is a critical concern in China. Photovoltaic agriculture refers to combining agricultural activities and photovoltaic power generation without changing the agricultural land and affecting agricultural production. It is a new agricultural production approach and has been identified as an important measure to deal with environmental pollution and fossil energy consumption. The goal of this study was to analyze the key factors that influence the willingness, behavior, and willingness-behavior consistency of farmers to adopt photovoltaic agriculture. A survey with 643 participants was conducted in China. The bivariate probit model and the binary logistic regression were used to test nineteen influencing factors. The results showed that the proportion of farmers whose adoption willingness was consistent with the adoption behavior was 37.1%, whereas 62.9% of farmers exhibited inconsistency between adoption willingness and adoption behavior. Differences were observed in the key factors influencing the willingness, behavior, and willingness-behavior consistency of adopting photovoltaic agriculture. The usefulness perception and technical training had significant positive impacts on the adoption willingness, adoption behavior, and willingness-behavior consistency of the farmers, whereas the photovoltaic investment cost had a negative impact. The results of this study provide an understanding of the factors influencing the promotion and dissemination of photovoltaic agriculture, a basis for optimizing related policies, and references to facilitate the implementation of photovoltaic agriculture in rural areas in other countries.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:enepol:v:149:y:2021:i:c:s0301421520308120
    DOI: 10.1016/j.enpol.2020.112101
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520308120
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2020.112101?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bashiri, Ali & Alizadeh, Sasan H., 2018. "The analysis of demographics, environmental and knowledge factors affecting prospective residential PV system adoption: A study in Tehran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3131-3139.
    2. Mao, Hui & Zhou, Li & Ifft, Jennifer & Ying, RuiYao, 2019. "Risk preferences, production contracts and technology adoption by broiler farmers in China," China Economic Review, Elsevier, vol. 54(C), pages 147-159.
    3. Zhang, Biao & Fu, Zetian & Wang, Jieqiong & Zhang, Lingxian, 2019. "Farmers’ adoption of water-saving irrigation technology alleviates water scarcity in metropolis suburbs: A case study of Beijing, China," Agricultural Water Management, Elsevier, vol. 212(C), pages 349-357.
    4. Zhai, Pei & Williams, Eric D., 2012. "Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model," Renewable Energy, Elsevier, vol. 41(C), pages 350-357.
    5. Yamamoto, Yoshihiro, 2015. "Opinion leadership and willingness to pay for residential photovoltaic systems," Energy Policy, Elsevier, vol. 83(C), pages 185-192.
    6. Christopher J. Traverse & Richa Pandey & Miles C. Barr & Richard R. Lunt, 2017. "Emergence of highly transparent photovoltaics for distributed applications," Nature Energy, Nature, vol. 2(11), pages 849-860, November.
    7. Alam, Khorshed, 2015. "Farmers’ adaptation to water scarcity in drought-prone environments: A case study of Rajshahi District, Bangladesh," Agricultural Water Management, Elsevier, vol. 148(C), pages 196-206.
    8. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    9. Gao, Li & Zhang, Wendong & Mei, Yingdan & Sam, Abdoul G. & Song, Yu & Jin, Shuqin, 2018. "Do farmers adopt fewer conservation practices on rented land? Evidence from straw retention in China," Land Use Policy, Elsevier, vol. 79(C), pages 609-621.
    10. Kardooni, Roozbeh & Yusoff, Sumiani Binti & Kari, Fatimah Binti, 2016. "Renewable energy technology acceptance in Peninsular Malaysia," Energy Policy, Elsevier, vol. 88(C), pages 1-10.
    11. Brudermann, Thomas & Reinsberger, Kathrin & Orthofer, Anita & Kislinger, Martin & Posch, Alfred, 2013. "Photovoltaics in agriculture: A case study on decision making of farmers," Energy Policy, Elsevier, vol. 61(C), pages 96-103.
    12. Chowdhury, Sanjeeda & Sumita, Ushio & Islam, Ashraful & Bedja, Idriss, 2014. "Importance of policy for energy system transformation: Diffusion of PV technology in Japan and Germany," Energy Policy, Elsevier, vol. 68(C), pages 285-293.
    13. Hassanien, Reda Hassanien Emam & Li, Ming & Dong Lin, Wei, 2016. "Advanced applications of solar energy in agricultural greenhouses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 989-1001.
    14. Li, Xuesong & Li, Hao & Wang, Xingwu, 2013. "Farmers' willingness to convert traditional houses to solar houses in rural areas: A survey of 465 households in Chongqing, China," Energy Policy, Elsevier, vol. 63(C), pages 882-886.
    15. Kassie, Menale & Shiferaw, Bekele & Muricho, Geoffrey, 2011. "Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda," World Development, Elsevier, vol. 39(10), pages 1784-1795.
    16. Marcello Graziano & Kenneth Gillingham, 2015. "Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment," Journal of Economic Geography, Oxford University Press, vol. 15(4), pages 815-839.
    17. Mariano, Marc Jim & Villano, Renato & Fleming, Euan, 2012. "Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines," Agricultural Systems, Elsevier, vol. 110(C), pages 41-53.
    18. Vasseur, Véronique & Kemp, René, 2015. "The adoption of PV in the Netherlands: A statistical analysis of adoption factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 483-494.
    19. Rode, Johannes & Weber, Alexander, 2016. "Does localized imitation drive technology adoption? A case study on rooftop photovoltaic systems in Germany," Journal of Environmental Economics and Management, Elsevier, vol. 78(C), pages 38-48.
    20. Sven Müller & Johannes Rode, 2013. "The adoption of photovoltaic systems in Wiesbaden, Germany," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 22(5), pages 519-535, July.
    21. Xue, Jinlin, 2017. "Photovoltaic agriculture - New opportunity for photovoltaic applications in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1-9.
    22. Fang, Xingming & Wang, Lu & Sun, Chuanwang & Zheng, Xuemei & Wei, Jing, 2021. "Gap between words and actions: Empirical study on consistency of residents supporting renewable energy development in China," Energy Policy, Elsevier, vol. 148(PA).
    23. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    24. Kurata, Masamitsu & Matsui, Noriatsu & Ikemoto, Yukio & Tsuboi, Hiromi, 2018. "Do determinants of adopting solar home systems differ between households and micro-enterprises? Evidence from rural Bangladesh," Renewable Energy, Elsevier, vol. 129(PA), pages 309-316.
    25. Baur, Lucia & Uriona M., Mauricio, 2018. "Diffusion of photovoltaic technology in Germany: A sustainable success or an illusion driven by guaranteed feed-in tariffs?," Energy, Elsevier, vol. 150(C), pages 289-298.
    26. Abdulai, Awudu & Owusu, Victor & Bakang, John-Eudes A., 2011. "Adoption of safer irrigation technologies and cropping patterns: Evidence from Southern Ghana," Ecological Economics, Elsevier, vol. 70(7), pages 1415-1423, May.
    27. Qureshi, Tahir Masood & Ullah, Kafait & Arentsen, Maarten J., 2017. "Factors responsible for solar PV adoption at household level: A case of Lahore, Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 754-763.
    28. Kwan, Calvin Lee, 2012. "Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States," Energy Policy, Elsevier, vol. 47(C), pages 332-344.
    29. De Groote, Olivier & Pepermans, Guido & Verboven, Frank, 2016. "Heterogeneity in the adoption of photovoltaic systems in Flanders," Energy Economics, Elsevier, vol. 59(C), pages 45-57.
    30. Rode, Johannes & Weber, Alexander, 2016. "Does localized imitation drive technology adoption? A case study on rooftop photovoltaic systems in Germany," Journal of Environmental Economics and Management, Elsevier, vol. 78(C), pages 38-48.
    31. Sommerfeld, Jeff & Buys, Laurie & Mengersen, Kerrie & Vine, Desley, 2017. "Influence of demographic variables on uptake of domestic solar photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 315-323.
    32. Jian Chen & Yiping Liu & Lingjun Wang, 2019. "Research on Coupling Coordination Development for Photovoltaic Agriculture System in China," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    33. Broman Toft, Madeleine & Schuitema, Geertje & Thøgersen, John, 2014. "Responsible technology acceptance: Model development and application to consumer acceptance of Smart Grid technology," Applied Energy, Elsevier, vol. 134(C), pages 392-400.
    34. Noll, Daniel & Dawes, Colleen & Rai, Varun, 2014. "Solar Community Organizations and active peer effects in the adoption of residential PV," Energy Policy, Elsevier, vol. 67(C), pages 330-343.
    35. Mukasa, Adamon N., 2018. "Technology adoption and risk exposure among smallholder farmers: Panel data evidence from Tanzania and Uganda," World Development, Elsevier, vol. 105(C), pages 299-309.
    36. Simtowe, Franklin, 2006. "Can Risk-aversion towards fertilizer explain part of the non-adoption puzzle for hybrid maize? Empirical evidence from Malawi," MPRA Paper 1241, University Library of Munich, Germany, revised 20 Dec 2006.
    37. Korcaj, Liridon & Hahnel, Ulf J.J. & Spada, Hans, 2015. "Intentions to adopt photovoltaic systems depend on homeowners' expected personal gains and behavior of peers," Renewable Energy, Elsevier, vol. 75(C), pages 407-415.
    38. Karakaya, Emrah & Hidalgo, Antonio & Nuur, Cali, 2015. "Motivators for adoption of photovoltaic systems at grid parity: A case study from Southern Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1090-1098.
    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. Fabian Scheller & Isabel Doser & Daniel Sloot & Russell McKenna & Thomas Bruckner, 2020. "Exploring the Role of Stakeholder Dynamics in Residential Photovoltaic Adoption Decisions: A Synthesis of the Literature," Energies, MDPI, vol. 13(23), pages 1-31, November.
    2. Esplin, Ryan & Nelson, Tim, 2022. "Redirecting solar feed in tariffs to residential battery storage: Would it be worth it?," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 373-389.
    3. Jinxing Wang & Wanming Li & Shamsheer ul Haq & Pomi Shahbaz, 2023. "Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    4. Alipour, M. & Salim, H. & Stewart, Rodney A. & Sahin, Oz, 2020. "Predictors, taxonomy of predictors, and correlations of predictors with the decision behaviour of residential solar photovoltaics adoption: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    5. Fabian Scheller & Isabel Doser & Emily Schulte & Simon Johanning & Russell McKenna & Thomas Bruckner, 2021. "Stakeholder dynamics in residential solar energy adoption: findings from focus group discussions in Germany," Papers 2104.14240, arXiv.org.
    6. Palm, A., 2020. "Early adopters and their motives: Differences between earlier and later adopters of residential solar photovoltaics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Yamashiro, Ririka & Mori, Akihisa, 2023. "Combined third-party ownership and aggregation business model for the adoption of rooftop solar PV–battery systems: Implications from the case of Miyakojima Island, Japan," Energy Policy, Elsevier, vol. 173(C).
    8. Bruno Moreno Rodrigo de Freitas, 2020. "Quantifying the effect of regulated volumetric electriciy tariffs on residential PV adoption under net metering scheme," Working Papers hal-02976874, HAL.
    9. Best, Rohan & Chareunsy, Andrea, 2022. "The impact of income on household solar panel uptake: Exploring diverse results using Australian data," Energy Economics, Elsevier, vol. 112(C).
    10. Neij, Lena & Heiskanen, Eva & Strupeit, Lars, 2017. "The deployment of new energy technologies and the need for local learning," Energy Policy, Elsevier, vol. 101(C), pages 274-283.
    11. Paul Simshauser & Tim Nelson & Joel Gilmore, 2022. "The sunshine state: implications from mass rooftop solar PV take-up rates in Queensland," Working Papers EPRG2219, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    12. Collier, Samuel H.C. & House, Jo I. & Connor, Peter M. & Harris, Richard, 2023. "Distributed local energy: Assessing the determinants of domestic-scale solar photovoltaic uptake at the local level across England and Wales," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    13. Bashiri, Ali & Alizadeh, Sasan H., 2018. "The analysis of demographics, environmental and knowledge factors affecting prospective residential PV system adoption: A study in Tehran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3131-3139.
    14. Bruno Moreno Rodrigo de Freitas, 2020. "Quantifying the effect of regulated volumetric electriciy tariffs on residential PV adoption under net metering scheme," Working papers of CATT hal-02976874, HAL.
    15. Balta-Ozkan, Nazmiye & Yildirim, Julide & Connor, Peter M. & Truckell, Ian & Hart, Phil, 2021. "Energy transition at local level: Analyzing the role of peer effects and socio-economic factors on UK solar photovoltaic deployment," Energy Policy, Elsevier, vol. 148(PB).
    16. Stefan Lamp, 2023. "Sunspots That Matter: The Effect of Weather on Solar Technology Adoption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 1179-1219, April.
    17. Rode, Johannes & Müller, Sven, 2016. "Spatio-Temporal Variation in Peer Effects - The Case of Rooftop Photovoltaic Systems in Germany," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84765, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    18. Lan, Haifeng & Gou, Zhonghua & Lu, Yi, 2021. "Machine learning approach to understand regional disparity of residential solar adoption in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    19. Moon-Hyun Kim & Tae-Hyoung Tommy Gim, 2021. "Spatial Characteristics of the Diffusion of Residential Solar Photovoltaics in Urban Areas: A Case of Seoul, South Korea," IJERPH, MDPI, vol. 18(2), pages 1-16, January.
    20. Hackbarth, André, 2018. "Attitudes, preferences, and intentions of German households concerning participation in peer-to-peer electricity trading," Reutlingen Working Papers on Marketing & Management 2019-2, Reutlingen University, ESB Business School.

    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:eee:enepol:v:149:y:2021:i:c:s0301421520308120. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.