IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v23y2021i10d10.1007_s10668-021-01307-1.html
   My bibliography  Save this article

Economic growth of green agriculture and its influencing factors in china: Based on emergy theory and spatial econometric model

Author

Listed:
  • Zhaoliang Li

    (Wuhan Institute of Technology)

  • Minghao Jin

    (Wuhan Institute of Technology)

  • Jianwei Cheng

    (Wuhan Institute of Technology)

Abstract

Based on emergy theory, this paper calculates the agricultural green GDP of China and its provincial units from 2003 to 2018. Following this, the spatial distribution characteristics and influencing factors of the economic growth of green agriculture in China are studied using the spatial analysis method. The results show that: (1) Compared with the traditional GDP derived from agriculture, the per capita green agriculture GDP growth in China is relatively slow, and the proportion of green agriculture GDP to the traditional GDP from agriculture is between 85 and 91%, which shows a downward trend. (2) The economic growth of China's green agriculture manifests significant spatial agglomeration, with gradually growing effects. (3) The per capita GDP of green agriculture has not broken the overall pattern of high economic growth in China’s eastern regions and low growth in the western. According to the results, the major cause of this spatial pattern lay in the higher efficiency of agricultural production in the eastern, economically more developed areas, and the relatively less efficient mode of production in the western areas. (4) A region’s level of economic development, technological innovation, infrastructure investment and openness were found to have a positive impact on the growth of green agriculture within it. In previous research, emergy methods have not been applied to study the economic sectors in China, the largest developing country in the world, especially in the agricultural sector. Only few studies report on the analysis of spatial characteristics and factors influencing the growth of green agriculture. The agricultural green GDP was calculated based on the emergy analysis method. The temporal and spatial characteristics, as well as the driving factors of green agriculture GDP, were analyzed under the full consideration of the spatial correlation, which contributes to the theoretical explanation of spatial agglomeration of economic factors in spatial economic theory. The conclusions provide a theoretical and practical basis for accurate evaluation of the green growth of China's agricultural economy, optimization of the spatial structure of the country’s green agricultural economy and coordination of the development of green agriculture in different regions.

Suggested Citation

  • Zhaoliang Li & Minghao Jin & Jianwei Cheng, 2021. "Economic growth of green agriculture and its influencing factors in china: Based on emergy theory and spatial econometric model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 15494-15512, October.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:10:d:10.1007_s10668-021-01307-1
    DOI: 10.1007/s10668-021-01307-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01307-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-021-01307-1?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. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    2. Li, Jing & Zhang, Zelie & Jin, Xianfeng & Chen, Jiaquan & Zhang, Shaojia & He, Zong & Li, Sheng & He, Zhiming & Zhang, Haipeng & Xiao, He, 2018. "Exploring the socioeconomic and ecological consequences of cash crop cultivation for policy implications," Land Use Policy, Elsevier, vol. 76(C), pages 46-57.
    3. Joshua Graff Zivin & Matthew Neidell, 2012. "The Impact of Pollution on Worker Productivity," American Economic Review, American Economic Association, vol. 102(7), pages 3652-3673, December.
    4. James LeSage & Yuxue Sheng, 2014. "A spatial econometric panel data examination of endogenous versus exogenous interaction in Chinese province-level patenting," Journal of Geographical Systems, Springer, vol. 16(3), pages 233-262, July.
    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. Zhao Liu & Lan-Ye Wei, 2022. "Effects of ODI and export trade structure on CO2 emissions in China: nonlinear relationships," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 13630-13656, December.
    2. Yujie Zhang & Qingsong Wang & Shu Tian & Yue Xu & Xueliang Yuan & Qiao Ma & Haichao Ma & Shuo Yang & Yuan Xu & Chengqing Liu, 2024. "Research on the path of industrial sector's carbon peak based on the perspective of provincial differentiation: a case study from China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 23245-23282, September.
    3. Zhang, Pei & Hao, Dongyang, 2023. "Enterprise financial management and fossil fuel energy efficiency for green economic growth," Resources Policy, Elsevier, vol. 84(C).
    4. Yuehua Xia & Honggen Long & Zhi Li & Jiasen Wang, 2022. "Farmers’ Credit Risk Assessment Based on Sustainable Supply Chain Finance for Green Agriculture," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    5. Mingjia Chi & Qinyang Guo & Lincheng Mi & Guofeng Wang & Weiming Song, 2022. "Spatial Distribution of Agricultural Eco-Efficiency and Agriculture High-Quality Development in China," Land, MDPI, vol. 11(5), pages 1-15, May.
    6. Yuanying Chi & Yangmei Xu & Xu Wang & Feng Jin & Jialin Li, 2021. "A Win–Win Scenario for Agricultural Green Development and Farmers’ Agricultural Income: An Empirical Analysis Based on the EKC Hypothesis," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    7. Lipeng Li & Apurbo Sarkar & Xi Zhou & Xiuling Ding & Hua Li, 2022. "Influence and Action Mechanisms of Governmental Relations Embeddedness for Fostering Green Production Demonstration Household: Evidence from Shaanxi, Sichuan, and Anhui Province, China," IJERPH, MDPI, vol. 19(19), pages 1-25, September.
    8. Ruifeng Hu & Weiqiao Xu, 2022. "Exploring the Technological Changes of Green Agriculture in China: Evidence from Patent Data (1998–2021)," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    9. Feng Zhou & Chunhui Wen, 2023. "Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development," Agriculture, MDPI, vol. 13(7), pages 1-47, July.
    10. Jingpeng Chen & Desheng Zhang & Zhi Chen & Zhijian Li & Zigong Cai, 2022. "Effect of Agricultural Social Services on Green Production of Natural Rubber: Evidence from Hainan, China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    11. Hua Guo & Fan Gu & Yanling Peng & Xin Deng & Lili Guo, 2022. "Does Digital Inclusive Finance Effectively Promote Agricultural Green Development?—A Case Study of China," IJERPH, MDPI, vol. 19(12), pages 1-17, June.

    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. Dongwoo Kang & Sandy Dall’erba, 2016. "Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach," Journal of Geographical Systems, Springer, vol. 18(2), pages 125-157, April.
    2. Yuxue Sheng & James LeSage, 2021. "A spatial regression methodology for exploring the role of regional connectivity in knowledge production: Evidence from Chinese regions," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 847-874, August.
    3. Zhao, Mingxuan & Lv, Lianhong & Wu, Jing & Wang, Shen & Zhang, Nan & Bai, Zihan & Luo, Hong, 2022. "Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model," Energy, Elsevier, vol. 251(C).
    4. Hausman, Catherine & Stolper, Samuel, 2021. "Inequality, information failures, and air pollution," Journal of Environmental Economics and Management, Elsevier, vol. 110(C).
    5. Shuai Shi & Kathy Pain, 2020. "Investigating China’s Mid-Yangtze River economic growth region using a spatial network growth model," Urban Studies, Urban Studies Journal Limited, vol. 57(14), pages 2973-2993, November.
    6. Carozzi, Felipe & Roth, Sefi, 2023. "Dirty density: Air quality and the density of American cities," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    7. Ziebarth, N. R. & Schmitt, M. & Karlsson, M., 2013. "The short-term population health effects of weather and pollution: implications of climate change," Health, Econometrics and Data Group (HEDG) Working Papers 13/34, HEDG, c/o Department of Economics, University of York.
    8. Zhuang Miao & Tomas Baležentis & Zhihua Tian & Shuai Shao & Yong Geng & Rui Wu, 2019. "Environmental Performance and Regulation Effect of China’s Atmospheric Pollutant Emissions: Evidence from “Three Regions and Ten Urban Agglomerations”," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(1), pages 211-242, September.
    9. Sumei Chen & Ling‐Yun He, 2019. "Taxation and the Environment–Health–Poverty Trap: A Policy Experiment Perspective," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 27(1), pages 72-92, January.
    10. Cécile Couharde & Rémi Generoso, 2024. "Assessing the Impact of National Air Quality Standards on Agricultural Land Values: Insights from Corn and Soybean Regions," Working Papers hal-04503777, HAL.
    11. Agarwal, Sumit & Satyanarain, Rengarajan & Sing, Tien Foo & Vollmer, Derek, 2016. "Effects of construction activities on residential electricity consumption: Evidence from Singapore's public housing estates," Energy Economics, Elsevier, vol. 55(C), pages 101-111.
    12. Ross Levine & Chen Lin & Zigan Wang, 2018. "Pollution and Human Capital Migration: Evidence from Corporate Executives," NBER Working Papers 24389, National Bureau of Economic Research, Inc.
    13. Lin, Jiada & Wan, Haiyuan & Yu, Yangcheng, 2024. "What you breathe makes you poor: The effect of air pollution on income," China Economic Review, Elsevier, vol. 83(C).
    14. Rosina Moreno & Ernest Miguélez, 2012. "A Relational Approach To The Geography Of Innovation: A Typology Of Regions," Journal of Economic Surveys, Wiley Blackwell, vol. 26(3), pages 492-516, July.
    15. Ernest Miguélez & Rosina Moreno, 2013. "Do Labour Mobility and Technological Collaborations Foster Geographical Knowledge Diffusion? The Case of European Regions," Growth and Change, Wiley Blackwell, vol. 44(2), pages 321-354, June.
    16. Åsa Johansson, 2016. "Public Finance, Economic Growth and Inequality: A Survey of the Evidence," OECD Economics Department Working Papers 1346, OECD Publishing.
    17. Colmer, Jonathan & Lin, Dajun & Liu, Siying & Shimshack, Jay, 2021. "Why are pollution damages lower in developed countries? Insights from high-Income, high-particulate matter Hong Kong," Journal of Health Economics, Elsevier, vol. 79(C).
    18. Timothy J. Halliday & Rachel Inafuku & Lester Lusher & Aureo de Paula, 2022. "VOG: Using Volcanic Eruptions to Estimate the Impact of Air Pollution on Student Learning Outcomes," Working Papers 202203, University of Hawaii at Manoa, Department of Economics.
    19. Klarl, Torben Alexander, 2015. "Urban-rural migration and congestion costs revisited: is there a triple dividend for cities in developing countries?," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112829, Verein für Socialpolitik / German Economic Association.
    20. Isaksen, Elisabeth & Johansen, Bjørn G., 2021. "Congestion pricing, air pollution, and individual-level behavioural responses," LSE Research Online Documents on Economics 111493, London School of Economics and Political Science, LSE Library.

    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:spr:endesu:v:23:y:2021:i:10:d:10.1007_s10668-021-01307-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.